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Pott J, Kheirkhah A, Gadin JR, Kleber ME, Delgado GE, Kirsten H, Forer L, Hauck SM, Burkhardt R, Scharnagl H, Loeffler M, März W, Thiery J, Gieger C, Peters A, Silveira A, Hooft FV, Kronenberg F, Scholz M. Sex and statin-related genetic associations at the PCSK9 gene locus: results of genome-wide association meta-analysis. Biol Sex Differ 2024; 15:26. [PMID: 38532495 DOI: 10.1186/s13293-024-00602-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics. METHODS We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups. RESULTS We identified 11 loci associated with PCSK9 in at least one stratified subgroup (p < 1.0 × 10-6), including the PCSK9 gene locus and five other lipid loci: APOB, TM6SF2, FADS1/FADS2, JMJD1C, and HP/HPR. The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2) and one for males at 12q24.22 (near KSR2/NOS1), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals. CONCLUSIONS We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Azin Kheirkhah
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Jesper R Gadin
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Solna, Sweden
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Graciela E Delgado
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Lukas Forer
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Hubert Scharnagl
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- SYNLAB Academy, Synlab Holding Deutschland GmbH, Mannheim and Augsburg, Germany
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Faculty of Medicine, University of Kiel, Kiel, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Angela Silveira
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Solna, Sweden
| | - Ferdinand Van't Hooft
- Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital Solna, Solna, Sweden
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
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2
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Beuchel C, Dittrich J, Becker S, Kirsten H, Tönjes A, Kovacs P, Stumvoll M, Loeffler M, Teren A, Thiery J, Isermann B, Ceglarek U, Scholz M. An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index. J Mol Med (Berl) 2023; 101:1305-1321. [PMID: 37672078 PMCID: PMC10560167 DOI: 10.1007/s00109-023-02362-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 09/07/2023]
Abstract
Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.
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Affiliation(s)
- Carl Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
| | - Julia Dittrich
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
| | - Susen Becker
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- Department of Forensic Toxicology, Institute of Legal Medicine, University Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Anke Tönjes
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
- Deutsches Zentrum für Diabetesforschung, Neuherberg, Germany
| | - Michael Stumvoll
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University Hospital Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | | | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Berend Isermann
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, Leipzig University, Leipzig, Germany
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
- LIFE - Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany.
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3
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Khan AH, Bagley JR, LaPierre N, Gonzalez-Figueroa C, Spencer TC, Choudhury M, Xiao X, Eskin E, Jentsch JD, Smith DJ. Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice. Cell Rep 2023; 42:112856. [PMID: 37481717 PMCID: PMC10530068 DOI: 10.1016/j.celrep.2023.112856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 06/06/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.
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Affiliation(s)
- Arshad H Khan
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Jared R Bagley
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Nathan LaPierre
- Department of Computer Science, UCLA, Los Angeles, CA 90095, USA
| | | | - Tadeo C Spencer
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Mudra Choudhury
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Xinshu Xiao
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA 90095, USA
| | - Eleazar Eskin
- Department of Computational Medicine, UCLA, Los Angeles, CA 90095, USA
| | - James D Jentsch
- Department of Psychology, Binghamton University, Binghamton, NY, USA
| | - Desmond J Smith
- Department of Molecular and Medical Pharmacology, Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
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4
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Iakovliev A, McGurnaghan SJ, Hayward C, Colombo M, Lipschutz D, Spiliopoulou A, Colhoun HM, McKeigue PM. Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics. Am J Hum Genet 2023; 110:913-926. [PMID: 37164005 PMCID: PMC10257008 DOI: 10.1016/j.ajhg.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/12/2023] [Indexed: 05/12/2023] Open
Abstract
The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D.
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Affiliation(s)
- Andrii Iakovliev
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland
| | - Stuart J McGurnaghan
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Caroline Hayward
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Marco Colombo
- University of Leipzig, Medical Faculty, University Hospital for Children and Adolescents, Center for Pediatric Research, Leipzig, Germany
| | - Debby Lipschutz
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Athina Spiliopoulou
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland
| | - Helen M Colhoun
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Paul M McKeigue
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland.
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5
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Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases. Sci Rep 2022; 12:20167. [PMID: 36424512 PMCID: PMC9686236 DOI: 10.1038/s41598-022-24611-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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6
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Dressman D, Buttrick T, Cimpean M, Bennett D, Menon V, Bradshaw EM, Vardarajan B, Elyaman W. Genotype-phenotype correlation of T-cell subtypes reveals senescent and cytotoxic genes in Alzheimer's disease. Hum Mol Genet 2022; 31:3355-3366. [PMID: 35640154 PMCID: PMC9523563 DOI: 10.1093/hmg/ddac126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 11/12/2022] Open
Abstract
Recent studies identifying expression quantitative trait loci (eQTLs) in immune cells have uncovered important links between disease risk alleles and gene expression trends in monocytes, T cells and other cell types. However, these studies are generally done with young, healthy subjects, limiting the utility of their findings for age-related conditions such as Alzheimer's disease (AD). We have performed RNA sequencing on four T-cell subsets in genome-wide genotyped and well-characterized AD subjects and age- and sex-matched controls from the Religious Orders Study/Memory and Aging Project. We correlated gene expression data with AD neuropathological traits and with single-nucleotide polymorphisms to detect eQTLs. We identified several significant genes involved in T-cell senescence and cytotoxicity, consistent with T-cell RNA sequencing studies in aged/AD cohorts. We identified unexpected eQTLs previously associated with neuropsychiatric disease traits. Finally, we discovered that pathways related to axon guidance and synaptic function were enriched among trans-eQTLs in coding regions of the genome. Our data strengthen the potential link between T-cell senescence and age-related neurodegenerative disease. In addition, our eQTL data suggest that T-cell phenotypes may influence neuropsychiatric disorders and can be influenced by genes involved in neurodevelopmental processes.
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Affiliation(s)
- Dallin Dressman
- Department of Pharmacology, Columbia University, New York, NY 10032, USA
- Department of Neurology, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY 10032, USA
| | | | | | - David Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Vilas Menon
- Department of Neurology, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY 10032, USA
| | - Elizabeth M Bradshaw
- Department of Neurology, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY 10032, USA
| | - Badri Vardarajan
- Department of Neurology, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY 10032, USA
| | - Wassim Elyaman
- Department of Neurology, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY 10032, USA
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7
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Klemp I, Hoffmann A, Müller L, Hagemann T, Horn K, Rohde-Zimmermann K, Tönjes A, Thiery J, Löffler M, Burkhardt R, Böttcher Y, Stumvoll M, Blüher M, Krohn K, Scholz M, Baber R, Franks PW, Kovacs P, Keller M. DNA methylation patterns reflect individual's lifestyle independent of obesity. Clin Transl Med 2022; 12:e851. [PMID: 35692099 PMCID: PMC9189420 DOI: 10.1002/ctm2.851] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/09/2022] [Accepted: 04/15/2022] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE Obesity is driven by modifiable lifestyle factors whose effects may be mediated by epigenetics. Therefore, we investigated lifestyle effects on blood DNA methylation in participants of the LIFE-Adult study, a well-characterised population-based cohort from Germany. RESEARCH DESIGN AND METHODS Lifestyle scores (LS) based on diet, physical activity, smoking and alcohol intake were calculated in 4107 participants of the LIFE-Adult study. Fifty subjects with an extremely healthy lifestyle and 50 with an extremely unhealthy lifestyle (5th and 95th percentiles LS) were selected for genome-wide DNA methylation analysis in blood samples employing Illumina Infinium® Methylation EPIC BeadChip system technology. RESULTS Differences in DNA methylation patterns between body mass index groups (<25 vs. >30 kg/m2 ) were rather marginal compared to inter-lifestyle differences (0 vs. 145 differentially methylated positions [DMPs]), which identified 4682 differentially methylated regions (DMRs; false discovery rate [FDR <5%) annotated to 4426 unique genes. A DMR annotated to the glutamine-fructose-6-phosphate transaminase 2 (GFPT2) locus showed the strongest hypomethylation (∼6.9%), and one annotated to glutamate rich 1 (ERICH1) showed the strongest hypermethylation (∼5.4%) in healthy compared to unhealthy lifestyle individuals. Intersection analysis showed that diet, physical activity, smoking and alcohol intake equally contributed to the observed differences, which affected, among others, pathways related to glutamatergic synapses (adj. p < .01) and axon guidance (adj. p < .05). We showed that methylation age correlates with chronological age and waist-to-hip ratio with lower DNA methylation age (DNAmAge) acceleration distances in participants with healthy lifestyles. Finally, two identified top DMPs for the alanyl aminopeptidase (ANPEP) locus also showed the strongest expression quantitative trait methylation in blood. CONCLUSIONS DNA methylation patterns help discriminate individuals with a healthy versus unhealthy lifestyle, which may mask subtle methylation differences derived from obesity.
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Affiliation(s)
- Ireen Klemp
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Anne Hoffmann
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Luise Müller
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Tobias Hagemann
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Kathrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Kerstin Rohde-Zimmermann
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | | | - Markus Löffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Ralph Burkhardt
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Yvonne Böttcher
- Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Medical Division, Akershus University Hospital, Lørenskog, Norway
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany.,Deutsches Zentrum für Diabetesforschung, Neuherberg, Germany
| | - Matthias Blüher
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Knut Krohn
- Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ronny Baber
- Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Maria Keller
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.,Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), Helmholtz Zentrum München, University of Leipzig and University Hospital Leipzig, Leipzig, Germany
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8
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Liu C, Joehanes R, Ma J, Wang Y, Sun X, Keshawarz A, Sooda M, Huan T, Hwang SJ, Bui H, Tejada B, Munson PJ, Cumhur D, Heard-Costa NL, Pitsillides AN, Peloso GM, Feolo M, Sharopova N, Vasan RS, Levy D. Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases. RESEARCH SQUARE 2022:rs.3.rs-1598646. [PMID: 35664994 PMCID: PMC9164515 DOI: 10.21203/rs.3.rs-1598646/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p < 5x10 - 8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR < 0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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9
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Pott J, Garcia T, Hauck SM, Petrera A, Wirkner K, Loeffler M, Kirsten H, Peters A, Scholz M. Genetically regulated gene expression and proteins revealed discordant effects. PLoS One 2022; 17:e0268815. [PMID: 35604899 PMCID: PMC9126407 DOI: 10.1371/journal.pone.0268815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
Background Although gene-expression (GE) and protein levels are typically strongly genetically regulated, their correlation is known to be low. Here we investigate this phenomenon by focusing on the genetic background of this correlation in order to understand the similarities and differences in the genetic regulation of these omics layers. Methods and results We performed locus-wide association studies of 92 protein levels measured in whole blood for 2,014 samples of European ancestry and found that 66 are genetically regulated. Three female- and one male-specific effects were detected. We estimated the genetically regulated GE for all significant genes in 49 GTEx v8 tissues. A total of 7 proteins showed negative correlations with their respective GE across multiple tissues. Finally, we tested for causal links of GE on protein expression via Mendelian Randomization, and confirmed a negative causal effect of GE on protein level for five of these genes in a total of 63 gene-tissue pairs: BLMH, CASP3, CXCL16, IL6R, and SFTPD. For IL6R, we replicated the negative causal effect on coronary-artery disease (CAD), while its GE was positively linked to CAD. Conclusion While total GE and protein levels are only weakly correlated, we found high correlations between their genetically regulated components across multiple tissues. Of note, strong negative causal effects of tissue-specific GE on five protein levels were detected. Causal network analyses revealed that GE effects on CAD risks was in general mediated by protein levels.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
- * E-mail: (JP); (MS)
| | - Tarcyane Garcia
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Stefanie M. Hauck
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Agnese Petrera
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Kerstin Wirkner
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Annette Peters
- Research Unit Protein Science and Metabolomics and Proteomics Core Facility, Helmholtz Zentrum Munich - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
- * E-mail: (JP); (MS)
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10
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Liu C, Joehanes R, Ma J, Wang Y, Sun X, Keshawarz A, Sooda M, Huan T, Hwang SJ, Bui H, Tejada B, Munson PJ, Cumhur D, Heard-Costa NL, Pitsillides AN, Peloso GM, Feolo M, Sharopova N, Vasan RS, Levy D. Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.04.13.22273841. [PMID: 35547845 PMCID: PMC9094109 DOI: 10.1101/2022.04.13.22273841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p <5×10 -8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p <1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR <0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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Affiliation(s)
- Chunyu Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiantao Ma
- Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Yuxuan Wang
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Xianbang Sun
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Amena Keshawarz
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meera Sooda
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tianxiao Huan
- University of Massachusetts Medical School, Worcester, MA, USA
| | - Shih-Jen Hwang
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Helena Bui
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brandon Tejada
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Munson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | | | - Gina M Peloso
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Michael Feolo
- University of Massachusetts Medical School, Worcester, MA, USA
| | | | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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11
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Kühnapfel A, Ahnert P, Horn K, Kirsten H, Loeffler M, Scholz M. First genome-wide association study of 99 body measures derived from 3-dimensional body scans. Genes Dis 2022; 9:777-788. [PMID: 35782980 PMCID: PMC9243350 DOI: 10.1016/j.gendis.2021.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 01/26/2021] [Accepted: 02/05/2021] [Indexed: 11/23/2022] Open
Abstract
Body height, body mass index, hip and waist circumference are important risk factors or outcome variables in clinical and epidemiological research with complex underlying genetics. However, these classical anthropometric traits represent only a very limited view on the human body and other traits with potentially higher functional specificity are not yet studied to a larger extent. Participants of LIFE-Adult were assessed by three-dimensional body scanner VITUS XXL determining 99 high-quality anthropometric traits in parallel. Genotyping was performed by Axiom Genome-Wide CEU 1 Array Plate microarray technology and imputation was done using 1000 Genomes phase 3 reference panel. Combined phenotype and genetic information are available for a total of 7,562 participants. Largest heritabilities were estimated for height traits (maximum heritability with h2 = 44% for neck height) and 61 traits achieved values larger than 20%. By genome-wide analyses, we identified 16 loci associated with at least one of the 99 traits. Ten of these loci were not described for association with classical anthropometric traits so far. The strongest novel association was observed for 7p14.3 (rs11979006, P = 2.12 × 10−9) for the trait Back Width with ZNRF2 as the most plausible candidate gene. Loci established for association with classical anthropometric traits were subjected to anthropometric phenome-wide association analysis. From the reported 709 loci, 211 are co-associated with body scanner traits (enrichment: OR = 1.96, P = 1.08 × 10−61). We conclude that genetics of 3D laser-based anthropometry is promising to identify novel loci and to improve the functional understanding of established ones.
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12
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Transcriptome Analyses of Adipose Tissue Samples Identify EGFL6 as a Candidate Gene Involved in Obesity-Related Adipose Tissue Dysfunction in Children. Int J Mol Sci 2022; 23:ijms23084349. [PMID: 35457174 PMCID: PMC9033114 DOI: 10.3390/ijms23084349] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 02/04/2023] Open
Abstract
Obesity develops early in childhood and is accompanied by early signs of adipose tissue (AT) dysfunction and metabolic disease in children. In order to analyse the molecular processes during obesity-related AT accumulation in children, we investigated genome-wide expression profiles in AT samples, isolated adipocytes, and stromal vascular fraction (SVF) cells and assessed their relation to obesity as well as biological and functional AT parameters. We detected alterations in gene expression associated with obesity and related parameters, i.e., BMI SDS, adipocyte size, macrophage infiltration, adiponectin, and/or leptin. While differential gene expression in AT and adipocytes shared an enrichment in metabolic pathways and pathways related to extracellular structural organisation, SVF cells showed an overrepresentation in inflammatory pathways. In adipocytes, we found the strongest positive association for epidermal growth factor-like protein 6 (EGFL6) with adipocyte hypertrophy. EGFL6 was also upregulated during in vitro adipocyte differentiation. In children, EGFL6 expression was positively correlated to parameters of AT dysfunction and metabolic disease such as macrophage infiltration into AT, hs-CRP, leptin levels, and HOMA-IR. In conclusion, we provide evidence for early alterations in AT gene expression related to AT dysfunction in children and identified EGFL6 as potentially being involved in processes underlying the pathogenesis of metabolic disease.
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13
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Beuchel C, Dittrich J, Pott J, Henger S, Beutner F, Isermann B, Loeffler M, Thiery J, Ceglarek U, Scholz M. Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure. Metabolites 2022; 12:metabo12030216. [PMID: 35323659 PMCID: PMC8949022 DOI: 10.3390/metabo12030216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 02/04/2023] Open
Abstract
A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and 97 whole blood amino acids, acylcarnitines and derived ratios in the LIFE-Adult (n = 9646) and LIFE-Heart (n = 5860) studies, respectively. In addition to hundreds of significant associations, a total of 62 associations of six phenotypes were found in both studies. Positive associations of various amino acids and a range of acylcarnitines with decreasing cardiovascular health indicate disruptions in mitochondrial, as well as peroxisomal fatty acid oxidation. We complemented our metabolite association analyses with whole blood and peripheral blood mononuclear cell (PBMC) gene-expression analyses of fatty acid oxidation and ketone-body metabolism related genes. This revealed several differential expressions for the heart failure biomarker N-terminal prohormone of brain natriuretic peptide (NT-proBNP) in peripheral blood mononuclear cell (PBMC) gene expression. Finally, we constructed and compared three prediction models of significant stenosis in the LIFE-Heart study using (1) traditional risk factors only, (2) the metabolite panel only and (3) a combined model. Area under the receiver operating characteristic curve (AUC) comparison of these three models shows an improved prediction accuracy for the combined metabolite and classical risk factor model (AUC = 0.78, 95%-CI: 0.76–0.80). In conclusion, we improved our understanding of metabolic implications of ASCVD phenotypes by observing associations with metabolite concentrations and gene expression of the mitochondrial and peroxisomal fatty acid oxidation. Additionally, we demonstrated the predictive potential of the metabolite profile to improve classification of patients with significant stenosis.
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Affiliation(s)
- Carl Beuchel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- Correspondence: (C.B.); (U.C.); (M.S.)
| | - Julia Dittrich
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
| | - Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
| | - Sylvia Henger
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
| | | | - Berend Isermann
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
- Faculty of Medicine, Christian-Albrecht University of Kiel, 24118 Kiel, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.D.); (B.I.); (J.T.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
- Correspondence: (C.B.); (U.C.); (M.S.)
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany; (J.P.); (S.H.); (M.L.)
- LIFE—Leipzig Research Center for Civilization Diseases, Leipzig University, 04103 Leipzig, Germany
- IFB AdiposityDiseases, University Hospital Leipzig, 04103 Leipzig, Germany
- Correspondence: (C.B.); (U.C.); (M.S.)
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14
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Barnes AB, Keener RM, Schott BH, Wang L, Valdivia RH, Ko DC. Human genetic diversity regulating the TLR10/TLR1/TLR6 locus confers increased cytokines in response to Chlamydia trachomatis. HGG ADVANCES 2022; 3:100071. [PMID: 35047856 PMCID: PMC8756536 DOI: 10.1016/j.xhgg.2021.100071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/15/2021] [Indexed: 12/25/2022] Open
Abstract
Human genetic diversity can have profound effects on health outcomes upon exposure to infectious agents. For infections with Chlamydia trachomatis (C. trachomatis), the wide range of genital and ocular disease manifestations are likely influenced by human genetic differences that regulate interactions between C. trachomatis and host cells. We leveraged this diversity in cellular responses to demonstrate the importance of variation at the Toll-like receptor 1 (TLR1), TLR6, and TLR10 locus to cytokine production in response to C. trachomatis. We determined that a single-nucleotide polymorphism (SNP) (rs1057807), located in a region that forms a loop with the TLR6 promoter, is associated with increased expression of TLR1, TLR6, and TLR10 and secreted levels of ten C. trachomatis-induced cytokines. Production of these C. trachomatis-induced cytokines is primarily dependent on MyD88 and TLR6 based on experiments using inhibitors, blocking antibodies, RNAi, and protein overexpression. Population genetic analyses further demonstrated that the mean IL-6 response of cells from two European populations were higher than the mean response of cells from three African populations and that this difference was partially attributable to variation in rs1057807 allele frequency. In contrast, a SNP associated with a different pro-inflammatory cytokine (rs2869462 associated with the chemokine CXCL10) exhibited an opposite response, underscoring the complexity of how different genetic variants contribute to an individual's immune response. This multidisciplinary study has identified a long-range chromatin interaction and genetic variation that regulates TLR6 to broaden our understanding of how human genetic variation affects the C. trachomatis-induced immune response.
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Affiliation(s)
- Alyson B. Barnes
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Rachel M. Keener
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Benjamin H. Schott
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Raphael H. Valdivia
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Dennis C. Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27710, USA
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Duke University, Durham, NC 27710, USA
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15
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Genome-wide meta-analysis of phytosterols reveals five novel loci and a detrimental effect on coronary atherosclerosis. Nat Commun 2022; 13:143. [PMID: 35013273 PMCID: PMC8748632 DOI: 10.1038/s41467-021-27706-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 11/22/2021] [Indexed: 12/29/2022] Open
Abstract
Phytosterol serum concentrations are under tight genetic control. The relationship between phytosterols and coronary artery disease (CAD) is controversially discussed. We perform a genome-wide meta-analysis of 32 phytosterol traits reflecting resorption, cholesterol synthesis and esterification in six studies with up to 9758 subjects and detect ten independent genome-wide significant SNPs at seven genomic loci. We confirm previously established associations at ABCG5/8 and ABO and demonstrate an extended locus heterogeneity at ABCG5/8 with different functional mechanisms. New loci comprise HMGCR, NPC1L1, PNLIPRP2, SCARB1 and APOE. Based on these results, we perform Mendelian Randomization analyses (MR) revealing a risk-increasing causal relationship of sitosterol serum concentrations and CAD, which is partly mediated by cholesterol. Here we report that phytosterols are polygenic traits. MR add evidence of both, direct and indirect causal effects of sitosterol on CAD.
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16
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Kühnapfel A, Horn K, Klotz U, Kiehntopf M, Rosolowski M, Loeffler M, Ahnert P, Suttorp N, Witzenrath M, Scholz M. Genetic Regulation of Cytokine Response in Patients with Acute Community-Acquired Pneumonia. Genes (Basel) 2022; 13:genes13010111. [PMID: 35052452 PMCID: PMC8774373 DOI: 10.3390/genes13010111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Community-acquired pneumonia (CAP) is an acute disease condition with a high risk of rapid deteriorations. We analysed the influence of genetics on cytokine regulation to obtain a better understanding of patient’s heterogeneity. Methods: For up to N = 389 genotyped participants of the PROGRESS study of hospitalised CAP patients, we performed a genome-wide association study of ten cytokines IL-1β, IL-6, IL-8, IL-10, IL-12, MCP-1 (MCAF), MIP-1α (CCL3), VEGF, VCAM-1, and ICAM-1. Consecutive secondary analyses were performed to identify independent hits and corresponding causal variants. Results: 102 SNPs from 14 loci showed genome-wide significant associations with five of the cytokines. The most interesting associations were found at 6p21.1 for VEGF (p = 1.58 × 10−20), at 17q21.32 (p = 1.51 × 10−9) and at 10p12.1 (p = 2.76 × 10−9) for IL-1β, at 10p13 for MIP-1α (CCL3) (p = 2.28 × 10−9), and at 9q34.12 for IL-10 (p = 4.52 × 10−8). Functionally plausible genes could be assigned to the majority of loci including genes involved in cytokine secretion, granulocyte function, and cilial kinetics. Conclusion: This is the first context-specific genetic association study of blood cytokine concentrations in CAP patients revealing numerous biologically plausible candidate genes. Two of the loci were also associated with atherosclerosis with probable common or consecutive pathomechanisms.
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Affiliation(s)
- Andreas Kühnapfel
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
- Correspondence:
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
| | - Ulrike Klotz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
| | - Michael Kiehntopf
- Institute for Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, 07740 Jena, Germany;
| | - Maciej Rosolowski
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
| | - Peter Ahnert
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
| | - Norbert Suttorp
- Division of Infectiology and Pneumonology, Medical Department, Charité—Berlin University Medicine, 13353 Berlin, Germany; (N.S.); (M.W.)
| | - Martin Witzenrath
- Division of Infectiology and Pneumonology, Medical Department, Charité—Berlin University Medicine, 13353 Berlin, Germany; (N.S.); (M.W.)
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; (K.H.); (U.K.); (M.R.); (M.L.); (P.A.); (M.S.)
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17
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Mortlock S, McKinnon B, Montgomery GW. Genetic Regulation of Transcription in the Endometrium in Health and Disease. FRONTIERS IN REPRODUCTIVE HEALTH 2022; 3:795464. [PMID: 36304015 PMCID: PMC9580733 DOI: 10.3389/frph.2021.795464] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2023] Open
Abstract
The endometrium is a complex and dynamic tissue essential for fertility and implicated in many reproductive disorders. The tissue consists of glandular epithelium and vascularised stroma and is unique because it is constantly shed and regrown with each menstrual cycle, generating up to 10 mm of new mucosa. Consequently, there are marked changes in cell composition and gene expression across the menstrual cycle. Recent evidence shows expression of many genes is influenced by genetic variation between individuals. We and others have reported evidence for genetic effects on hundreds of genes in endometrium. The genetic factors influencing endometrial gene expression are highly correlated with the genetic effects on expression in other reproductive (e.g., in uterus and ovary) and digestive tissues (e.g., salivary gland and stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. There is also increasing evidence for cell specific genetic effects for some genes. Sample size for studies in endometrium are modest and results from the larger studies of gene expression in blood report genetic effects for a much higher proportion of genes than currently reported for endometrium. There is also emerging evidence for the importance of genetic variation on RNA splicing. Gene mapping studies for common disease, including diseases associated with endometrium, show most variation maps to intergenic regulatory regions. It is likely that genetic risk factors for disease function through modifying the program of cell specific gene expression. The emerging evidence from our gene mapping studies coupled with tissue specific studies, and the GTEx, eQTLGen and EpiMap projects, show we need to expand our understanding of the complex regulation of gene expression. These data also help to link disease genetic risk factors to specific target genes. Combining our data on genetic regulation of gene expression in endometrium, and cell types within the endometrium with gene mapping data for endometriosis and related diseases is beginning to uncover the specific genes and pathways responsible for increased risk of these diseases.
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Affiliation(s)
| | | | - Grant W. Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
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Bankier S, Michoel T. eQTLs as causal instruments for the reconstruction of hormone linked gene networks. Front Endocrinol (Lausanne) 2022; 13:949061. [PMID: 36060942 PMCID: PMC9428692 DOI: 10.3389/fendo.2022.949061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.
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19
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Lee T, Lee H. Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis. Biomedicines 2021; 9:biomedicines9111525. [PMID: 34829754 PMCID: PMC8614900 DOI: 10.3390/biomedicines9111525] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- Correspondence: ; Tel.: +82-62-715-2213
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20
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Pott J, Gadin J, Theusch E, Kleber ME, Delgado GE, Kirsten H, Hauck SM, Burkhardt R, Scharnagl H, Krauss RM, Loeffler M, März W, Thiery J, Silveira A, Vant Hooft FM, Scholz M. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Hum Mol Genet 2021; 31:999-1011. [PMID: 34590679 PMCID: PMC8947322 DOI: 10.1093/hmg/ddab279] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/15/2022] Open
Abstract
Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player in lipid metabolism, as it degrades low-density lipoprotein (LDL) receptors from hepatic cell membranes. So far, only variants of the PCSK9 gene locus were found to be associated with PCSK9 levels. Here we aimed to identify novel genetic loci that regulate PCSK9 levels and how they relate to other lipid traits. Additionally, we investigated to what extend the causal effect of PCSK9 on coronary artery disease (CAD) is mediated by low-density lipoprotein–cholesterol (LDL–C). Methods and Results We performed a genome-wide association study meta-analysis of PCSK9 levels in up to 12 721 samples of European ancestry. The estimated heritability was 10.3%, which increased to 12.6% using only samples from patients without statin treatment. We successfully replicated the known PCSK9 hit consisting of three independent signals. Interestingly, in a study of 300 African Americans, we confirmed the locus with a different PCSK9 variant. Beyond PCSK9, our meta-analysis detected three novel loci with genome-wide significance. Co-localization analysis with cis-eQTLs and lipid traits revealed biologically plausible candidate genes at two of them: APOB and TM6SF2. In a bivariate Mendelian Randomization analysis, we detected a strong effect of PCSK9 on LDL-C, but not vice versa. LDL-C mediated 63% of the total causal effect of PCSK9 on CAD. Conclusion Our study identified novel genetic loci with plausible candidate genes affecting PCSK9 levels. Ethnic heterogeneity was observed at the PCSK9 locus itself. Although the causal effect of PCSK9 on CAD is mainly mediated by LDL-C, an independent direct effect also occurs.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Jesper Gadin
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital Solna, Sweden
| | - Elizabeth Theusch
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.,SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Stefanie M Hauck
- Metabolomics and Proteomics Core and Research Unit Protein Science, Helmholtz Zentrum München, Neuherberg, Germany
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig.,Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Hubert Scharnagl
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Ronald M Krauss
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA.,Department of Medicine, University of California San Francisco, Oakland, CA, USA
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.,Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria.,SYNLAB Academy, SYNALB Holding Deutschland GmbH, Mannheim, Germany
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig.,Faculty of Medicine, Kiel University, Kiel, Germany
| | - Angela Silveira
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital Solna, Sweden
| | - Ferdinand M Vant Hooft
- Division of Cardiovascular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital Solna, Sweden
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, Medical Faculty, University of Leipzig, Leipzig, Germany
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21
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Zhou X, Cai X. Joint eQTL mapping and inference of gene regulatory network improves power of detecting both cis- and trans-eQTLs. Bioinformatics 2021; 38:149-156. [PMID: 34487140 PMCID: PMC8696109 DOI: 10.1093/bioinformatics/btab609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/19/2021] [Accepted: 08/25/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: (i) relatively small size of samples available, and (ii) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when one tries to identify trans-eQTLs that are far away from the genes they influence. If one can exploit co-expressed genes jointly in eQTL-mapping, effective sample size can be increased. Furthermore, using the structure of the gene regulatory network (GRN) may help to identify trans-eQTLs without increasing multiple testing burden. RESULTS In this article, we use the structure equation model (SEM) to model both GRN and effect of eQTLs on gene expression, and then develop a novel algorithm, named sparse SEM for eQTL mapping (SSEMQ), to conduct joint eQTL mapping and GRN inference. The SEM can exploit co-expressed genes jointly in eQTL mapping and also use GRN to determine trans-eQTLs. Computer simulations demonstrate that our SSEMQ significantly outperforms nine existing eQTL mapping methods. SSEMQ is further used to analyze two real datasets of human breast and whole blood tissues, yielding a number of cis- and trans-eQTLs. AVAILABILITY AND IMPLEMENTATION R package ssemQr is available at https://github.com/Ivis4ml/ssemQr.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xin Zhou
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, USA
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22
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Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, Kirsten H, Saha A, Kreuzhuber R, Yazar S, Brugge H, Oelen R, de Vries DH, van der Wijst MGP, Kasela S, Pervjakova N, Alves I, Favé MJ, Agbessi M, Christiansen MW, Jansen R, Seppälä I, Tong L, Teumer A, Schramm K, Hemani G, Verlouw J, Yaghootkar H, Sönmez Flitman R, Brown A, Kukushkina V, Kalnapenkis A, Rüeger S, Porcu E, Kronberg J, Kettunen J, Lee B, Zhang F, Qi T, Hernandez JA, Arindrarto W, Beutner F, Dmitrieva J, Elansary M, Fairfax BP, Georges M, Heijmans BT, Hewitt AW, Kähönen M, Kim Y, Knight JC, Kovacs P, Krohn K, Li S, Loeffler M, Marigorta UM, Mei H, Momozawa Y, Müller-Nurasyid M, Nauck M, Nivard MG, Penninx BWJH, Pritchard JK, Raitakari OT, Rotzschke O, Slagboom EP, Stehouwer CDA, Stumvoll M, Sullivan P, 't Hoen PAC, Thiery J, Tönjes A, van Dongen J, van Iterson M, Veldink JH, Völker U, Warmerdam R, Wijmenga C, Swertz M, Andiappan A, Montgomery GW, Ripatti S, Perola M, Kutalik Z, Dermitzakis E, Bergmann S, Frayling T, van Meurs J, Prokisch H, Ahsan H, Pierce BL, Lehtimäki T, Boomsma DI, Psaty BM, Gharib SA, Awadalla P, Milani L, Ouwehand WH, Downes K, Stegle O, Battle A, Visscher PM, Yang J, Scholz M, Powell J, Gibson G, Esko T, Franke L. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021; 53:1300-1310. [PMID: 34475573 PMCID: PMC8432599 DOI: 10.1038/s41588-021-00913-z] [Citation(s) in RCA: 526] [Impact Index Per Article: 175.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 07/12/2021] [Indexed: 12/22/2022]
Abstract
Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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Affiliation(s)
- Urmo Võsa
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Oncode Institute, Amsterdam, the Netherlands.
- Structural & Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Harm-Jan Westra
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Marc Jan Bonder
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Patrick Deelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
- Genomics Coordination Center, University Medical Centre Groningen, Groningen, the Netherlands
- Department of Genetics, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Biao Zeng
- School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Roman Kreuzhuber
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Seyhan Yazar
- Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, New South Wales, Australia
| | - Harm Brugge
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Roy Oelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Dylan H de Vries
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Monique G P van der Wijst
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Silva Kasela
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Natalia Pervjakova
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Isabel Alves
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- L'institut du thorax, Université de Nantes, CHU Nantes, INSERM, CNRS, Nantes, France
| | - Marie-Julie Favé
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Mawussé Agbessi
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Mark W Christiansen
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Katharina Schramm
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University, Munich, Germany
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Joost Verlouw
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
- School of Life Sciences, College of Liberal Arts and Science, University of Westminster, London, United Kingdom
- Division of Medical Sciences, Department of Health Sciences, Luleå University of Technology, Luleå, Sweden
| | - Reyhan Sönmez Flitman
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Andrew Brown
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Population Health and Genomics, University of Dundee, Dundee, United Kingdom
| | - Viktorija Kukushkina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Sina Rüeger
- Lausanne University Hospital, Lausanne, Switzerland
| | | | - Jaanika Kronberg
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Bernett Lee
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
| | - Futao Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Ting Qi
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jose Alquicira Hernandez
- Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, New South Wales, Australia
| | | | - Frank Beutner
- Heart Center Leipzig, Universität Leipzig, Leipzig, Germany
| | - Julia Dmitrieva
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Mahmoud Elansary
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Benjamin P Fairfax
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Michel Georges
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | | | - Alex W Hewitt
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia
- Centre for Eye Research Australia, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Yungil Kim
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Genetics and Genomic Science Department, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Peter Kovacs
- IFB Adiposity Diseases, Universität Leipzig, Leipzig, Germany
| | - Knut Krohn
- Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universität Leipzig, Leipzig, Germany
| | - Shuang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University Medical Centre Groningen, Groningen, the Netherlands
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Urko M Marigorta
- School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
- Integrative Genomics Lab, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Bizkaia Science and Technology Park, Derio, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Hailang Mei
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Yukihide Momozawa
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University, Munich, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Michel G Nivard
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Jonathan K Pritchard
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Olaf Rotzschke
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
| | | | - Coen D A Stehouwer
- Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Patrick Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter A C 't Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute for Laboratory Medicine, LIFE-Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
| | - Anke Tönjes
- Department of Medicine, Universität Leipzig, Leipzig, Germany
| | - Jenny van Dongen
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | | | - Jan H Veldink
- UMC Utrecht Brain Center, University Medical Center Utrecht, Department of Neurology, Utrecht University, Utrecht, the Netherlands
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Robert Warmerdam
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Oncode Institute, Amsterdam, the Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Morris Swertz
- Genomics Coordination Center, University Medical Centre Groningen, Groningen, the Netherlands
| | - Anand Andiappan
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Markus Perola
- National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland
| | - Zoltan Kutalik
- Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Emmanouil Dermitzakis
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Timothy Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Holger Prokisch
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Sina A Gharib
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Philip Awadalla
- Computational Biology, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Joseph Powell
- Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Greg Gibson
- School of Biological Sciences, Georgia Tech, Atlanta, GA, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Oncode Institute, Amsterdam, the Netherlands.
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23
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Chiliński M, Sengupta K, Plewczynski D. From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect. Semin Cell Dev Biol 2021; 121:171-185. [PMID: 34429265 DOI: 10.1016/j.semcdb.2021.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 08/06/2021] [Accepted: 08/12/2021] [Indexed: 12/30/2022]
Abstract
The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.
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Affiliation(s)
- Mateusz Chiliński
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland; Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Kaustav Sengupta
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Dariusz Plewczynski
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland; Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland.
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24
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Schierding W, Horsfield JA, O'Sullivan JM. Low tolerance for transcriptional variation at cohesin genes is accompanied by functional links to disease-relevant pathways. J Med Genet 2021; 58:534-542. [PMID: 32917770 PMCID: PMC8327319 DOI: 10.1136/jmedgenet-2020-107095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/08/2020] [Accepted: 06/20/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND The cohesin complex plays an essential role in genome organisation and cell division. A full complement of the cohesin complex and its regulators is important for normal development, since heterozygous mutations in genes encoding these components can be sufficient to produce a disease phenotype. The implication that genes encoding the cohesin subunits or cohesin regulators must be tightly controlled and resistant to variability in expression has not yet been formally tested. METHODS Here, we identify spatial-regulatory connections with potential to regulate expression of cohesin loci (Mitotic: SMC1A, SMC3, STAG1, STAG2, RAD21/RAD21-AS; Meiotic: SMC1B, STAG3, REC8, RAD21L1), cohesin-ring support genes (NIPBL, MAU2, WAPL, PDS5A, PDS5B) and CTCF, including linking their expression to that of other genes. We searched the genome-wide association studies (GWAS) catalogue for SNPs mapped or attributed to cohesin genes by GWAS (GWAS-attributed) and the GTEx catalogue for SNPs mapped to cohesin genes by cis-regulatory variants in one or more of 44 tissues across the human body (expression quantitative trail locus-attributed). RESULTS Connections that centre on the cohesin ring subunits provide evidence of coordinated regulation that has little tolerance for perturbation. We used the CoDeS3D SNP-gene attribution methodology to identify transcriptional changes across a set of genes coregulated with the cohesin loci that include biological pathways such as extracellular matrix production and proteasome-mediated protein degradation. Remarkably, many of the genes that are coregulated with cohesin loci are themselves intolerant to loss-of-function. CONCLUSIONS The results highlight the importance of robust regulation of cohesin genes and implicate novel pathways that may be important in the human cohesinopathy disorders.
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Affiliation(s)
| | - Julia A Horsfield
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, Hampshire, UK
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25
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Silberstein M, Nesbit N, Cai J, Lee PH. Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities. J Genet Genomics 2021; 48:173-183. [PMID: 33896739 PMCID: PMC8286309 DOI: 10.1016/j.jgg.2021.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 12/23/2022]
Abstract
Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments.
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Affiliation(s)
- Micah Silberstein
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nicholas Nesbit
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jacquelyn Cai
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Phil H Lee
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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26
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Bertero A. RNA Biogenesis Instructs Functional Inter-Chromosomal Genome Architecture. Front Genet 2021; 12:645863. [PMID: 33732290 PMCID: PMC7957078 DOI: 10.3389/fgene.2021.645863] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/08/2021] [Indexed: 12/12/2022] Open
Abstract
Three-dimensional (3D) genome organization has emerged as an important layer of gene regulation in development and disease. The functional properties of chromatin folding within individual chromosomes (i.e., intra-chromosomal or in cis) have been studied extensively. On the other hand, interactions across different chromosomes (i.e., inter-chromosomal or in trans) have received less attention, being often regarded as background noise or technical artifacts. This viewpoint has been challenged by emerging evidence of functional relationships between specific trans chromatin interactions and epigenetic control, transcription, and splicing. Therefore, it is an intriguing possibility that the key processes involved in the biogenesis of RNAs may both shape and be in turn influenced by inter-chromosomal genome architecture. Here I present the rationale behind this hypothesis, and discuss a potential experimental framework aimed at its formal testing. I present a specific example in the cardiac myocyte, a well-studied post-mitotic cell whose development and response to stress are associated with marked rearrangements of chromatin topology both in cis and in trans. I argue that RNA polymerase II clusters (i.e., transcription factories) and foci of the cardiac-specific splicing regulator RBM20 (i.e., splicing factories) exemplify the existence of trans-interacting chromatin domains (TIDs) with important roles in cellular homeostasis. Overall, I propose that inter-molecular 3D proximity between co-regulated nucleic acids may be a pervasive functional mechanism in biology.
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Affiliation(s)
- Alessandro Bertero
- Department of Laboratory Medicine and Pathology, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, United States
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27
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Chen J, Cao H, Kaufmann T, Westlye LT, Tost H, Meyer-Lindenberg A, Schwarz E. Identification of Reproducible BCL11A Alterations in Schizophrenia Through Individual-Level Prediction of Coexpression. Schizophr Bull 2020; 46:1165-1171. [PMID: 32232389 PMCID: PMC7505190 DOI: 10.1093/schbul/sbaa047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Previous studies have provided evidence for an alteration of genetic coexpression in schizophrenia (SCZ). However, such analyses have thus far lacked biological specificity for individual genes, which may be critical for identifying illness-relevant effects. Therefore, we applied machine learning to identify gene-specific coexpression differences at the individual subject level and compared these between individuals with SCZ, bipolar disorder, major depressive disorder (MDD), autism spectrum disorder (ASD), and healthy controls. Utilizing transcriptome-wide gene expression data from 21 independent datasets, comprising a total of 9509 participants, we identified a reproducible decrease of BCL11A coexpression across 4 SCZ datasets that showed diagnostic specificity for SCZ when compared with ASD and MDD. We further demonstrate that individual-level coexpression differences can be combined in multivariate coexpression scores that show reproducible illness classification across independent datasets in SCZ and ASD. This study demonstrates that machine learning can capture gene-specific coexpression differences at the individual subject level for SCZ and identify novel biomarker candidates.
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Affiliation(s)
- Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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28
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Kim-Hellmuth S, Aguet F, Oliva M, Muñoz-Aguirre M, Kasela S, Wucher V, Castel SE, Hamel AR, Viñuela A, Roberts AL, Mangul S, Wen X, Wang G, Barbeira AN, Garrido-Martín D, Nadel BB, Zou Y, Bonazzola R, Quan J, Brown A, Martinez-Perez A, Soria JM, Getz G, Dermitzakis ET, Small KS, Stephens M, Xi HS, Im HK, Guigó R, Segrè AV, Stranger BE, Ardlie KG, Lappalainen T. Cell type-specific genetic regulation of gene expression across human tissues. Science 2020; 369:eaaz8528. [PMID: 32913075 PMCID: PMC8051643 DOI: 10.1126/science.aaz8528] [Citation(s) in RCA: 158] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 07/31/2020] [Indexed: 12/15/2022]
Abstract
The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.
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Affiliation(s)
- Sarah Kim-Hellmuth
- Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Meritxell Oliva
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Manuel Muñoz-Aguirre
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), Barcelona, Catalonia, Spain
| | - Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Valentin Wucher
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Stephane E Castel
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Andrew R Hamel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ana Viñuela
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Amy L Roberts
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Serghei Mangul
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, Los Angeles, CA, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Gao Wang
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Brian B Nadel
- Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Jie Quan
- Inflammation & Immunology, Pfizer, Cambridge, MA, USA
| | - Andrew Brown
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Population Health and Genomics, University of Dundee, Dundee, Scotland, UK
| | - Angel Martinez-Perez
- Unit of Genomic of Complex Diseases, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain
| | - José Manuel Soria
- Unit of Genomic of Complex Diseases, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Hualin S Xi
- Foundational Neuroscience Center, AbbVie, Cambridge, MA, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Ayellet V Segrè
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | | | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA
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29
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Rode M, Teren A, Wirkner K, Horn K, Kirsten H, Loeffler M, Scholz M, Pott J. Genome-wide association analysis of pulse wave velocity traits provide new insights into the causal relationship between arterial stiffness and blood pressure. PLoS One 2020; 15:e0237237. [PMID: 32790701 PMCID: PMC7425880 DOI: 10.1371/journal.pone.0237237] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 07/22/2020] [Indexed: 11/19/2022] Open
Abstract
Background The pathophysiology of arterial stiffness is not completely understood. Pulse wave velocity (PWV) is an established marker for arterial stiffness. We compare genetics of three PWV modes, namely carotid-femoral PWV (cfPWV), brachial-ankle (baPWV) and brachial-femoral (bfPWV), reflecting different vascular segments to analyse association with genetic variants, heritability and genetic correlation with other biological traits. Furthermore we searched for shared genetic architecture concerning PWV, blood pressure (BP) and coronary artery disease (CAD) and examined the causal relationship between PWV and BP. Methods and results We performed a genome-wide association study (GWAS) for cfPWV, baPWV and bfPWV in LIFE-Adult (N = 3,643–6,734). We analysed the overlap of detected genetic loci with those of BP and CAD and performed genetic correlation analyses. By bidirectional Mendelian Randomization, we assessed the causal relationships between PWV and BP. For cfPWV we identified a new locus with genome-wide significance near SLC4A7 on cytoband 3p24.1 (lead SNP rs939834: p = 2.05x10-8). We replicated a known PWV locus on cytoband 14q32.2 near RP11-61O1.1 (lead SNPs: rs17773233, p = 1.38x10-4; rs1381289, p = 1.91x10-4) For baPWV we estimated a heritability of 28% and significant genetic correlation with hypertension (rg = 0.27, p = 6.65x10-8). We showed a positive causal effect of systolic blood pressure on PWV modes (cfPWV: p = 1.51x10-4; bfPWV: p = 1.45x10-3; baPWV: p = 6.82x10-15). Conclusions We identified a new locus for arterial stiffness and successfully replicated an earlier proposed locus. PWV shares common genetic architecture with BP and CAD. BP causally affects PWV. Larger studies are required to further unravel the genetic determinants and effects of PWV.
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Affiliation(s)
- Michael Rode
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Kerstin Wirkner
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
- * E-mail:
| | - Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
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30
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Scholz M, Henger S, Beutner F, Teren A, Baber R, Willenberg A, Ceglarek U, Pott J, Burkhardt R, Thiery J. Cohort Profile: The Leipzig Research Center for Civilization Diseases–Heart Study (LIFE-Heart). Int J Epidemiol 2020; 49:1439-1440h. [DOI: 10.1093/ije/dyaa075] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Markus Scholz
- Institute for Medical Informatics, Statistic and Epidemiology, University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Sylvia Henger
- Institute for Medical Informatics, Statistic and Epidemiology, University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Frank Beutner
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Andrej Teren
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Ronny Baber
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Anja Willenberg
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Janne Pott
- Institute for Medical Informatics, Statistic and Epidemiology, University of Leipzig, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Ralph Burkhardt
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Joachim Thiery
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
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31
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Fryett JJ, Morris AP, Cordell HJ. Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies. Genet Epidemiol 2020; 44:425-441. [PMID: 32190932 PMCID: PMC8641384 DOI: 10.1002/gepi.22290] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/05/2020] [Accepted: 03/06/2020] [Indexed: 01/14/2023]
Abstract
In transcriptome-wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods-LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model, and Random Forests-by performing cross-validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, the expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and the development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.
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Affiliation(s)
- James J. Fryett
- Population Health Sciences Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew P. Morris
- Division of Musculoskeletal and Dermatological SciencesUniversity of ManchesterManchesterUK
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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Pott J, Beutner F, Horn K, Kirsten H, Olischer K, Wirkner K, Loeffler M, Scholz M. Genome-wide analysis of carotid plaque burden suggests a role of IL5 in men. PLoS One 2020; 15:e0233728. [PMID: 32469969 PMCID: PMC7259763 DOI: 10.1371/journal.pone.0233728] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 05/11/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Carotid artery plaque is an established marker of subclinical atherosclerosis with pronounced sex-dimorphism. Here, we aimed to identify genetic variants associated with carotid plaque burden (CPB) and to examine potential sex-specific genetic effects on plaque sizes. METHODS AND RESULTS We defined six operationalizations of CPB considering plaques in common carotid arteries, carotid bulb, and internal carotid arteries. We performed sex-specific genome-wide association analyses for all traits in the LIFE-Adult cohort (n = 727 men and n = 550 women) and tested significantly associated loci for sex-specific effects. In order to identify causal genes, we analyzed candidate gene expression data for correlation with CPB traits and corresponding sex-specific effects. Further, we tested if previously reported SNP associations with CAD and plaque prevalence are also associated with CBP. We found seven loci with suggestive significance for CPB (p<3.33x10-7), explaining together between 6 and 13% of the CPB variance. Sex-specific analysis showed a genome-wide significant hit for men at 5q31.1 (rs201629990, β = -0.401, p = 5.22x10-9), which was not associated in women (β = -0.127, p = 0.093) with a significant difference in effect size (p = 0.008). Analyses of gene expression data suggested IL5 as the most plausible candidate, as it reflected the same sex-specific association with CPBs (p = 0.037). Known plaque prevalence or CAD loci showed no enrichment in the association with CPB. CONCLUSIONS We showed that CPB is a complementary trait in analyzing genetics of subclinical atherosclerosis. We detected a novel locus for plaque size in men only suggesting a role of IL5. Several estrogen response elements in this locus point towards a functional explanation of the observed sex-specific effect.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Frank Beutner
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Kay Olischer
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Kerstin Wirkner
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
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Levings D, Shaw KE, Lacher SE. Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions. Toxicology 2020; 441:152505. [PMID: 32450112 DOI: 10.1016/j.tox.2020.152505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 05/18/2020] [Accepted: 05/18/2020] [Indexed: 12/27/2022]
Abstract
The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.
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Affiliation(s)
- Daniel Levings
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth Campus, 1035 University Drive, Duluth, MN, 55812, USA
| | - Kirsten E Shaw
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth Campus, 1035 University Drive, Duluth, MN, 55812, USA
| | - Sarah E Lacher
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth Campus, 1035 University Drive, Duluth, MN, 55812, USA.
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Mortlock S, Kendarsari RI, Fung JN, Gibson G, Yang F, Restuadi R, Girling JE, Holdsworth-Carson SJ, Teh WT, Lukowski SW, Healey M, Qi T, Rogers PAW, Yang J, McKinnon B, Montgomery GW. Tissue specific regulation of transcription in endometrium and association with disease. Hum Reprod 2020; 35:377-393. [PMID: 32103259 PMCID: PMC7048713 DOI: 10.1093/humrep/dez279] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 11/13/2019] [Accepted: 12/02/2019] [Indexed: 12/13/2022] Open
Abstract
STUDY QUESTION Are genetic effects on endometrial gene expression tissue specific and/or associated with reproductive traits and diseases? SUMMARY ANSWER Analyses of RNA-sequence data and individual genotype data from the endometrium identified novel and disease associated, genetic mechanisms regulating gene expression in the endometrium and showed evidence that these mechanisms are shared across biologically similar tissues. WHAT IS KNOWN ALREADY The endometrium is a complex tissue vital for female reproduction and is a hypothesized source of cells initiating endometriosis. Understanding genetic regulation specific to, and shared between, tissue types can aid the identification of genes involved in complex genetic diseases. STUDY DESIGN, SIZE, DURATION RNA-sequence and genotype data from 206 individuals was analysed and results were compared with large publicly available datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS RNA-sequencing and genotype data from 206 endometrial samples was used to identify the influence of genetic variants on gene expression, via expression quantitative trait loci (eQTL) analysis and to compare these endometrial eQTLs with those in other tissues. To investigate the association between endometrial gene expression regulation and reproductive traits and diseases, we conducted a tissue enrichment analysis, transcriptome-wide association study (TWAS) and summary data-based Mendelian randomisation (SMR) analyses. Transcriptomic data was used to test differential gene expression between women with and without endometriosis. MAIN RESULTS AND THE ROLE OF CHANCE A tissue enrichment analysis with endometriosis genome-wide association study summary statistics showed that genes surrounding endometriosis risk loci were significantly enriched in reproductive tissues. A total of 444 sentinel cis-eQTLs (P < 2.57 × 10-9) and 30 trans-eQTLs (P < 4.65 × 10-13) were detected, including 327 novel cis-eQTLs in endometrium. A large proportion (85%) of endometrial eQTLs are present in other tissues. Genetic effects on endometrial gene expression were highly correlated with the genetic effects on reproductive (e.g. uterus, ovary) and digestive tissues (e.g. salivary gland, stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. The TWAS analysis indicated that gene expression at 39 loci is associated with endometriosis, including five known endometriosis risk loci. SMR analyses identified potential target genes pleiotropically or causally associated with reproductive traits and diseases including endometriosis. However, without taking account of genetic variants, a direct comparison between women with and without endometriosis showed no significant difference in endometrial gene expression. LARGE SCALE DATA The eQTL dataset generated in this study is available at http://reproductivegenomics.com.au/shiny/endo_eqtl_rna/. Additional datasets supporting the conclusions of this article are included within the article and the supplementary information files, or are available on reasonable request. LIMITATIONS, REASONS FOR CAUTION Data are derived from fresh tissue samples and expression levels are an average of expression from different cell types within the endometrium. Subtle cell-specifc expression changes may not be detected and differences in cell composition between samples and across the menstrual cycle will contribute to sample variability. Power to detect tissue specific eQTLs and differences between women with and without endometriosis was limited by the sample size in this study. The statistical approaches used in this study identify the likely gene targets for specific genetic risk factors, but not the functional mechanism by which changes in gene expression may influence disease risk. WIDER IMPLICATIONS OF THE FINDINGS Our results identify novel genetic variants that regulate gene expression in endometrium and the majority of these are shared across tissues. This allows analysis with large publicly available datasets to identify targets for female reproductive traits and diseases. Much larger studies will be required to identify genetic regulation of gene expression that will be specific to endometrium. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Health and Medical Research Council (NHMRC) under project grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321, GNT1083405 and GNT1107258. G.W.M is supported by a NHMRC Fellowship (GNT1078399). J.Y is supported by an ARC Fellowship (FT180100186). There are no competing interests.
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Affiliation(s)
- Sally Mortlock
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Raden I Kendarsari
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jenny N Fung
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Greg Gibson
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Fei Yang
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Restuadi Restuadi
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jane E Girling
- Department of Anatomy, University of Otago, Dunedin, New Zealand
- University of Melbourne Department of Obstetrics and Gynaecology, and Gynaecology Research Centre, Royal Women’s Hospital, Parkville VIC 3052, Australia
| | - Sarah J Holdsworth-Carson
- University of Melbourne Department of Obstetrics and Gynaecology, and Gynaecology Research Centre, Royal Women’s Hospital, Parkville VIC 3052, Australia
| | - Wan Tinn Teh
- University of Melbourne Department of Obstetrics and Gynaecology, and Gynaecology Research Centre, Royal Women’s Hospital, Parkville VIC 3052, Australia
| | - Samuel W Lukowski
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Martin Healey
- University of Melbourne Department of Obstetrics and Gynaecology, and Gynaecology Research Centre, Royal Women’s Hospital, Parkville VIC 3052, Australia
| | - Ting Qi
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter A W Rogers
- University of Melbourne Department of Obstetrics and Gynaecology, and Gynaecology Research Centre, Royal Women’s Hospital, Parkville VIC 3052, Australia
| | - Jian Yang
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Brett McKinnon
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- Department of Obstetrics and Gynaecology, Inselspital University Hospital of Berne, 3010 Berne, Switzerland
| | - Grant W Montgomery
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
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The genetics of migraine and the path to precision medicine. PROGRESS IN BRAIN RESEARCH 2020; 255:403-418. [DOI: 10.1016/bs.pbr.2020.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/08/2020] [Accepted: 05/10/2020] [Indexed: 12/26/2022]
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Jawinski P, Kirsten H, Sander C, Spada J, Ulke C, Huang J, Burkhardt R, Scholz M, Hensch T, Hegerl U. Human brain arousal in the resting state: a genome-wide association study. Mol Psychiatry 2019; 24:1599-1609. [PMID: 29703947 DOI: 10.1038/s41380-018-0052-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 01/22/2018] [Accepted: 02/19/2018] [Indexed: 12/20/2022]
Abstract
Arousal affects cognition, emotion, and behavior and has been implicated in the etiology of psychiatric disorders. Although environmental conditions substantially contribute to the level of arousal, stable interindividual characteristics are well-established and a genetic basis has been suggested. Here we investigated the molecular genetics of brain arousal in the resting state by conducting a genome-wide association study (GWAS). We selected N = 1877 participants from the population-based LIFE-Adult cohort. Participants underwent a 20-min eyes-closed resting state EEG, which was analyzed using the computerized VIGALL 2.1 (Vigilance Algorithm Leipzig). At the SNP-level, GWAS analyses revealed no genome-wide significant locus (p < 5E-8), although seven loci were suggestive (p < 1E-6). The strongest hit was an expression quantitative trait locus (eQTL) of TMEM159 (lead-SNP: rs79472635, p = 5.49E-8). Importantly, at the gene-level, GWAS analyses revealed significant evidence for TMEM159 (p = 0.013, Bonferroni-corrected). By mapping our SNPs to the GWAS results from the Psychiatric Genomics Consortium, we found that all corresponding markers of TMEM159 showed nominally significant associations with Major Depressive Disorder (MDD; 0.006 ≤ p ≤ 0.011). More specifically, variants associated with high arousal levels have previously been linked to an increased risk for MDD. In line with this, the MetaXcan database suggests increased expression levels of TMEM159 in MDD, as well as Autism Spectrum Disorder, and Alzheimer's Disease. Furthermore, our pathway analyses provided evidence for a role of sodium/calcium exchangers in resting state arousal. In conclusion, the present GWAS identifies TMEM159 as a novel candidate gene which may modulate the risk for psychiatric disorders through arousal mechanisms. Our results also encourage the elaboration of the previously reported interrelations between ion-channel modulators, sleep-wake behavior, and psychiatric disorders.
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Affiliation(s)
- Philippe Jawinski
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany. .,Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Leipzig, Germany. .,Depression Research Centre, German Depression Foundation, Leipzig, Germany.
| | - Holger Kirsten
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Christian Sander
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Janek Spada
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Christine Ulke
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Jue Huang
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Leipzig, Germany
| | - Ralph Burkhardt
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Markus Scholz
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Tilman Hensch
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Leipzig, Germany
| | - Ulrich Hegerl
- LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.,Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
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Weingarten MFJ, Scholz M, Wohland T, Horn K, Stumvoll M, Kovacs P, Tönjes A. Circulating Oxytocin Is Genetically Determined and Associated With Obesity and Impaired Glucose Tolerance. J Clin Endocrinol Metab 2019; 104:5621-5632. [PMID: 31361301 DOI: 10.1210/jc.2019-00643] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Accepted: 07/22/2019] [Indexed: 01/01/2023]
Abstract
CONTEXT Despite the emerging evidence on the role of oxytocin (OXT) in metabolic diseases, there is a lack of well-powered studies addressing the relationship of circulating OXT with obesity and diabetes. OBJECTIVES AND DESIGN Here, we measured OXT in a study cohort (n = 721; 396 women, 325 men; mean age ± SD, 47.7 ± 15.2 years) with subphenotypes related to obesity, including anthropometric traits such as body mass index [BMI (mean ± SD), 26.8 ± 4.6 kg/m2], waist-to-hip ratio (WHR; 0.88 ± 0.09), blood parameters (glucose, 5.32 ± 0.50 mmol/L; insulin, 5.3 ± 3.3 µU/mL), and oral glucose tolerance test to clarify the association with OXT. We also tested in a genome-wide association study (GWAS) whether the interindividual variation in OXT serum levels might be explained by genetic variation. RESULTS The OXT concentration was increased in subjects with elevated BMI and positively correlated with WHR, waist circumference, and triglyceride levels. The OXT concentration in subjects with BMI <25 kg/m2 was significantly lower (n = 256; 78.6 pg/mL) than in subjects with a BMI between 25 and 30 kg/m2 (n = 314; 98.5 pg/mL, P = 6 × 10-6) and with BMI >30 kg/m2 (n = 137; 106.4 pg/mL, P = 8 × 10-6). OXT levels were also positively correlated with plasma glucose and insulin and were elevated in subjects with impaired glucose tolerance (P = 4.6 × 10-3). Heritability of OXT was estimated at 12.8%. In a GWAS, two hits in linkage disequilibrium close (19 kb) to the OXT reached genome-wide significant association (top-hit rs12625893, P = 3.1 × 10-8, explained variance 3%). CONCLUSIONS Our data show that OXT is genetically affected by a variant near OXT and is associated with obesity and impaired glucose tolerance.
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Affiliation(s)
| | - Markus Scholz
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center, University of Leipzig, Leipzig, Germany
| | - Tobias Wohland
- IFB Adiposity Diseases, University of Leipzig Medical Center, Leipzig, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center, University of Leipzig, Leipzig, Germany
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- IFB Adiposity Diseases, University of Leipzig Medical Center, Leipzig, Germany
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
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Pott J, Bae YJ, Horn K, Teren A, Kühnapfel A, Kirsten H, Ceglarek U, Loeffler M, Thiery J, Kratzsch J, Scholz M. Genetic Association Study of Eight Steroid Hormones and Implications for Sexual Dimorphism of Coronary Artery Disease. J Clin Endocrinol Metab 2019; 104:5008-5023. [PMID: 31169883 DOI: 10.1210/jc.2019-00757] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 05/31/2019] [Indexed: 02/09/2023]
Abstract
CONTEXT Steroid hormones are important regulators of physiological processes in humans and are under genetic control. A link to coronary artery disease (CAD) is supposed. OBJECTIVE Our main objective was to identify genetic loci influencing steroid hormone levels. As a secondary aim, we searched for causal effects of steroid hormones on CAD. DESIGN We conducted genome-wide meta-association studies for eight steroid hormones: cortisol, dehydroepiandrosterone sulfate (DHEAS), estradiol, and testosterone in two independent cohorts (LIFE-Adult, LIFE-Heart, maximum n = 7667), and progesterone, 17-hydroxyprogesterone, androstenedione, and aldosterone in LIFE-Heart only (maximum n = 2070). All genome-wide significant loci were tested for sex interactions. Furthermore, we tested whether previously reported CAD single-nucleotide polymorphisms were associated with our steroid hormone panel and investigated causal links between hormone levels and CAD status using Mendelian randomization (MR) approaches. RESULTS We discovered 15 novel associated loci for 17-hydroxyprogesterone, progesterone, DHEAS, cortisol, androstenedione, and estradiol. Five of these loci relate to genes directly involved in steroid metabolism, that is, CYP21A1, CYP11B1, CYP17A1, STS, and HSD17B12, almost completing the set of steroidogenic enzymes with genetic associations. Sexual dimorphisms were found for seven of the novel loci. Other loci correspond, for example, to the WNT4/β-catenin pathway. MR revealed that cortisol, androstenedione, 17-hydroxyprogesterone, and DHEA-S had causal effects on CAD. We also observed enrichment of cortisol and testosterone associations among known CAD hits. CONCLUSION Our study greatly improves insight into genetic regulation of steroid hormones and their dependency on sex. These results could serve as a basis for analyzing sexual dimorphism in other complex diseases.
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Affiliation(s)
- Janne Pott
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Yoon Ju Bae
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University Hospital, Leipzig, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Andreas Kühnapfel
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University Hospital, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University Hospital, Leipzig, Germany
| | - Jürgen Kratzsch
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University Hospital, Leipzig, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
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Teumer A, Li Y, Ghasemi S, Prins BP, Wuttke M, Hermle T, Giri A, Sieber KB, Qiu C, Kirsten H, Tin A, Chu AY, Bansal N, Feitosa MF, Wang L, Chai JF, Cocca M, Fuchsberger C, Gorski M, Hoppmann A, Horn K, Li M, Marten J, Noce D, Nutile T, Sedaghat S, Sveinbjornsson G, Tayo BO, van der Most PJ, Xu Y, Yu Z, Gerstner L, Ärnlöv J, Bakker SJL, Baptista D, Biggs ML, Boerwinkle E, Brenner H, Burkhardt R, Carroll RJ, Chee ML, Chee ML, Chen M, Cheng CY, Cook JP, Coresh J, Corre T, Danesh J, de Borst MH, De Grandi A, de Mutsert R, de Vries APJ, Degenhardt F, Dittrich K, Divers J, Eckardt KU, Ehret G, Endlich K, Felix JF, Franco OH, Franke A, Freedman BI, Freitag-Wolf S, Gansevoort RT, Giedraitis V, Gögele M, Grundner-Culemann F, Gudbjartsson DF, Gudnason V, Hamet P, Harris TB, Hicks AA, Holm H, Foo VHX, Hwang SJ, Ikram MA, Ingelsson E, Jaddoe VWV, Jakobsdottir J, Josyula NS, Jung B, Kähönen M, Khor CC, Kiess W, Koenig W, Körner A, Kovacs P, Kramer H, Krämer BK, Kronenberg F, Lange LA, Langefeld CD, Lee JJM, Lehtimäki T, Lieb W, Lim SC, Lind L, Lindgren CM, Liu J, Loeffler M, Lyytikäinen LP, Mahajan A, Maranville JC, Mascalzoni D, McMullen B, Meisinger C, Meitinger T, Miliku K, Mook-Kanamori DO, Müller-Nurasyid M, Mychaleckyj JC, Nauck M, Nikus K, Ning B, Noordam R, Connell JO, Olafsson I, Palmer ND, Peters A, Podgornaia AI, Ponte B, Poulain T, Pramstaller PP, Rabelink TJ, Raffield LM, Reilly DF, Rettig R, Rheinberger M, Rice KM, Rivadeneira F, Runz H, Ryan KA, Sabanayagam C, Saum KU, Schöttker B, Shaffer CM, Shi Y, Smith AV, Strauch K, Stumvoll M, Sun BB, Szymczak S, Tai ES, Tan NYQ, Taylor KD, Teren A, Tham YC, Thiery J, Thio CHL, Thomsen H, Thorsteinsdottir U, Tönjes A, Tremblay J, Uitterlinden AG, van der Harst P, Verweij N, Vogelezang S, Völker U, Waldenberger M, Wang C, Wilson OD, Wong C, Wong TY, Yang Q, Yasuda M, Akilesh S, Bochud M, Böger CA, Devuyst O, Edwards TL, Ho K, Morris AP, Parsa A, Pendergrass SA, Psaty BM, Rotter JI, Stefansson K, Wilson JG, Susztak K, Snieder H, Heid IM, Scholz M, Butterworth AS, Hung AM, Pattaro C, Köttgen A. Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria. Nat Commun 2019; 10:4130. [PMID: 31511532 PMCID: PMC6739370 DOI: 10.1038/s41467-019-11576-0] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 07/23/2019] [Indexed: 02/08/2023] Open
Abstract
Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.
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Affiliation(s)
- Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany.
| | - Yong Li
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Bram P Prins
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
- Renal Division, Department of Medicine, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Tobias Hermle
- Renal Division, Department of Medicine, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Karsten B Sieber
- Target Sciences - Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | - Chengxiang Qiu
- Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, USA
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Epidemiology and Clinical Research, Welch Centre for Prevention, Baltimore, MD, USA
| | - Audrey Y Chu
- Genetics, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Massimiliano Cocca
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
| | - Christian Fuchsberger
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Mathias Gorski
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Man Li
- Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Damia Noce
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Teresa Nutile
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso" - CNR, Naples, Italy
| | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Bamidele O Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Yizhe Xu
- Department of Medicine, Division of Nephrology and Hypertension, University of Utah, Salt Lake City, UT, USA
| | - Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lea Gerstner
- Renal Division, Department of Medicine, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Studies, Dalarna University, Falun, Sweden
| | - Stephan J L Bakker
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Centre, University of Texas Health Science Centre, Houston, TX, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Ralph Burkhardt
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Miao-Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Mengmeng Chen
- Renal Division, Department of Medicine, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tanguy Corre
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - John Danesh
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Martin H de Borst
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alessandro De Grandi
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Aiko P J de Vries
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - Frauke Degenhardt
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Katalin Dittrich
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Centre for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Jasmin Divers
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Kai-Uwe Eckardt
- Intensive Care Medicine, Charité, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Georg Ehret
- Cardiology, Geneva University Hospitals, Geneva, Switzerland
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Janine F Felix
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Barry I Freedman
- Internal Medicine - Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, USA
| | - Ron T Gansevoort
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Molecular Geriatrics, Uppsala University, Uppsala, Sweden
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Franziska Grundner-Culemann
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Pavel Hamet
- Montreal University Hospital Research Centre, CHUM, Montreal, QC, Canada
- Medpharmgene, Montreal, QC, Canada
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Andrew A Hicks
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Hilma Holm
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
| | - Valencia Hui Xian Foo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study, Framingham, MA, USA
- The Centre for Population Studies, NHLBI, Framingham, MA, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Vincent W V Jaddoe
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Johanna Jakobsdottir
- Icelandic Heart Association, Holtasmari 1, Kopavogur, IS-201, Iceland
- The Centre of Public Health Sciences, University of Iceland, Sturlugata 8, Reykjavík, IS-101, Iceland
| | - Navya Shilpa Josyula
- Geisinger Research, Biomedical and Translational Informatics Institute, Rockville, MD, USA
| | - Bettina Jung
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Chiea-Chuen Khor
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Wieland Kiess
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Centre for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Biostatistics, University of Ulm, Ulm, Germany
| | - Antje Körner
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Centre for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Peter Kovacs
- Integrated Research and Treatment Centre Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
- Division of Nephrology and Hypertension, Loyola University Chicago, Chicago, IL, USA
| | - Bernhard K Krämer
- 5th Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jeannette Jen-Mai Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany
| | - Su-Chi Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Diabetes Centre, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Lars Lind
- Cardiovascular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | | | - Deborah Mascalzoni
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | | | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology Ludwig- Maximilians-Universität München at UNIKA-T Augsburg, Augsburg, Germany
| | - Thomas Meitinger
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Kozeta Miliku
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, The Netherlands
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
- Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Josyf C Mychaleckyj
- Centre for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Boting Ning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | | | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | | | - Annette Peters
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | | | - Belen Ponte
- Service de Néphrologie, Geneva University Hospitals, Geneva, Switzerland
| | - Tanja Poulain
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Peter P Pramstaller
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy
| | - Ton J Rabelink
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
- Einthoven Laboratory of Experimental Vascular Research, Leiden University Medical Centre, Leiden, The Netherlands
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Rainer Rettig
- Institute of Physiology, University Medicine Greifswald, Karlsburg, Germany
| | - Myriam Rheinberger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Heiko Runz
- MRL, Merck & Co., Inc., Kenilworth, NJ, USA
- Biogen Inc., Cambridge, MA, USA
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Kai-Uwe Saum
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuan Shi
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Albert V Smith
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Michael Stumvoll
- Department of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany
| | - Benjamin B Sun
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Silke Szymczak
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, USA
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nicholas Y Q Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Andrej Teren
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Centre Leipzig, Leipzig, Germany
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Joachim Thiery
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Hauke Thomsen
- Division of Molecular Genetic Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | | | - Anke Tönjes
- Department of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany
| | - Johanne Tremblay
- Montreal University Hospital Research Centre, CHUM, Montreal, QC, Canada
- CRCHUM, Montreal, QC, Canada
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Durrer Centre for Cardiovascular Research, The Netherlands Heart Institute, Utrecht, The Netherlands
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Suzanne Vogelezang
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Melanie Waldenberger
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Neuherberg, Germany
| | - Chaolong Wang
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Otis D Wilson
- Vanderbilt University Medical Centre, Division of Nephrology & Hypertension, Nashville, TN, USA
| | - Charlene Wong
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Masayuki Yasuda
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shreeram Akilesh
- Kidney Research Institute, University of Washington, Seattle, WA, USA
- Anatomic Pathology, University of Washington Medical Center, Seattle, WA, USA
| | - Murielle Bochud
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology, Diabetology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
| | - Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Centre, Nashville, TN, USA
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Kevin Ho
- Kidney Health Research Institute (KHRI), Geisinger, Danville, PA, USA
- Department of Nephrology, Geisinger, Danville, PA, USA
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Sarah A Pendergrass
- Geisinger Research, Biomedical and Translational Informatics Institute, Danville, PA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Service, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics, Harbor-UCLA Medical Centre, Torrance, CA, USA
- Department of Medicine, Harbor-UCLA Medical Centre, Torrance, CA, USA
| | | | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Centre, Jackson, MS, USA
| | - Katalin Susztak
- Renal Electrolyte and Hypertension Division, Department of Medicine, Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Adam S Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Adriana M Hung
- Vanderbilt University Medical Centre, Division of Nephrology & Hypertension, Nashville, TN, USA
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated to the University of Lübeck), Bolzano, Italy.
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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40
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Wheeler HE, Ploch S, Barbeira AN, Bonazzola R, Andaleon A, Fotuhi Siahpirani A, Saha A, Battle A, Roy S, Im HK. Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits. Genet Epidemiol 2019; 43:596-608. [PMID: 30950127 PMCID: PMC6687523 DOI: 10.1002/gepi.22205] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 02/15/2019] [Accepted: 03/18/2019] [Indexed: 11/17/2022]
Abstract
Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.
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Affiliation(s)
- Heather E. Wheeler
- Department of BiologyLoyola University ChicagoChicagoIllinois
- Department of Computer ScienceLoyola University ChicagoChicagoIllinois
- Department of Public Health SciencesStritch School of Medicine, Loyola University ChicagoMaywoodIllinois
| | - Sally Ploch
- Department of BiologyLoyola University ChicagoChicagoIllinois
| | - Alvaro N. Barbeira
- Section of Genetic Medicine, Department of MedicineUniversity of ChicagoChicagoIllinois
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of MedicineUniversity of ChicagoChicagoIllinois
| | - Angela Andaleon
- Department of BiologyLoyola University ChicagoChicagoIllinois
| | | | - Ashis Saha
- Department of Computer ScienceJohns Hopkins UniversityBaltimoreMaryland
| | - Alexis Battle
- Department of Computer ScienceJohns Hopkins UniversityBaltimoreMaryland
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMaryland
| | - Sushmita Roy
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐MadisonMadisonWisconsin
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of MedicineUniversity of ChicagoChicagoIllinois
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41
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Huang QQ, Ritchie SC, Brozynska M, Inouye M. Power, false discovery rate and Winner's Curse in eQTL studies. Nucleic Acids Res 2019; 46:e133. [PMID: 30189032 PMCID: PMC6294523 DOI: 10.1093/nar/gky780] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 08/17/2018] [Indexed: 12/16/2022] Open
Abstract
Investigation of the genetic architecture of gene expression traits has aided interpretation of disease and trait-associated genetic variants; however, key aspects of expression quantitative trait loci (eQTL) study design and analysis remain understudied. We used extensive, empirically driven simulations to explore eQTL study design and the performance of various analysis strategies. Across multiple testing correction methods, false discoveries of genes with eQTLs (eGenes) were substantially inflated when false discovery rate (FDR) control was applied to all tests and only appropriately controlled using hierarchical procedures. All multiple testing correction procedures had low power and inflated FDR for eGenes whose causal SNPs had small allele frequencies using small sample sizes (e.g. frequency <10% in 100 samples), indicating that even moderately low frequency eQTL SNPs (eSNPs) in these studies are enriched for false discoveries. In scenarios with ≥80% power, the top eSNP was the true simulated eSNP 90% of the time, but substantially less frequently for very common eSNPs (minor allele frequencies >25%). Overestimation of eQTL effect sizes, so-called ‘Winner’s Curse’, was common in low and moderate power settings. To address this, we developed a bootstrap method (BootstrapQTL) that led to more accurate effect size estimation. These insights provide a foundation for future eQTL studies, especially those with sampling constraints and subtly different conditions.
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Affiliation(s)
- Qin Qin Huang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne 3004, Victoria, Australia.,Department of Clinical Pathology, University of Melbourne, Parkville 3010, Victoria, Australia
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne 3004, Victoria, Australia.,Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Marta Brozynska
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne 3004, Victoria, Australia.,Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne 3004, Victoria, Australia.,Department of Clinical Pathology, University of Melbourne, Parkville 3010, Victoria, Australia.,Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.,The Alan Turing Institute, London, UK
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42
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Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, Tin A, Wang L, Chu AY, Hoppmann A, Kirsten H, Giri A, Chai JF, Sveinbjornsson G, Tayo BO, Nutile T, Fuchsberger C, Marten J, Cocca M, Ghasemi S, Xu Y, Horn K, Noce D, van der Most PJ, Sedaghat S, Yu Z, Akiyama M, Afaq S, Ahluwalia TS, Almgren P, Amin N, Ärnlöv J, Bakker SJL, Bansal N, Baptista D, Bergmann S, Biggs ML, Biino G, Boehnke M, Boerwinkle E, Boissel M, Bottinger EP, Boutin TS, Brenner H, Brumat M, Burkhardt R, Butterworth AS, Campana E, Campbell A, Campbell H, Canouil M, Carroll RJ, Catamo E, Chambers JC, Chee ML, Chee ML, Chen X, Cheng CY, Cheng Y, Christensen K, Cifkova R, Ciullo M, Concas MP, Cook JP, Coresh J, Corre T, Sala CF, Cusi D, Danesh J, Daw EW, de Borst MH, De Grandi A, de Mutsert R, de Vries APJ, Degenhardt F, Delgado G, Demirkan A, Di Angelantonio E, Dittrich K, Divers J, Dorajoo R, Eckardt KU, Ehret G, Elliott P, Endlich K, Evans MK, Felix JF, Foo VHX, Franco OH, Franke A, Freedman BI, Freitag-Wolf S, Friedlander Y, Froguel P, Gansevoort RT, Gao H, Gasparini P, Gaziano JM, Giedraitis V, Gieger C, Girotto G, Giulianini F, Gögele M, Gordon SD, Gudbjartsson DF, Gudnason V, Haller T, Hamet P, Harris TB, Hartman CA, Hayward C, Hellwege JN, Heng CK, Hicks AA, Hofer E, Huang W, Hutri-Kähönen N, Hwang SJ, Ikram MA, Indridason OS, Ingelsson E, Ising M, Jaddoe VWV, Jakobsdottir J, Jonas JB, Joshi PK, Josyula NS, Jung B, Kähönen M, Kamatani Y, Kammerer CM, Kanai M, Kastarinen M, Kerr SM, Khor CC, Kiess W, Kleber ME, Koenig W, Kooner JS, Körner A, Kovacs P, Kraja AT, Krajcoviechova A, Kramer H, Krämer BK, Kronenberg F, Kubo M, Kühnel B, Kuokkanen M, Kuusisto J, La Bianca M, Laakso M, Lange LA, Langefeld CD, Lee JJM, Lehne B, Lehtimäki T, Lieb W, Lim SC, Lind L, Lindgren CM, Liu J, Liu J, Loeffler M, Loos RJF, Lucae S, Lukas MA, Lyytikäinen LP, Mägi R, Magnusson PKE, Mahajan A, Martin NG, Martins J, März W, Mascalzoni D, Matsuda K, Meisinger C, Meitinger T, Melander O, Metspalu A, Mikaelsdottir EK, Milaneschi Y, Miliku K, Mishra PP, Mohlke KL, Mononen N, Montgomery GW, Mook-Kanamori DO, Mychaleckyj JC, Nadkarni GN, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Noordam R, O'Connell J, O'Donoghue ML, Olafsson I, Oldehinkel AJ, Orho-Melander M, Ouwehand WH, Padmanabhan S, Palmer ND, Palsson R, Penninx BWJH, Perls T, Perola M, Pirastu M, Pirastu N, Pistis G, Podgornaia AI, Polasek O, Ponte B, Porteous DJ, Poulain T, Pramstaller PP, Preuss MH, Prins BP, Province MA, Rabelink TJ, Raffield LM, Raitakari OT, Reilly DF, Rettig R, Rheinberger M, Rice KM, Ridker PM, Rivadeneira F, Rizzi F, Roberts DJ, Robino A, Rossing P, Rudan I, Rueedi R, Ruggiero D, Ryan KA, Saba Y, Sabanayagam C, Salomaa V, Salvi E, Saum KU, Schmidt H, Schmidt R, Schöttker B, Schulz CA, Schupf N, Shaffer CM, Shi Y, Smith AV, Smith BH, Soranzo N, Spracklen CN, Strauch K, Stringham HM, Stumvoll M, Svensson PO, Szymczak S, Tai ES, Tajuddin SM, Tan NYQ, Taylor KD, Teren A, Tham YC, Thiery J, Thio CHL, Thomsen H, Thorleifsson G, Toniolo D, Tönjes A, Tremblay J, Tzoulaki I, Uitterlinden AG, Vaccargiu S, van Dam RM, van der Harst P, van Duijn CM, Velez Edward DR, Verweij N, Vogelezang S, Völker U, Vollenweider P, Waeber G, Waldenberger M, Wallentin L, Wang YX, Wang C, Waterworth DM, Bin Wei W, White H, Whitfield JB, Wild SH, Wilson JF, Wojczynski MK, Wong C, Wong TY, Xu L, Yang Q, Yasuda M, Yerges-Armstrong LM, Zhang W, Zonderman AB, Rotter JI, Bochud M, Psaty BM, Vitart V, Wilson JG, Dehghan A, Parsa A, Chasman DI, Ho K, Morris AP, Devuyst O, Akilesh S, Pendergrass SA, Sim X, Böger CA, Okada Y, Edwards TL, Snieder H, Stefansson K, Hung AM, Heid IM, Scholz M, Teumer A, Köttgen A, Pattaro C. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 2019; 51:957-972. [PMID: 31152163 PMCID: PMC6698888 DOI: 10.1038/s41588-019-0407-x] [Citation(s) in RCA: 445] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 03/29/2019] [Indexed: 12/18/2022]
Abstract
Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
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Affiliation(s)
- Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Renal Division, Department of Medicine IV, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Yong Li
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Man Li
- Division of Nephrology and Hypertension, Department of Medicine, University of Utah, Salt Lake City, USA
| | - Karsten B Sieber
- Target Sciences-Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Mathias Gorski
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, MD, USA
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | | | - Bamidele O Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Teresa Nutile
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
| | - Christian Fuchsberger
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Yizhe Xu
- Division of Nephrology and Hypertension, Department of Medicine, University of Utah, Salt Lake City, USA
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Damia Noce
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
- Institute of Public Health & Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | | | - Peter Almgren
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Johan Ärnlöv
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Studies, Dalarna University, Stockholm, Sweden
| | - Stephan J L Bakker
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | | | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ginevra Biino
- Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Mathilde Boissel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso Plattner Institute and University of Potsdam, Potsdam, Germany
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Marco Brumat
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Adam S Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Eric Campana
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Archie Campbell
- Center for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Center for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Mickaël Canouil
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eulalia Catamo
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Cardiology, Ealing Hospital, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Miao-Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Yurong Cheng
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Kaare Christensen
- Unit of Epidemiology, Biostatistics and Biodemography, Department of Public Health, Southern Denmark University, Odense, Denmark
| | - Renata Cifkova
- Center for Cardiovascular Prevention, Charles University in Prague, First Faculty of Medicine and Thomayer Hospital, Prague, Czech Republic
- Department of Medicine II, Charles University in Prague, First Faculty of Medicine, Prague, Czech Republic
| | - Marina Ciullo
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tanguy Corre
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Daniele Cusi
- Institute of Biomedical Technologies, National Research Council of Italy, Milan, Italy
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
| | - John Danesh
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - E Warwick Daw
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Martin H de Borst
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alessandro De Grandi
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Aiko P J de Vries
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Frauke Degenhardt
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Graciela Delgado
- Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Emanuele Di Angelantonio
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, Cambridge, UK
| | - Katalin Dittrich
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Jasmin Divers
- Public Health Sciences-Biostatistics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Kai-Uwe Eckardt
- Intensive Care Medicine, Charité, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Georg Ehret
- Cardiology, Geneva University Hospitals, Geneva, Switzerland
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- Imperial College NIHR Biomedical Research Center, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
- Health Data Research UK-London, London, UK
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Janine F Felix
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Valencia Hui Xian Foo
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Barry I Freedman
- Section on Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Yechiel Friedlander
- School of Public Health and Community Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Philippe Froguel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
- Department of Genomics of Common Disease, Imperial College London, London, UK
| | - Ron T Gansevoort
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - He Gao
- Department of Epidemiology and Biostatistics, MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Paolo Gasparini
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Vilmantas Giedraitis
- Molecular Geriatrics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Giorgia Girotto
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Toomas Haller
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pavel Hamet
- Montreal University Hospital Research Center, CHUM, Montreal, Quebec, Canada
- Medpharmgene, Montreal, Quebec, Canada
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Bethesda, MD, USA
| | - Catharina A Hartman
- Interdisciplinary Center of Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Jacklyn N Hellwege
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - Andrew A Hicks
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, China
- Shanghai Industrial Technology Institute, Shanghai, China
| | - Nina Hutri-Kähönen
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland
- Department of Pediatrics, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study, Framingham, MA, USA
- The Center for Population Studies, NHLBI, Framingham, MA, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Olafur S Indridason
- Division of Nephrology, Internal Medicine Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Erik Ingelsson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Vincent W V Jaddoe
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Peter K Joshi
- Center for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Navya Shilpa Josyula
- Geisinger Research, Biomedical and Translational Informatics Institute, Rockville, MD, USA
| | - Bettina Jung
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Candace M Kammerer
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Mika Kastarinen
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Chiea-Chuen Khor
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Wieland Kiess
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Marcus E Kleber
- Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Wolfgang Koenig
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Epidemiology and Biostatistics, University of Ulm, Ulm, Germany
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Antje Körner
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Peter Kovacs
- Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Aldi T Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Alena Krajcoviechova
- Center for Cardiovascular Prevention, Charles University in Prague, First Faculty of Medicine and Thomayer Hospital, Prague, Czech Republic
- Department of Medicine II, Charles University in Prague, First Faculty of Medicine, Prague, Czech Republic
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
- Division of Nephrology and Hypertension, Loyola University Chicago, Chicago, IL, USA
| | - Bernhard K Krämer
- Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama (Kanagawa), Japan
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Mikko Kuokkanen
- The Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland
| | - Johanna Kuusisto
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Martina La Bianca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Markku Laakso
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver-Anschutz Medical Campus, Aurora, CO, USA
| | - Carl D Langefeld
- Public Health Sciences-Biostatistics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jeannette Jen-Mai Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany
| | - Su-Chi Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Diabetes Center, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Lars Lind
- Cardiovascular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Mary Ann Lukas
- Target Sciences-Genetics, GlaxoSmithKline, Albuquerque, NM, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anubha Mahajan
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK
- Oxford Center for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jade Martins
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Winfried März
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Medical Clinic V, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Deborah Mascalzoni
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology, Ludwig- Maximilians-Universität München at UNIKA-T Augsburg, Augsburg, Germany
| | - Thomas Meitinger
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Olle Melander
- Hypertension and Cardiovascular Disease, Department of Clincial Sciences Malmö, Lund University, Malmö, Sweden
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Kozeta Miliku
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, St Lucia, Queensland, Australia
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, VA, USA
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, Tampere University, Tampere, Finland
| | - Boting Ning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Michelle L O'Donoghue
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- TIMI Study Group, Boston, MA, USA
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Albertine J Oldehinkel
- Interdisciplinary Center of Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marju Orho-Melander
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden
| | - Willem H Ouwehand
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | | | - Runolfur Palsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Division of Nephrology, Internal Medicine Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Thomas Perls
- Department of Medicine, Geriatrics Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
| | - Mario Pirastu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Li Punti, Sassari, Italy
| | - Nicola Pirastu
- Center for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Giorgio Pistis
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | | | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
- Gen-info Ltd, Zagreb, Croatia
| | - Belen Ponte
- Service de Néphrologie, Geneva University Hospitals, Geneva, Switzerland
| | - David J Porteous
- Center for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Center for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Tanja Poulain
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Peter P Pramstaller
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bram P Prins
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ton J Rabelink
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory of Experimental Vascular Research, Leiden University Medical Center, Leiden, the Netherlands
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | | | - Rainer Rettig
- Institute of Physiology, University Medicine Greifswald, Karlsburg, Germany
| | - Myriam Rheinberger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Federica Rizzi
- Department of Health Sciences, University of Milan, Milano, Italy
- ePhood Scientific Unit, ePhood SRL, Milano, Italy
| | - David J Roberts
- NHS Blood and Transplant, BRC Oxford Haematology Theme; Nuffield Division of Clinical Laboratory Sciences; University of Oxford, Oxford, UK
| | - Antonietta Robino
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | | | - Igor Rudan
- Center for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yasaman Saba
- Molecular Biology and Biochemistry, Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | | | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Erika Salvi
- Department of Health Sciences, University of Milan, Milano, Italy
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Kai-Uwe Saum
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Center for Molecular Medicine, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Christina-Alexandra Schulz
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden
| | - Nicole Schupf
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Gertrude H. Sergievsky Center, Columbia University Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, USA
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuan Shi
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Albert V Smith
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | | | | | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-Universität München, München, Germany
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Stumvoll
- Department of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany
| | - Per O Svensson
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
- Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - Silke Szymczak
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Salman M Tajuddin
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Nicholas Y Q Tan
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Hauke Thomsen
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - Anke Tönjes
- Department of Endocrinology and Nephrology, University of Leipzig, Leipzig, Germany
| | - Johanne Tremblay
- Montreal University Hospital Research Center, CHUM, Montreal, Quebec, Canada
- CRCHUM, Montreal, Canada
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Simona Vaccargiu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Li Punti, Sassari, Italy
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Durrer Center for Cardiovascular Research, The Netherlands Heart Institute, Utrecht, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Digna R Velez Edward
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Department of Obstetrics and Gynecology, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Suzanne Vogelezang
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Peter Vollenweider
- Internal Medicine, Department of Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Gerard Waeber
- Internal Medicine, Department of Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Lars Wallentin
- Cardiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chaolong Wang
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Harvey White
- Green Lane Cardiovascular Service, Auckland City Hospital and University of Auckland, Auckland, New Zealand
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sarah H Wild
- Center for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - James F Wilson
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Center for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Charlene Wong
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Liang Xu
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Masayuki Yasuda
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | | | - Weihua Zhang
- Department of Cardiology, Ealing Hospital, Middlesex, UK
- Department of Epidemiology and Biostatistics, MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Murielle Bochud
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Service, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kevin Ho
- Kidney Health Research Institute (KHRI), Geisinger, Danville, PA, USA
- Department of Nephrology, Geisinger, Danville, PA, USA
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK
| | - Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Shreeram Akilesh
- Kidney Research Institute, University of Washington, Seattle, WA, USA
- Anatomic Pathology, University of Washington Medical Center, Seattle, WA, USA
| | - Sarah A Pendergrass
- Geisinger Research, Biomedical and Translational Informatics Institute, Danville, PA, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Regensburg, Germany
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Osaka, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Todd L Edwards
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Adriana M Hung
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Division of Nephrology & Hypertension, Nashville, TN, USA
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy.
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43
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Sodium valproate rescues expression of TRANK1 in iPSC-derived neural cells that carry a genetic variant associated with serious mental illness. Mol Psychiatry 2019; 24:613-624. [PMID: 30135510 PMCID: PMC6894932 DOI: 10.1038/s41380-018-0207-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 05/19/2018] [Accepted: 06/08/2018] [Indexed: 01/07/2023]
Abstract
Biological characterization of genetic variants identified in genome-wide association studies (GWAS) remains a substantial challenge. Here we used human-induced pluripotent stem cells (iPSC) and their neural derivatives to characterize common variants on chromosome 3p22 that have been associated by GWAS with major mental illnesses. IPSC-derived neural progenitor cells carrying the risk allele of the single nucleotide polymorphism (SNP), rs9834970, displayed lower baseline TRANK1 expression that was rescued by chronic treatment with therapeutic dosages of valproic acid (VPA). VPA had the greatest effects on TRANK1 expression in iPSC, NPC, and astrocytes. Although rs9834970 has no known function, we demonstrated that a nearby SNP, rs906482, strongly affects binding by the transcription factor, CTCF, and that the high-affinity allele usually occurs on haplotypes carrying the rs9834970 risk allele. Decreased expression of TRANK1 perturbed expression of many genes involved in neural development and differentiation. These findings have important implications for the pathophysiology of major mental illnesses and the development of novel therapeutics.
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44
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Savel D, Koyutürk M. Characterizing human genomic coevolution in locus-gene regulatory interactions. BioData Min 2019; 12:8. [PMID: 30923571 PMCID: PMC6419833 DOI: 10.1186/s13040-019-0195-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 02/19/2019] [Indexed: 11/10/2022] Open
Abstract
Background Coevolution has been used to identify and predict interactions and functional relationships between proteins of many different organisms including humans. Current efforts in annotating the human genome increasingly show that non-coding DNA sequence has important functional and regulatory interactions. Furthermore, regulatory elements do not necessarily reside in close proximity of the coding region for their target genes. Results We characterize coevolution as it appears in locus-gene interactions in the human genome, focusing on expression Quantitative Trait - Locus (eQTL) interactions. Our results show that in these interactions the conservation status of the loci is predictive of the conservation status of their target genes. Furthermore, comparing the phylogenetic histories of intra-chromosomal pairs of loci and transcription start sites, we find that pairs that appear coevolved are enriched for cis-eQTL interactions. Exploring this property we found that coevolution might be useful in prioritizing association tests in cis-eQTL detection. Conclusions The relationship between the conservation status of pairs of loci and protein coding transcription start sites reveal correlations with regulatory interactions. Pairs that appear coevolved are enriched for intra-chromosomal regulatory interactions, thus our results suggest that measures of coevolution can be useful for prediction and detection of new interactions. Measures of coevolution are genome-wide and could potentially be used to prioritize the detection of distant or inter-chromosomal interactions such as trans-eQTL interactions in the human genome.
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Affiliation(s)
- Daniel Savel
- 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106 OH USA
| | - Mehmet Koyutürk
- 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106 OH USA.,2Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, 44106 OH USA
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45
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Schulz A, Müller NV, van de Lest NA, Eisenreich A, Schmidbauer M, Barysenka A, Purfürst B, Sporbert A, Lorenzen T, Meyer AM, Herlan L, Witten A, Rühle F, Zhou W, de Heer E, Scharpfenecker M, Panáková D, Stoll M, Kreutz R. Analysis of the genomic architecture of a complex trait locus in hypertensive rat models links Tmem63c to kidney damage. eLife 2019; 8:42068. [PMID: 30900988 PMCID: PMC6478434 DOI: 10.7554/elife.42068] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 03/20/2019] [Indexed: 12/23/2022] Open
Abstract
Unraveling the genetic susceptibility of complex diseases such as chronic kidney disease remains challenging. Here, we used inbred rat models of kidney damage associated with elevated blood pressure for the comprehensive analysis of a major albuminuria susceptibility locus detected in these models. We characterized its genomic architecture by congenic substitution mapping, targeted next-generation sequencing, and compartment-specific RNA sequencing analysis in isolated glomeruli. This led to prioritization of transmembrane protein Tmem63c as a novel potential target. Tmem63c is differentially expressed in glomeruli of allele-specific rat models during onset of albuminuria. Patients with focal segmental glomerulosclerosis exhibited specific TMEM63C loss in podocytes. Functional analysis in zebrafish revealed a role for tmem63c in mediating the glomerular filtration barrier function. Our data demonstrate that integrative analysis of the genomic architecture of a complex trait locus is a powerful tool for identification of new targets such as Tmem63c for further translational investigation. The human kidneys filter the entire volume of the blood about 300 times each day. This ability depends on specialized cells, known as podocytes, which wrap around some of the blood vessels in the kidney. These cells control which molecules leave the blood based on their size. Normally large molecules like proteins are blocked, while smaller molecules including waste products, toxins, excess water and salts pass through into the urine. If this filtration system is damaged, by high blood pressure, for example, it can lead to chronic kidney disease. A hallmark of this disease, often called CKD for short, is high levels of the protein albumin in the urine. Previous studies involving rats with high blood pressure have found several regions of the genome that contribute to high levels of albumin in the urine, including one on chromosome 6. However, this region contains several genes and it was unclear which genes affected the condition. Schulz et al. set out to narrow down the list and find specific genes that might contribute to elevated albumin in the urine of rats with high blood pressure. This search identified the gene for a protein called TMEM63c as a likely candidate. This protein spans the outer membrane of podocyte cells. Analysis of kidney biopsies showed that patients with chronic kidney disease also had low levels of this protein in their podocytes. Further experiments, this time in zebrafish, showed that reducing the activity of the gene for tmem63c led to damaged podocytes and a leakier filter in the kidneys. The results suggest that this gene plays an important role in the integrity of the kidneys filtration barrier. It is possible that faulty versions of this gene are behind some cases of chronic kidney disease. If this proves to be the case, a better understanding of the role of this gene may lead to new treatments for the condition.
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Affiliation(s)
- Angela Schulz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany
| | - Nicola Victoria Müller
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany.,Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Electrochemical Signaling in Development and Disease, Berlin, Germany
| | - Nina Anne van de Lest
- Department of Pathology, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Andreas Eisenreich
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany
| | - Martina Schmidbauer
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany
| | - Andrei Barysenka
- Westfälische Wilhelms University, Genetic Epidemiology, Institute for Human Genetics, Münster, Germany
| | - Bettina Purfürst
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Core Facility Electron Microscopy, Berlin, Germany
| | - Anje Sporbert
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Advanced Light Microscopy, Berlin, Germany
| | - Theodor Lorenzen
- Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany
| | | | - Laura Herlan
- Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany
| | - Anika Witten
- Westfälische Wilhelms University, Genetic Epidemiology, Institute for Human Genetics, Münster, Germany
| | - Frank Rühle
- Westfälische Wilhelms University, Genetic Epidemiology, Institute for Human Genetics, Münster, Germany
| | - Weibin Zhou
- Division of Nephrology, Department of Medicine, Center for Human Disease Modeling, Duke University School of Medicine, Durham, United States
| | - Emile de Heer
- Department of Pathology, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Marion Scharpfenecker
- Department of Pathology, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Daniela Panáková
- DZHK (German Centre for Cardiovascular Research), Partner site Berlin, Berlin, Germany
| | - Monika Stoll
- Westfälische Wilhelms University, Genetic Epidemiology, Institute for Human Genetics, Münster, Germany.,Department of Biochemistry, Maastricht University, Genetic Epidemiology and Statistical Genetics, Maastricht, The Netherlands
| | - Reinhold Kreutz
- Institute of Clinical Pharmacology and Toxicology, Berlin Institute of Health, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner site Berlin, Berlin, Germany
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46
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Ho EYK, Cao Q, Gu M, Chan RWL, Wu Q, Gerstein M, Yip KY. Shaping the nebulous enhancer in the era of high-throughput assays and genome editing. Brief Bioinform 2019; 21:836-850. [PMID: 30895290 DOI: 10.1093/bib/bbz030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 02/15/2019] [Accepted: 02/26/2019] [Indexed: 01/22/2023] Open
Abstract
Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.
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Affiliation(s)
| | - Qin Cao
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Mengting Gu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
| | - Ricky Wai-Lun Chan
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Qiong Wu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.,School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.,Program in Computational Biology and Bioinformatics.,Department of Computer Science, Yale University, New Haven, Connecticut, USA
| | - Kevin Y Yip
- Department of Biomedical Engineering.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.,Hong Kong Bioinformatics Centre.,CUHK-BGI Innovation Institute of Trans-omics.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong
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47
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Ali AT, Boehme L, Carbajosa G, Seitan VC, Small KS, Hodgkinson A. Nuclear genetic regulation of the human mitochondrial transcriptome. eLife 2019; 8:e41927. [PMID: 30775970 PMCID: PMC6420317 DOI: 10.7554/elife.41927] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 02/14/2019] [Indexed: 12/21/2022] Open
Abstract
Mitochondria play important roles in cellular processes and disease, yet little is known about how the transcriptional regime of the mitochondrial genome varies across individuals and tissues. By analyzing >11,000 RNA-sequencing libraries across 36 tissue/cell types, we find considerable variation in mitochondrial-encoded gene expression along the mitochondrial transcriptome, across tissues and between individuals, highlighting the importance of cell-type specific and post-transcriptional processes in shaping mitochondrial-encoded RNA levels. Using whole-genome genetic data we identify 64 nuclear loci associated with expression levels of 14 genes encoded in the mitochondrial genome, including missense variants within genes involved in mitochondrial function (TBRG4, MTPAP and LONP1), implicating genetic mechanisms that act in trans across the two genomes. We replicate ~21% of associations with independent tissue-matched datasets and find genetic variants linked to these nuclear loci that are associated with cardio-metabolic phenotypes and Vitiligo, supporting a potential role for variable mitochondrial-encoded gene expression in complex disease.
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Affiliation(s)
- Aminah T Ali
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Lena Boehme
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Guillermo Carbajosa
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Vlad C Seitan
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, School of Life Course SciencesKing’s College LondonLondonUnited Kingdom
| | - Alan Hodgkinson
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
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48
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van den Maagdenberg AMJM, Nyholt DR, Anttila V. Novel hypotheses emerging from GWAS in migraine? J Headache Pain 2019; 20:5. [PMID: 30634909 PMCID: PMC6734558 DOI: 10.1186/s10194-018-0956-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 12/27/2018] [Indexed: 12/23/2022] Open
Abstract
Recent technical advances in genetics made large-scale genome-wide association studies (GWAS) in migraine feasible and have identified over 40 common DNA sequence variants that affect risk for migraine types. Most of the variants, which are all single nucleotide polymorphisms (SNPs), show robust association with migraine as evidenced by the fact that the vast majority replicate in subsequent independent studies. However, despite thorough bioinformatic efforts aimed at linking the migraine risk SNPs with genes and their molecular pathways, there remains quite some discussion as to how successful this endeavour has been, and their current practical use for the diagnosis and treatment of migraine patients. Although existing genetic information seems to favour involvement of vascular mechanisms, but also neuronal and other mechanisms such as metal ion homeostasis and neuronal migration, the complexity of the underlying genetic pathophysiology presents challenges to advancing genetic knowledge to clinical use. A major issue is to what extent one can rely on bioinformatics to pinpoint the actual disease genes, and from this the linked pathways. In this Commentary, we will provide an overview of findings from GWAS in migraine, current hypotheses of the disease pathways that emerged from these findings, and some of the major drawbacks of the approaches used to identify the genes and pathways. We argue that more functional research is urgently needed to turn the hypotheses that emerge from GWAS in migraine to clinically useful information.
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Affiliation(s)
- Arn M. J. M. van den Maagdenberg
- Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Neurology, Leiden University Medical Centre, 9600, 2300 RC Leiden, The Netherlands
| | - Dale R. Nyholt
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD Australia
| | - Verneri Anttila
- Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
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49
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Tönjes A, Scholz M, Krüger J, Krause K, Schleinitz D, Kirsten H, Gebhardt C, Marzi C, Grallert H, Ladenvall C, Heyne H, Laurila E, Kriebel J, Meisinger C, Rathmann W, Gieger C, Groop L, Prokopenko I, Isomaa B, Beutner F, Kratzsch J, Fischer-Rosinsky A, Pfeiffer A, Krohn K, Spranger J, Thiery J, Blüher M, Stumvoll M, Kovacs P. Genome-wide meta-analysis identifies novel determinants of circulating serum progranulin. Hum Mol Genet 2019; 27:546-558. [PMID: 29186428 DOI: 10.1093/hmg/ddx413] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 11/22/2017] [Indexed: 11/14/2022] Open
Abstract
Progranulin is a secreted protein with important functions in processes including immune and inflammatory response, metabolism and embryonic development. The present study aimed at identification of genetic factors determining progranulin concentrations. We conducted a genome-wide association meta-analysis for serum progranulin in three independent cohorts from Europe: Sorbs (N = 848) and KORA (N = 1628) from Germany and PPP-Botnia (N = 335) from Finland (total N = 2811). Single nucleotide polymorphisms (SNPs) associated with progranulin levels were replicated in two additional German cohorts: LIFE-Heart Study (Leipzig; N = 967) and Metabolic Syndrome Berlin Potsdam (Berlin cohort; N = 833). We measured mRNA expression of genes in peripheral blood mononuclear cells (PBMC) by micro-arrays and performed mRNA expression quantitative trait and expression-progranulin association studies to functionally substantiate identified loci. Finally, we conducted siRNA silencing experiments in vitro to validate potential candidate genes within the associated loci. Heritability of circulating progranulin levels was estimated at 31.8% and 26.1% in the Sorbs and LIFE-Heart cohort, respectively. SNPs at three loci reached study-wide significance (rs660240 in CELSR2-PSRC1-MYBPHL-SORT1, rs4747197 in CDH23-PSAP and rs5848 in GRN) explaining 19.4%/15.0% of the variance and 61%/57% of total heritability in the Sorbs/LIFE-Heart Study. The strongest evidence for association was at rs660240 (P = 5.75 × 10-50), which was also associated with mRNA expression of PSRC1 in PBMC (P = 1.51 × 10-21). Psrc1 knockdown in murine preadipocytes led to a consecutive 30% reduction in progranulin secretion. In conclusion, the present meta-GWAS combined with mRNA expression identified three loci associated with progranulin and supports the role of PSRC1 in the regulation of progranulin secretion.
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Affiliation(s)
- Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany.,LIFE Research Center, University of Leipzig, Leipzig 04103, Germany
| | - Jacqueline Krüger
- Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
| | - Kerstin Krause
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Dorit Schleinitz
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany.,LIFE Research Center, University of Leipzig, Leipzig 04103, Germany
| | - Claudia Gebhardt
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany
| | - Carola Marzi
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Claes Ladenvall
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, CRC at Skåne University Hospital, Malmö 20502, Sweden
| | - Henrike Heyne
- Institute of Human Genetics, University of Leipzig, Leipzig 04103, Germany
| | - Esa Laurila
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, CRC at Skåne University Hospital, Malmö 20502, Sweden
| | - Jennifer Kriebel
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Christa Meisinger
- German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany.,German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Wolfgang Rathmann
- German Diabetes Center, Institute of Biometrics and Epidemiology, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg 85764, Germany.,German Research Center for Environmental Health, Institute of Epidemiology II, Helmholtz Center Munich, Neuherberg 85764, Germany
| | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University and Lund University Diabetes Centre, CRC at Skåne University Hospital, Malmö 20502, Sweden
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK.,Department of Genomics of Common Diseases, Imperial College London, London SW7 2AZ, UK
| | - Bo Isomaa
- Department of Social Services and Healthcare, Jakobstad 68601, Finland.,Folkhälsan Research Centre, Helsinki 00290, Finland
| | - Frank Beutner
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig 04103, Germany
| | - Jürgen Kratzsch
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig 04103, Germany
| | - Antje Fischer-Rosinsky
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin, Berlin 10117, Germany
| | - Andreas Pfeiffer
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin, Berlin 10117, Germany.,Department of Clinical Nutrition, German Institute of Human Nutrition, Nuthetal 14558, Germany
| | - Knut Krohn
- Interdisciplinary Centre for Clinical Research, University of Leipzig, Leipzig 04103, Germany
| | - Joachim Spranger
- Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin, Berlin 10117, Germany
| | - Joachim Thiery
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig 04103, Germany
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany.,Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany.,Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
| | - Peter Kovacs
- Leipzig University Medical Center, IFB AdiposityDiseases, University of Leipzig, Leipzig 04103, Germany
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Whalen S, Pollard KS. Most chromatin interactions are not in linkage disequilibrium. Genome Res 2019; 29:334-343. [PMID: 30617125 PMCID: PMC6396425 DOI: 10.1101/gr.238022.118] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 12/12/2018] [Indexed: 02/07/2023]
Abstract
Chromatin interactions and linkage disequilibrium (LD) are both pairwise measurements between genomic loci that show block patterns along mammalian chromosomes. Their values are generally high for sites that are nearby in the linear genome but abruptly drop across block boundaries. One function of chromatin boundaries is to insulate regulatory domains from one another. Since recombination is depressed within genes and between distal regulatory elements and their promoters, we hypothesized that LD and chromatin contact frequency might be correlated genome-wide with the boundaries of LD blocks and chromatin domains frequently coinciding. To comprehensively address this question, we compared chromatin contacts in 22 cell types to LD across billions of pairs of loci in the human genome. These computationally intensive analyses revealed that there is no concordance between LD and chromatin interactions, even at genomic distances below 25 kilobases (kb) where both tend to be high. At genomic distances where LD is approximately zero, chromatin interactions are frequent. While LD is somewhat elevated between distal regulatory elements and their promoters, LD block boundaries are depleted—not enriched—at chromatin boundaries. Finally, gene expression and ontology data suggest that chromatin contacts identify regulatory variants more reliably than do LD and genomic proximity. We conclude that the genomic architectures of genetic and physical interactions are independent, with important implications for gene regulatory evolution, interpretation of genetic association studies, and precision medicine.
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Affiliation(s)
- Sean Whalen
- Gladstone Institutes, San Francisco, California 94158, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, California 94158, USA.,Department of Epidemiology and Biostatistics, Institute for Human Genetics, Quantitative Biology Institute, and Institute for Computational Health Sciences, University of California San Francisco, San Francisco, California 94158, USA.,Chan-Zuckerberg Biohub, San Francisco, California 94158, USA
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