201
|
Hofmeister RJ, Rubinacci S, Ribeiro DM, Buil A, Kutalik Z, Delaneau O. Parent-of-Origin inference for biobanks. Nat Commun 2022; 13:6668. [PMID: 36335127 PMCID: PMC9637181 DOI: 10.1038/s41467-022-34383-6] [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: 03/18/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
Identical genetic variations can have different phenotypic effects depending on their parent of origin. Yet, studies focusing on parent-of-origin effects have been limited in terms of sample size due to the lack of parental genomes or known genealogies. We propose a probabilistic approach to infer the parent-of-origin of individual alleles that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent sharing with second- and third-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. We combine this with robust haplotype inference and haploid imputation to infer the parent-of-origin for 26,393 UK Biobank individuals. We screen 99 phenotypes for parent-of-origin effects and replicate the discoveries of 6 GWAS studies, confirming signals on body mass index, type 2 diabetes, standing height and multiple blood biomarkers, including the known maternal effect at the MEG3/DLK1 locus on platelet phenotypes. We also report a novel maternal effect at the TERT gene on telomere length, thereby providing new insights on the heritability of this phenotype. All our summary statistics are publicly available to help the community to better characterize the molecular mechanisms leading to parent-of-origin effects and their implications for human health.
Collapse
Affiliation(s)
- Robin J Hofmeister
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Simone Rubinacci
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Diogo M Ribeiro
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark.,Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Olivier Delaneau
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
| |
Collapse
|
202
|
Abraham A, LaBella AL, Capra JA, Rokas A. Mosaic patterns of selection in genomic regions associated with diverse human traits. PLoS Genet 2022; 18:e1010494. [PMID: 36342969 PMCID: PMC9671423 DOI: 10.1371/journal.pgen.1010494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 11/17/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022] Open
Abstract
Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer's disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.
Collapse
Affiliation(s)
- Abin Abraham
- Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Abigail L. LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina, United States of America
- North Carolina Research Center, Kannapolis, North Carolina, United States of America
| | - John A. Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America
| |
Collapse
|
203
|
Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, Li C, Smith RF, Wittemans LBL, Bomba L, Menni C, Zierer J, Rossi N, Sheridan PA, Watkins NA, Mangino M, Hysi PG, Di Angelantonio E, Falchi M, Spector TD, Soranzo N, Michelotti GA, Arlt W, Lotta LA, Denaxas S, Hemingway H, Gamazon ER, Howson JMM, Wood AM, Danesh J, Wareham NJ, Kastenmüller G, Fauman EB, Suhre K, Butterworth AS, Langenberg C. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med 2022; 28:2321-2332. [PMID: 36357675 PMCID: PMC9671801 DOI: 10.1038/s41591-022-02046-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 09/16/2022] [Indexed: 11/12/2022]
Abstract
Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.
Collapse
Affiliation(s)
- Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Raffler
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Digital Medicine, University Hospital of Augsburg, Augsburg, Germany
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Rebecca F Smith
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Lorenzo Bomba
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Niccolò Rossi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | | | | | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Pirro G Hysi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Human Technopole, Milan, Italy
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | | | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Joanna M M Howson
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
| |
Collapse
|
204
|
Debost JPG, Thorsteinsson E, Trabjerg B, Benros ME, Albiñana C, Vilhjalmsson BJ, Børglum A, Mors O, Werge T, Mortensen PB, Agerbo E, Petersen LV. Genetic and psychosocial influence on the association between early childhood infections and later psychiatric disorders. Acta Psychiatr Scand 2022; 146:406-419. [PMID: 35999619 PMCID: PMC9826256 DOI: 10.1111/acps.13491] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 01/29/2023]
Abstract
To evaluate the influence of extensive genetic and psychosocial confounding on the association between early childhood infection and five major psychiatric disorders METHODS: A case-cohort study including participants from the Danish iPSYCH2012 sample, a case-cohort sample where all cases born between May 1, 1981, and December 31, 2005, diagnosed with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar affective disorder (BIP), Major Depressive Disorder (MDD) or schizophrenia (SCZ), were identified and pooled with a representative sample (subcohort) of the Danish population. We used Cox proportional hazards regression customized to the case-cohort setup to calculate hazard ratios of outcome with 95% confidence intervals (CIs), following exposure to early childhood infection before the age of 5 years for ADHD and ASD, and before the age of 10 years for BIP, MDD, and SCZ. To evaluate psychosocial confounding we included sex, calendar period, sibling infections, urbanicity, parental socio-economic status, parental mental health information, and polygenic risk scores for all five disorders, as covariates. To estimate how liability for psychiatric disorders measured through the PRS influenced the risk of early childhood infection, we calculated odds ratios (ORs) with 95% CIs, using logistic regression RESULTS: Early childhood infection was associated with ADHD, ASD, MDD, and SCZ with number of childhood infections increasing the hazard. The HR was still significant in the model with full adjustments after 1 infection for ADHD (HR 1.29, 95% CI: 1.19-1.41), ASD (HR 1.28, 95% CI: 1.18-1.40), MDD (HR 1.23, 95% CI: 1.14-1.33), and SCZ (HR 1.21, 95% CI: 1.07-1.36), but not for BIP (HR1.17, 95% CI: 0.96-1.42). Probands exposed to sibling infections, but not own infection had an absolute risk of ADHD, BIP, MDD, and SCZ that closely approached the absolute risk for individuals exposed to own infections. We found evidence of gene-environment correlation with higher PRS of MDD and to some extent SCZ increasing the risk of infections and higher PRS of BIP associated with significantly decreased risk CONCLUSION: Early childhood infection is significantly associated with ADHD, ASD, MDD, and SCZ and not explained by genetic or psychosocial confounding. Although we found evidence of gene-environment correlation, it had minor impact on the results.
Collapse
Affiliation(s)
- Jean‐Christophe Philippe Goldtsche Debost
- Department of PsychosisAarhus University Hospital – PsychiatryAarhusDenmark,National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark
| | - Erla Thorsteinsson
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark
| | - Betina Trabjerg
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark
| | - Michael Eriksen Benros
- Biological and Precision Psychiatry, Copenhagen Research Centre for Mental Health, Mental Health Centre CopenhagenCopenhagen UniversityCopenhagenDenmark,Department of Immunology and Microbiology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Clara Albiñana
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark
| | - Bjarni Johann Vilhjalmsson
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark
| | - Anders Børglum
- iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark,Department of Biomedicine and Centre for Integrative Sequencing, iSEQAarhus UniversityAarhusDenmark,Center for Genomics and Personalized MedicineCentral Region Denmark and Aarhus UniversityAarhusDenmark
| | - Ole Mors
- iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark,Psychosis Research UnitAarhus University Hospital – PsychiatryAarhusDenmark
| | - Thomas Werge
- iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark,Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health ServicesRoskildeDenmark,Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark,Center for GeoGeneticsGLOBE Institute, University of CopenhagenCopenhagenDenmark
| | - Preben Bo Mortensen
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark,CIRRAU – Centre for Integrated Register‐based ResearchAarhus UniversityAarhusDenmark
| | - Esben Agerbo
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark,CIRRAU – Centre for Integrated Register‐based ResearchAarhus UniversityAarhusDenmark
| | - Liselotte Vogdrup Petersen
- National Centre for Register‐based Research, Department of Economics and Business EconomicsAarhus UniversityAarhusDenmark,iPSYCH ‐ The Lundbeck Foundation Initiative for Integrative Psychiatric ResearchDenmark
| |
Collapse
|
205
|
Tiplady KM, Lopdell TJ, Sherlock RG, Johnson TJ, Spelman RJ, Harris BL, Davis SR, Littlejohn MD, Garrick DJ. Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins. J Dairy Sci 2022; 105:9763-9791. [DOI: 10.3168/jds.2022-22089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
|
206
|
Assessing the contribution of genetic nurture to refractive error. Eur J Hum Genet 2022; 30:1226-1232. [PMID: 35618892 PMCID: PMC9626539 DOI: 10.1038/s41431-022-01126-6] [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: 12/01/2021] [Revised: 05/10/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023] Open
Abstract
Parents pass on both their genes and environment to offspring, prompting debate about the relative importance of nature versus nurture in the inheritance of complex traits. Advances in molecular genetics now make it possible to quantify an individual's genetic predisposition to a trait via his or her 'polygenic score'. However, part of the risk captured by an individual's polygenic score may actually be attributed to the genotype of their parents. In the most well-studied example of this indirect 'genetic nurture' effect, about half the genetic contribution to educational attainment was found to be attributed to parental alleles, even if those alleles were not inherited by the child. Refractive errors, such as myopia, are a common cause of visual impairment and pose high economic and quality-of-life costs. Despite strong evidence that refractive errors are highly heritable, the extent to which genetic risk is conferred directly via transmitted risk alleles or indirectly via the environment that parents create for their children is entirely unknown. Here, an instrumental variable analysis in 1944 pairs of adult siblings from the United Kingdom was used to quantify the proportion of the genetic risk ('single nucleotide polymorphism (SNP) heritability') of refractive error contributed by genetic nurture. We found no evidence of a contribution from genetic nurture: non-within-family SNP-heritability estimate = 0.213 (95% confidence interval 0.134-0.310) and within-family SNP-heritability estimate = 0.250 (0.152-0.372). Our findings imply the genetic contribution to refractive error is principally an intrinsic effect from alleles transmitted from parents to offspring.
Collapse
|
207
|
Lin C, Sun Z, Mei Z, Zeng H, Zhao M, Hu J, Xia M, Huang T, Wang C, Gao X, Zheng Y. The causal associations of circulating amino acids with blood pressure: a Mendelian randomization study. BMC Med 2022; 20:414. [PMID: 36307799 PMCID: PMC9615211 DOI: 10.1186/s12916-022-02612-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Circulating levels of amino acids were associated with blood pressure (BP) in observational studies. However, the causation of such associations has been hypothesized but is difficult to prove in human studies. Here, we aimed to use two-sample Mendelian randomization analyses to evaluate the potential causal associations of circulating levels of amino acids with BP and risk of hypertension. METHODS We generated genetic instruments for circulating levels of nine amino acids by conducting meta-analyses of genome-wide association study (GWAS) in UK Biobank participants with metabolomic data (n = 98,317) and another published metabolomics GWAS (n = 24,925). Data on the associations of the genetic variants with BP and hypertension were obtained in the UK Biobank participants without metabolomic data (n = 286,390). The causal effects were estimated using inverse-variance weighted method. RESULTS Significant evidence consistently supported the causal effects of increased branched-chain amino acids (BCAAs, i.e., leucine, isoleucine, and valine) levels on higher BP and risk of hypertension (all P < 0.006 after Bonferroni correction except for Pleucine-on-diastolicBP = 0.008). For example, per standard deviation higher of genetically predicted isoleucine levels were associated with 2.71 ± 0.78 mmHg higher systolic BP and 1.24 ± 0.34 mmHg higher diastolic BP, as well as with 7% higher risk of hypertension (odds ratio: 1.07, [95% CI: 1.04-1.10]). In addition, per standard deviation higher of genetically predicted glycine level was associated with lower systolic BP (- 0.70 ± 0.17 mmHg, P = 4.04 × 10-5) and a lower risk of hypertension (0.99 [0.98-0.99], P = 6.46 × 10-5). In the reverse direction, genetically predicted higher systolic BP was associated with lower circulating levels of glycine (- 0.025±0.008, P = 0.001). CONCLUSIONS This study provides evidence for causal impacts of genetically predicted circulating BCAAs and glycine levels on BP. Meanwhile, genetically predicted higher BP was associated with lower glycine levels. Further investigations are warranted to clarify the underlying mechanisms.
Collapse
Affiliation(s)
- Chenhao Lin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China.,Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhonghan Sun
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China
| | - Zhendong Mei
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China
| | - Hailuan Zeng
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan Institute for Metabolic Diseases, and Human Phenome Institute, Fudan University, Shanghai, China
| | - Manying Zhao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China
| | - Jianying Hu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China
| | - Mingfeng Xia
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan Institute for Metabolic Diseases, and Human Phenome Institute, Fudan University, Shanghai, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xin Gao
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan Institute for Metabolic Diseases, and Human Phenome Institute, Fudan University, Shanghai, China
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200433, China. .,Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China.
| |
Collapse
|
208
|
Hujoel MLA, Sherman MA, Barton AR, Mukamel RE, Sankaran VG, Terao C, Loh PR. Influences of rare copy-number variation on human complex traits. Cell 2022; 185:4233-4248.e27. [PMID: 36306736 PMCID: PMC9800003 DOI: 10.1016/j.cell.2022.09.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/22/2022] [Accepted: 09/19/2022] [Indexed: 11/06/2022]
Abstract
The human genome contains hundreds of thousands of regions harboring copy-number variants (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs have been ascertainable from SNP-array data generated by large biobanks. We developed a computational approach leveraging haplotype sharing in biobank cohorts to more sensitively detect CNVs. Applied to UK Biobank, this approach accounted for approximately half of all rare gene inactivation events produced by genomic structural variation. This CNV call set enabled a detailed analysis of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (p < 5 × 10-8) likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling insights into dosage sensitivity of these genes and uncovering several gene-trait relationships. These results demonstrate the ability of haplotype-informed analysis to provide insights into the genetic basis of human complex traits.
Collapse
Affiliation(s)
- Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Maxwell A Sherman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alison R Barton
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
| | - Ronen E Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan; Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
209
|
Mompeo O, Freidin MB, Gibson R, Hysi PG, Christofidou P, Segal E, Valdes AM, Spector TD, Menni C, Mangino M. Genome-Wide Association Analysis of Over 170,000 Individuals from the UK Biobank Identifies Seven Loci Associated with Dietary Approaches to Stop Hypertension (DASH) Diet. Nutrients 2022; 14:4431. [PMID: 36297114 PMCID: PMC9611599 DOI: 10.3390/nu14204431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/17/2022] [Accepted: 10/17/2022] [Indexed: 01/24/2023] Open
Abstract
Diet is a modifiable risk factor for common chronic diseases and mental health disorders, and its effects are under partial genetic control. To estimate the impact of diet on individual health, most epidemiological and genetic studies have focused on individual aspects of dietary intake. However, analysing individual food groups in isolation does not capture the complexity of the whole diet pattern. Dietary indices enable a holistic estimation of diet and account for the intercorrelations between food and nutrients. In this study we performed the first ever genome-wide association study (GWA) including 173,701 individuals from the UK Biobank to identify genetic variants associated with the Dietary Approaches to Stop Hypertension (DASH) diet. DASH was calculated using the 24 h-recall questionnaire collected by UK Biobank. The GWA was performed using a linear mixed model implemented in BOLT-LMM. We identified seven independent single-nucleotide polymorphisms (SNPs) associated with DASH. Significant genetic correlations were observed between DASH and several educational traits with a significant enrichment for genes involved in the AMP-dependent protein kinase (AMPK) activation that controls the appetite by regulating the signalling in the hypothalamus. The colocalization analysis implicates genes involved in body mass index (BMI)/obesity and neuroticism (ARPP21, RP11-62H7.2, MFHAS1, RHEBL1). The Mendelian randomisation analysis suggested that increased DASH score, which reflect a healthy diet style, is causal of lower glucose, and insulin levels. These findings further our knowledge of the pathways underlying the relationship between diet and health outcomes. They may have significant implications for global public health and provide future dietary recommendations for the prevention of common chronic diseases.
Collapse
Affiliation(s)
- Olatz Mompeo
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | - Maxim B. Freidin
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | - Rachel Gibson
- Department of Nutritional Sciences, King’s College London, London SE1 9NH, UK
| | - Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | - Paraskevi Christofidou
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ana M. Valdes
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
- Academic Rheumatology Clinical Sciences Building, Nottingham City Hospital, University of Nottingham, Nottingham NG5 1PB, UK
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London SE1 9RT, UK
| |
Collapse
|
210
|
The HUNT study: A population-based cohort for genetic research. CELL GENOMICS 2022; 2:100193. [PMID: 36777998 PMCID: PMC9903730 DOI: 10.1016/j.xgen.2022.100193] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/10/2022] [Accepted: 09/13/2022] [Indexed: 11/06/2022]
Abstract
The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.
Collapse
|
211
|
Fang S, Holmes MV, Gaunt TR, Davey Smith G, Richardson TG. Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. eLife 2022; 11:e73951. [PMID: 36219204 PMCID: PMC9553209 DOI: 10.7554/elife.73951] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Background Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease. Methods We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank. Results As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings. Conclusions We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas. Funding This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.
Collapse
Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| |
Collapse
|
212
|
Fang S, Holmes MV, Gaunt TR, Davey Smith G, Richardson TG. Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. eLife 2022; 11. [PMID: 36219204 DOI: 10.1101/2021.10.14.21265005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/12/2022] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease. METHODS We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank. RESULTS As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings. CONCLUSIONS We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas. FUNDING This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.
Collapse
Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| |
Collapse
|
213
|
Wang W, Luo J, Willems van Dijk K, Hägg S, Grassmann F, T Hart LM, van Heemst D, Noordam R. Assessment of the bi-directional relationship between blood mitochondrial DNA copy number and type 2 diabetes mellitus: a multivariable-adjusted regression and Mendelian randomisation study. Diabetologia 2022; 65:1676-1686. [PMID: 35867128 PMCID: PMC9477915 DOI: 10.1007/s00125-022-05759-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/17/2022] [Indexed: 11/07/2022]
Abstract
AIMS/HYPOTHESIS Mitochondrial dysfunction, which can be approximated by blood mitochondrial DNA copy number (mtDNA-CN), has been implicated in the pathogenesis of type 2 diabetes mellitus. Thus far, however, insights from prospective cohort studies and Mendelian randomisation (MR) analyses on this relationship are limited. We assessed the association between blood mtDNA-CN and incident type 2 diabetes using multivariable-adjusted regression analyses, and the associations between blood mtDNA-CN and type 2 diabetes and BMI using bi-directional MR. METHODS Multivariable-adjusted Cox proportional hazard models were used to estimate the association between blood mtDNA-CN and incident type 2 diabetes in 285,967 unrelated European individuals from UK Biobank free of type 2 diabetes at baseline. Additionally, a cross-sectional analysis was performed to investigate the association between blood mtDNA-CN and BMI. We also assessed the potentially causal relationship between blood mtDNA-CN and type 2 diabetes (N=898,130 from DIAGRAM, N=215,654 from FinnGen) and BMI (N=681,275 from GIANT) using bi-directional two-sample MR. RESULTS During a median follow-up of 11.87 years, 15,111 participants developed type 2 diabetes. Participants with a higher level of blood mtDNA-CN are at lower risk of developing type 2 diabetes (HR 0.90 [95% CI 0.89, 0.92]). After additional adjustment for BMI and other confounders, these results attenuated moderately and remained present. The multivariable-adjusted cross-sectional analyses showed that higher blood mtDNA-CN was associated with lower BMI (-0.12 [95% CI -0.14, -0.10]) kg/m2. In the bi-directional MR analyses, we found no evidence for causal associations between blood mtDNA-CN and type 2 diabetes, and blood mtDNA-CN and BMI in either direction. CONCLUSIONS/INTERPRETATION The results from the present study indicate that the observed association between low blood mtDNA-CN and higher risk of type 2 diabetes is likely not causal.
Collapse
Affiliation(s)
- Wenyi Wang
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jiao Luo
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Health and Medical University, Potsdam, Germany
| | - Leen M T Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| |
Collapse
|
214
|
Benegiamo G, Bou Sleiman M, Wohlwend M, Rodríguez-López S, Goeminne LJE, Laurila PP, Klevjer M, Salonen MK, Lahti J, Jha P, Cogliati S, Enriquez JA, Brumpton BM, Bye A, Eriksson JG, Auwerx J. COX7A2L genetic variants determine cardiorespiratory fitness in mice and human. Nat Metab 2022; 4:1336-1351. [PMID: 36253618 PMCID: PMC9584823 DOI: 10.1038/s42255-022-00655-0] [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: 10/18/2021] [Accepted: 09/06/2022] [Indexed: 01/20/2023]
Abstract
Mitochondrial respiratory complexes form superassembled structures called supercomplexes. COX7A2L is a supercomplex-specific assembly factor in mammals, although its implication for supercomplex formation and cellular metabolism remains controversial. Here we identify a role for COX7A2L for mitochondrial supercomplex formation in humans. By using human cis-expression quantitative trait loci data, we highlight genetic variants in the COX7A2L gene that affect its skeletal muscle expression specifically. The most significant cis-expression quantitative trait locus is a 10-bp insertion in the COX7A2L 3' untranslated region that increases messenger RNA stability and expression. Human myotubes harboring this insertion have more supercomplexes and increased respiration. Notably, increased COX7A2L expression in the muscle is associated with lower body fat and improved cardiorespiratory fitness in humans. Accordingly, specific reconstitution of Cox7a2l expression in C57BL/6J mice leads to higher maximal oxygen consumption, increased lean mass and increased energy expenditure. Furthermore, Cox7a2l expression in mice is induced specifically in the muscle upon exercise. These findings elucidate the genetic basis of mitochondrial supercomplex formation and function in humans and show that COX7A2L plays an important role in cardiorespiratory fitness, which could have broad therapeutic implications in reducing cardiovascular mortality.
Collapse
Affiliation(s)
- Giorgia Benegiamo
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maroun Bou Sleiman
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Martin Wohlwend
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sandra Rodríguez-López
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ludger J E Goeminne
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pirkka-Pekka Laurila
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Marie Klevjer
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Cardiology, St Olav's Hospital, Trondheim, Norway
| | - Minna K Salonen
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
- Turku Institute for Advanced Studies, University of Turku, Turku, Finland
| | - Pooja Jha
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sara Cogliati
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain
- Centro de Biología Molecular Severo Ochoa (CBMSO) & Institute for Molecular Biology-IUBM, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain
- Instituto Universitario de Biología Molecular - IUBM (Universidad Autónoma de Madrid), Madrid, Spain
| | - José Antonio Enriquez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain
- Centro de Investigaciones Biomedicas en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Ben M Brumpton
- Clinic of Medicine, St Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Anja Bye
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Cardiology, St Olav's Hospital, Trondheim, Norway
| | - Johan G Eriksson
- Folkhälsan Research Center, Helsinki, Finland
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
| | - Johan Auwerx
- Laboratory of Integrative Systems Physiology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| |
Collapse
|
215
|
Caliebe A, Tekola‐Ayele F, Darst BF, Wang X, Song YE, Gui J, Sebro RA, Balding DJ, Saad M, Dubé M. Including diverse and admixed populations in genetic epidemiology research. Genet Epidemiol 2022; 46:347-371. [PMID: 35842778 PMCID: PMC9452464 DOI: 10.1002/gepi.22492] [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] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022]
Abstract
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
Collapse
Affiliation(s)
- Amke Caliebe
- Institute of Medical Informatics and StatisticsKiel University and University Hospital Schleswig‐HolsteinKielGermany
| | - Fasil Tekola‐Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMarylandUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Xuexia Wang
- Department of MathematicsUniversity of North TexasDentonTexasUSA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Jiang Gui
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth CollegeOne Medical Center Dr.LebanonNew HampshireUSA
| | | | - David J. Balding
- Melbourne Integrative Genomics, Schools of BioSciences and of Mathematics & StatisticsUniversity of MelbourneMelbourneAustralia
| | - Mohamad Saad
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
- Neuroscience Research Center, Faculty of Medical SciencesLebanese UniversityBeirutLebanon
| | - Marie‐Pierre Dubé
- Department of Medicine, and Social and Preventive MedicineUniversité de MontréalMontréalQuébecCanada
- Beaulieu‐Saucier Pharmacogenomcis CentreMontreal Heart InstituteMontrealCanada
| | | |
Collapse
|
216
|
Quantitative trait locus analysis for endophenotypes reveals genetic substrates of core symptom domains and neurocognitive function in autism spectrum disorder. Transl Psychiatry 2022; 12:407. [PMID: 36153334 PMCID: PMC9509384 DOI: 10.1038/s41398-022-02179-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 11/25/2022] Open
Abstract
Autism spectrum disorder (ASD) represents a heterogeneous group of neurodevelopmental disorders and is largely attributable to genetic risk factors. Phenotypic and genetic heterogeneity of ASD have been well-recognized; however, genetic substrates for endophenotypes that constitute phenotypic heterogeneity are not yet known. In the present study, we compiled data from the Autism Genetic Resource Exchange, which contains the demographic and detailed phenotype information of 11,961 individuals. Notably, the whole-genome sequencing data available from MSSNG and iHART for 3833 individuals in this dataset was used to perform an endophenotype-wide association study. Using a linear mixed model, genome-wide association analyses were performed for 29 endophenotype scores and 0.58 million common variants with variant allele frequency ≥ 5%. We discovered significant associations between 9 genetic variants and 6 endophenotype scores comprising neurocognitive development and severity scores for core symptoms of ASD at a significance threshold of p < 5 × 10-7. Of note, the Stereotyped Behaviors and Restricted Interests total score in Autism Diagnostic Observation Schedule Module 3 was significantly associated with multiple variants in the VPS13B gene, a causal gene for Cohen syndrome and a candidate gene for syndromic ASD. Our findings yielded loci with small effect sizes due to the moderate sample size and, thus, require validation in another cohort. Nonetheless, our endophenotype-wide association analysis extends previous candidate gene discovery in the context of genotype and endophenotype association. As a result, these candidate genes may be responsible for specific traits that constitute core symptoms and neurocognitive function of ASD rather than the disorder itself.
Collapse
|
217
|
John M, Ankenbrand MJ, Artmann C, Freudenthal JA, Korte A, Grimm DG. Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions. Bioinformatics 2022; 38:ii5-ii12. [PMID: 36124808 PMCID: PMC9486594 DOI: 10.1093/bioinformatics/btac455] [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] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) are an integral tool for studying the architecture of complex genotype and phenotype relationships. Linear mixed models (LMMs) are commonly used to detect associations between genetic markers and a trait of interest, while at the same time allowing to account for population structure and cryptic relatedness. Assumptions of LMMs include a normal distribution of the residuals and that the genetic markers are independent and identically distributed-both assumptions are often violated in real data. Permutation-based methods can help to overcome some of these limitations and provide more realistic thresholds for the discovery of true associations. Still, in practice, they are rarely implemented due to the high computational complexity. RESULTS We propose permGWAS, an efficient LMM reformulation based on 4D tensors that can provide permutation-based significance thresholds. We show that our method outperforms current state-of-the-art LMMs with respect to runtime and that permutation-based thresholds have lower false discovery rates for skewed phenotypes compared to the commonly used Bonferroni threshold. Furthermore, using permGWAS we re-analyzed more than 500 Arabidopsis thaliana phenotypes with 100 permutations each in less than 8 days on a single GPU. Our re-analyses suggest that applying a permutation-based threshold can improve and refine the interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION permGWAS is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Maura John
- To whom correspondence should be addressed. or
| | - Markus J Ankenbrand
- Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Carolin Artmann
- Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Jan A Freudenthal
- Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | | | | |
Collapse
|
218
|
Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks. Nat Commun 2022; 13:5437. [PMID: 36114182 PMCID: PMC9481565 DOI: 10.1038/s41467-022-32885-x] [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: 12/07/2021] [Accepted: 08/22/2022] [Indexed: 01/11/2023] Open
Abstract
With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.
Collapse
|
219
|
Zhuang Y, Wolford BN, Nam K, Bi W, Zhou W, Willer CJ, Mukherjee B, Lee S. Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies. Bioinformatics 2022; 38:4337-4343. [PMID: 35876838 PMCID: PMC9477535 DOI: 10.1093/bioinformatics/btac459] [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: 12/05/2021] [Revised: 05/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution. RESULTS We propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case-control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant-phenotype associations. AVAILABILITY AND IMPLEMENTATION The summary statistics and code generated in this study are available at https://github.com/styvon/TAPE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yongwen Zhuang
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Brooke N Wolford
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul, Korea
| | - Wenjian Bi
- Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Wei Zhou
- Massachusetts General Hospital, Broad Institute, Boston, MA, USA
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Michigan Institute of Data Science, University of Michigan, Ann Arbor, MI, USA
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, Korea
| |
Collapse
|
220
|
Smith GA, Padmanabhan A, Lau BH, Pampana A, Li L, Lee CY, Pelonero A, Nishino T, Sadagopan N, Xia VQ, Jain R, Natarajan P, Wu RS, Black BL, Srivastava D, Shokat KM, Chorba JS. Cold shock domain-containing protein E1 is a posttranscriptional regulator of the LDL receptor. Sci Transl Med 2022; 14:eabj8670. [PMID: 36103516 PMCID: PMC10174261 DOI: 10.1126/scitranslmed.abj8670] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The low-density lipoprotein receptor (LDLR) controls cellular delivery of cholesterol and clears LDL from the bloodstream, protecting against atherosclerotic heart disease, the leading cause of death in the United States. We therefore sought to identify regulators of the LDLR beyond the targets of current therapies and known causes of familial hypercholesterolemia. We found that cold shock domain-containing protein E1 (CSDE1) enhanced hepatic LDLR messenger RNA (mRNA) decay via its 3' untranslated region and regulated atherogenic lipoproteins in vivo. Using parallel phenotypic genome-wide CRISPR interference screens in a tissue culture model, we identified 40 specific regulators of the LDLR that were not previously identified by observational human genetic studies. Among these, we demonstrated that, in HepG2 cells, CSDE1 regulated the LDLR at least as strongly as statins and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. In addition, we showed that hepatic gene silencing of Csde1 treated diet-induced dyslipidemia in mice to a similar degree as Pcsk9 silencing. These results suggest the therapeutic potential of targeting CSDE1 to manipulate the posttranscriptional regulation of the LDLR mRNA for the prevention of cardiovascular disease. Our approach of modeling a clinically relevant phenotype in a forward genetic screen, followed by mechanistic pharmacologic dissection and in vivo validation, may serve as a generalizable template for the identification of therapeutic targets in other human disease states.
Collapse
Affiliation(s)
- Geoffrey A Smith
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Arun Padmanabhan
- Division of Cardiology, UCSF Health, San Francisco, CA 94143, USA.,Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA
| | - Bryan H Lau
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Akhil Pampana
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Li Li
- Department of Medicine and Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Clara Y Lee
- Division of Cardiology, UCSF Health, San Francisco, CA 94143, USA.,Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA
| | - Angelo Pelonero
- Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA
| | - Tomohiro Nishino
- Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA
| | - Nandhini Sadagopan
- Division of Cardiology, UCSF Health, San Francisco, CA 94143, USA.,Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA
| | - Vivian Q Xia
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Division of Cardiology, Zuckerberg San Francisco General Hospital, San Francisco, CA 94110, USA
| | - Rajan Jain
- Department of Medicine and Penn Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Cell and Developmental Biology, Institute of Regenerative Medicine, and Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.,Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Roland S Wu
- Division of Cardiology, UCSF Health, San Francisco, CA 94143, USA.,Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Brian L Black
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Deepak Srivastava
- Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA.,Departments of Pediatrics and Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA.,Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA 94158, USA
| | - Kevan M Shokat
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA.,Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - John S Chorba
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.,Division of Cardiology, Zuckerberg San Francisco General Hospital, San Francisco, CA 94110, USA
| |
Collapse
|
221
|
Genetic risk factors have a substantial impact on healthy life years. Nat Med 2022; 28:1893-1901. [PMID: 36097220 PMCID: PMC9499866 DOI: 10.1038/s41591-022-01957-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 07/19/2022] [Indexed: 12/15/2022]
Abstract
The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; 'lost healthy life years'). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 (LPA) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.
Collapse
|
222
|
Smith CJ, Sinnott-Armstrong N, Cichońska A, Julkunen H, Fauman EB, Würtz P, Pritchard JK. Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation. eLife 2022; 11:e79348. [PMID: 36073519 PMCID: PMC9536840 DOI: 10.7554/elife.79348] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/06/2022] [Indexed: 11/15/2022] Open
Abstract
Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.
Collapse
Affiliation(s)
- Courtney J Smith
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
| | - Nasa Sinnott-Armstrong
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
- Herbold Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | | | | | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and MedicalCambridgeUnited States
| | | | - Jonathan K Pritchard
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
| |
Collapse
|
223
|
Bell S, Tozer DJ, Markus HS. Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. Sci Rep 2022; 12:14938. [PMID: 36056064 PMCID: PMC9440133 DOI: 10.1038/s41598-022-19106-7] [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: 03/10/2022] [Accepted: 08/24/2022] [Indexed: 11/23/2022] Open
Abstract
Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.
Collapse
Affiliation(s)
- Steven Bell
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - Daniel J Tozer
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| |
Collapse
|
224
|
Brain-specific genes contribute to chronic but not to acute back pain. Pain Rep 2022; 7:e1018. [PMID: 35975136 PMCID: PMC9371560 DOI: 10.1097/pr9.0000000000001018] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/20/2022] [Accepted: 05/20/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Back pain is the leading cause of disability worldwide. Although most back pain cases are acute, 20% of acute pain patients experience chronic back pain symptoms. It is unclear whether acute pain and chronic pain have similar or distinct underlying genetic mechanisms. Objectives To characterize the molecular and cellular pathways contributing to acute and chronic pain states. Methods Cross-sectional observational genome-wide association study. Results A total of 375,158 individuals from the UK Biobank cohort were included in the discovery of genome-wide association study. Of those, 70,633 (19%) and 32,209 (9%) individuals met the definition of chronic and acute back pain, respectively. A total of 355 single nucleotide polymorphism grouped into 13 loci reached the genome-wide significance threshold (5x10-8) for chronic back pain, but none for acute. Of these, 7 loci were replicated in the Nord-Trøndelag Health Study (HUNT) cohort (19,760 chronic low back pain cases and 28,674 pain-free controls). Single nucleotide polymorphism heritability was 4.6% (P=1.4x10-78) for chronic back pain and 0.81% (P=1.4x10-8) for acute back pain. Similar differences in heritability estimates between acute and chronic back pain were found in the HUNT cohort: 3.4% (P=0.0011) and 0.6% (P=0.851), respectively. Pathway analyses, tissue-specific heritability enrichment analyses, and epigenetic characterization suggest a substantial genetic contribution to chronic but not acute back pain from the loci predominantly expressed in the central nervous system. Conclusion Chronic back pain is substantially more heritable than acute back pain. This heritability is mostly attributed to genes expressed in the brain.
Collapse
|
225
|
Zhao Y, Gardner EJ, Tuke MA, Zhang H, Pietzner M, Koprulu M, Jia RY, Ruth KS, Wood AR, Beaumont RN, Tyrrell J, Jones SE, Lango Allen H, Day FR, Langenberg C, Frayling TM, Weedon MN, Perry JRB, Ong KK, Murray A. Detection and characterization of male sex chromosome abnormalities in the UK Biobank study. Genet Med 2022; 24:1909-1919. [PMID: 35687092 DOI: 10.1016/j.gim.2022.05.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 11/21/2022] Open
Abstract
PURPOSE The study aimed to systematically ascertain male sex chromosome abnormalities, 47,XXY (Klinefelter syndrome [KS]) and 47,XYY, and characterize their risks of adverse health outcomes. METHODS We analyzed genotyping array or exome sequence data in 207,067 men of European ancestry aged 40 to 70 years from the UK Biobank and related these to extensive routine health record data. RESULTS Only 49 of 213 (23%) of men whom we identified with KS and only 1 of 143 (0.7%) with 47,XYY had a diagnosis of abnormal karyotype on their medical records or self-report. We observed expected associations for KS with reproductive dysfunction (late puberty: risk ratio [RR] = 2.7; childlessness: RR = 4.2; testosterone concentration: RR = -3.8 nmol/L, all P < 2 × 10-8), whereas XYY men appeared to have normal reproductive function. Despite this difference, we identified several higher disease risks shared across both KS and 47,XYY, including type 2 diabetes (RR = 3.0 and 2.6, respectively), venous thrombosis (RR = 6.4 and 7.4, respectively), pulmonary embolism (RR = 3.3 and 3.7, respectively), and chronic obstructive pulmonary disease (RR = 4.4 and 4.6, respectively) (all P < 7 × 10-6). CONCLUSION KS and 47,XYY were mostly unrecognized but conferred substantially higher risks for metabolic, vascular, and respiratory diseases, which were only partially explained by higher levels of body mass index, deprivation, and smoking.
Collapse
Affiliation(s)
- Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Marcus A Tuke
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Huairen Zhang
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom; Computational Medicine, Berlin Institute of Health (BIH) at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Mine Koprulu
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Raina Y Jia
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Katherine S Ruth
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Samuel E Jones
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Hana Lango Allen
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom; Computational Medicine, Berlin Institute of Health (BIH) at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, Cambridge University, Cambridge, United Kingdom.
| | - Anna Murray
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Royal Devon & Exeter Hospital, Exeter, United Kingdom.
| |
Collapse
|
226
|
Young WJ, Lahrouchi N, Isaacs A, Duong T, Foco L, Ahmed F, Brody JA, Salman R, Noordam R, Benjamins JW, Haessler J, Lyytikäinen LP, Repetto L, Concas MP, van den Berg ME, Weiss S, Baldassari AR, Bartz TM, Cook JP, Evans DS, Freudling R, Hines O, Isaksen JL, Lin H, Mei H, Moscati A, Müller-Nurasyid M, Nursyifa C, Qian Y, Richmond A, Roselli C, Ryan KA, Tarazona-Santos E, Thériault S, van Duijvenboden S, Warren HR, Yao J, Raza D, Aeschbacher S, Ahlberg G, Alonso A, Andreasen L, Bis JC, Boerwinkle E, Campbell A, Catamo E, Cocca M, Cutler MJ, Darbar D, De Grandi A, De Luca A, Ding J, Ellervik C, Ellinor PT, Felix SB, Froguel P, Fuchsberger C, Gögele M, Graff C, Graff M, Guo X, Hansen T, Heckbert SR, Huang PL, Huikuri HV, Hutri-Kähönen N, Ikram MA, Jackson RD, Junttila J, Kavousi M, Kors JA, Leal TP, Lemaitre RN, Lin HJ, Lind L, Linneberg A, Liu S, MacFarlane PW, Mangino M, Meitinger T, Mezzavilla M, Mishra PP, Mitchell RN, Mononen N, Montasser ME, Morrison AC, Nauck M, Nauffal V, Navarro P, Nikus K, Pare G, Patton KK, Pelliccione G, Pittman A, Porteous DJ, Pramstaller PP, Preuss MH, Raitakari OT, Reiner AP, Ribeiro ALP, Rice KM, Risch L, Schlessinger D, Schotten U, Schurmann C, Shen X, Shoemaker MB, Sinagra G, Sinner MF, Soliman EZ, Stoll M, Strauch K, Tarasov K, Taylor KD, Tinker A, Trompet S, Uitterlinden A, Völker U, Völzke H, Waldenberger M, Weng LC, Whitsel EA, Wilson JG, Avery CL, Conen D, Correa A, Cucca F, Dörr M, Gharib SA, Girotto G, Grarup N, Hayward C, Jamshidi Y, Järvelin MR, Jukema JW, Kääb S, Kähönen M, Kanters JK, Kooperberg C, Lehtimäki T, Lima-Costa MF, Liu Y, Loos RJF, Lubitz SA, Mook-Kanamori DO, Morris AP, O'Connell JR, Olesen MS, Orini M, Padmanabhan S, Pattaro C, Peters A, Psaty BM, Rotter JI, Stricker B, van der Harst P, van Duijn CM, Verweij N, Wilson JF, Arking DE, Ramirez J, Lambiase PD, Sotoodehnia N, Mifsud B, Newton-Cheh C, Munroe PB. Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways. Nat Commun 2022; 13:5144. [PMID: 36050321 PMCID: PMC9436946 DOI: 10.1038/s41467-022-32821-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 08/17/2022] [Indexed: 11/10/2022] Open
Abstract
The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.
Collapse
Affiliation(s)
- William J Young
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS trust, London, UK
| | - Najim Lahrouchi
- Amsterdam UMC, University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Aaron Isaacs
- Deptartment of Physiology, Cardiovascular Research Institute Maastricht CARIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Center for Systems Biology MaCSBio, Maastricht University, Maastricht, The Netherlands
| | - ThuyVy Duong
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luisa Foco
- Eurac Research, Institute for Biomedicine affiliated with the University of Lübeck, Bolzano, Italy
| | - Farah Ahmed
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Reem Salman
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
| | - Raymond Noordam
- Department of Internal Medicine, section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan-Walter Benjamins
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - Jeffrey Haessler
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 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 Health Technology, Tampere University, Tampere, Finland
| | - Linda Repetto
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Maria Pina Concas
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
| | - Marten E van den Berg
- Department of Epidemiology, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Stefan Weiss
- DZHK German Centre for Cardiovascular Research; partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics; Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Antoine R Baldassari
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Biostatistics and Medicine, University of Washington, Seattle, WA, USA
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Daniel S Evans
- California Pacific Medical Center, Research Institute, San Francisco, CA, USA
| | - Rebecca Freudling
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Oliver Hines
- Genetics Research Centre, St George's University of London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Jonas L Isaksen
- Laboratory of Experimental Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Honghuang Lin
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Hao Mei
- Department of Data Science, University of Mississippi Medical Center, Jackson, USA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics IMBEI, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Casia Nursyifa
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yong Qian
- Translational Gerontology Branch, National Institute on Aging, National Institute of Health, Baltimore, US
| | - Anne Richmond
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Carolina Roselli
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Eduardo Tarazona-Santos
- Department of Genetics, Ecology and Evolution, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte/Minas Gerais, Brazil
| | - Sébastien Thériault
- Population Health Research Institute, McMaster University, Hamilton, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Quebec, Canada
| | - Stefan van Duijvenboden
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- Institute of Cardiovascular Sciences, University of College London, London, UK
| | - Helen R Warren
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences/The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Dania Raza
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- Brighton and Sussex Medical School, Brighton, UK
| | - Stefanie Aeschbacher
- Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gustav Ahlberg
- Laboratory for Molecular Cardiology, The Heart Centre, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Laura Andreasen
- Laboratory for Molecular Cardiology, The Heart Centre, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Archie Campbell
- Usher Institute, University of Edinburgh, Nine, Edinburgh Bioquarter, 9 Little France Road, Edinburgh, UK
- Health Data Research UK, University of Edinburgh, Nine, Edinburgh Bioquarter, 9 Little France Road, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Eulalia Catamo
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
| | - Massimiliano Cocca
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
| | - Michael J Cutler
- Intermountain Heart Institute, Intermountain Medical Center, Murray, UT, USA
| | - Dawood Darbar
- Department of Medicine, University of Illinois at Chicago, Chicago, USA
| | - Alessandro De Grandi
- Eurac Research, Institute for Biomedicine affiliated with the University of Lübeck, Bolzano, Italy
| | - Antonio De Luca
- Cardiothoracovascular Department, ASUGI, University of Trieste, Trieste, Italy
| | - Jun Ding
- Translational Gerontology Branch, National Institute on Aging, National Institute of Health, Baltimore, US
| | - Christina Ellervik
- Department of Data and Data Support, Region Zealand, 4180, Sorø, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
- Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Stephan B Felix
- DZHK German Centre for Cardiovascular Research; partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B - Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine; University Medicine Greifswald, Greifswald, Germany
| | - Philippe Froguel
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
| | - Christian Fuchsberger
- Eurac Research, Institute for Biomedicine affiliated with the University of Lübeck, Bolzano, Italy
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, USA
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine affiliated with the University of Lübeck, Bolzano, Italy
| | - Claus Graff
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences/The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology/University of Washington, Seattle, WA, USA
| | - Paul L Huang
- Cardiology Division and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Heikki V Huikuri
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and University Hospital of Oulu, Oulu, Finland
| | - Nina Hutri-Kähönen
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland
- Department of Pediatrics, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Tampere Centre for Skills Training and Simulation, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Rebecca D Jackson
- Center for Clinical and Translational Science, Ohio State Medical Center, Columbus, OH, USA
| | - Juhani Junttila
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and University Hospital of Oulu, Oulu, Finland
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, NL, The Netherlands
| | - Thiago P Leal
- Department of Genetics, Ecology and Evolution, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte/Minas Gerais, Brazil
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Henry J Lin
- Institute for Translational Genomics and Population Sciences/The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Lars Lind
- Deptartment of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simin Liu
- Center for Global Cardiometabolic Health, Departments of Epidemiology, Medicine and Surgery, Brown University, Providence, USA
| | - Peter W MacFarlane
- Institute of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Thomas Meitinger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research, partner site: Munich Heart Alliance, Munich, Germany
| | - Massimo Mezzavilla
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
| | - Pashupati P Mishra
- 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
| | - Rebecca N Mitchell
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nina Mononen
- 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
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Matthias Nauck
- DZHK German Centre for Cardiovascular Research; partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Victor Nauffal
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - 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
| | - Guillaume Pare
- Population Health Research Institute, McMaster University, Hamilton, Canada
| | - Kristen K Patton
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Giulia Pelliccione
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
| | - Alan Pittman
- Genetics Research Centre, St George's University of London, London, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Peter P Pramstaller
- Eurac Research, Institute for Biomedicine affiliated with the University of Lübeck, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- 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
| | - Alexander P Reiner
- Department of Epidemiology/University of Washington, Seattle, WA, USA
- Fred Hutchinson Cancer Center, University of Washington, Seattle, WA, USA
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Brazil, Belo Horizonte, Minas Gerais, Brazil
- Cardiology Service and Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, Belo Horizonte, Minas Gerais, Brazil
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Lorenz Risch
- Labormedizinisches zentrum Dr. Risch, Vaduz, Liechtenstein
- Faculty of Medical Sciences, Private University in the Principality of Liechtenstein, Triesen, Liechtenstein
- Center of Laboratory Medicine, University Institute of Clinical Chemistry, University of Bern, Inselspital, Bern, Switzerland
| | - David Schlessinger
- Laboratory of Genetics and Genomics, National Institute on Aging, National Institute of Health, Baltimore, US
| | - Ulrich Schotten
- Deptartment of Physiology, Cardiovascular Research Institute Maastricht CARIM, Maastricht University, Maastricht, The Netherlands
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xia Shen
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Greater Bay Area Institute of Precision Medicine Guangzhou, Fudan University, Nansha District, Guangzhou, China
| | - M Benjamin Shoemaker
- Department of Medicine, Division of Cardiovascular Medicine, Arrhythmia Section, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gianfranco Sinagra
- Cardiothoracovascular Department, ASUGI, University of Trieste, Trieste, Italy
| | - Moritz F Sinner
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research, partner site: Munich Heart Alliance, Munich, Germany
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center EPICARE, Wake Forest School of Medicine, Winston Salem, USA
| | - Monika Stoll
- Maastricht Center for Systems Biology MaCSBio, Maastricht University, Maastricht, The Netherlands
- Dept. of Biochemistry, Cardiovascular Research Institute Maastricht CARIM, Maastricht University, Maastricht, NL, The Netherlands
- Institute of Human Genetics, Genetic Epidemiology, University of Muenster, Muenster, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics IMBEI, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Kirill Tarasov
- Laboratory of Cardiovascular Sciences, National Institute on Aging, National Institute of Health, Baltimore, US
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences/The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Andrew Tinker
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Stella Trompet
- Department of Internal Medicine, section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Uwe Völker
- DZHK German Centre for Cardiovascular Research; partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics; Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- DZHK German Centre for Cardiovascular Research; partner site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Melanie Waldenberger
- DZHK (German Centre for Cardiovascular Research, partner site: Munich Heart Alliance, Munich, Germany
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, USA
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, USA
| | - Christy L Avery
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - David Conen
- Population Health Research Institute, McMaster University, Hamilton, Canada
| | - Adolfo Correa
- Departments of Medicine, Pediatrics and Population Health Science, University of Mississippi Medical Center, Jackson, USA
| | - Francesco Cucca
- Institute of Genetic and Biomedical Rsearch, Italian National Research Council, Monserrato, Italy
| | - Marcus Dörr
- DZHK German Centre for Cardiovascular Research; partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B - Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine; University Medicine Greifswald, Greifswald, Germany
| | - Sina A Gharib
- Center for Lung Biology, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - Giorgia Girotto
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Yalda Jamshidi
- Genetics Research Centre, St George's University of London, London, UK
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Stefan Kääb
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research, partner site: Munich Heart Alliance, Munich, 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
| | - Jørgen K Kanters
- Laboratory of Experimental Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Charles Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - 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
| | | | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Ruth J F Loos
- The 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
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - 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
| | - Andrew P Morris
- Department of Health Data Science, University of Liverpool, Liverpool, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Jeffrey R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Michele Orini
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS trust, London, UK
- Institute of Cardiovascular Sciences, University of College London, London, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine affiliated with the University of Lübeck, Bolzano, Italy
| | - Annette Peters
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research, partner site: Munich Heart Alliance, Munich, Germany
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology/University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences/The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics/Harbor-UCLA Medical Center, Torrance, CA, USA
- Departments of Pediatrics and Human Genetics/David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bruno Stricker
- Department of Epidemiology, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Pim van der Harst
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
- Department of Cardiology, Heart and Lung Division, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Niek Verweij
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julia Ramirez
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- Institute of Cardiovascular Sciences, University of College London, London, UK
| | - Pier D Lambiase
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS trust, London, UK
- Institute of Cardiovascular Sciences, University of College London, London, UK
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Borbala Mifsud
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK
- Genomics and Translational Biomedicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Christopher Newton-Cheh
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Cardiovascular Research Center, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - Patricia B Munroe
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London, UK.
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| |
Collapse
|
227
|
Klevjer M, Nordeidet AN, Hansen AF, Madssen E, Wisløff U, Brumpton BM, Bye A. Genome-Wide Association Study Identifies New Genetic Determinants of Cardiorespiratory Fitness: The Trøndelag Health Study. Med Sci Sports Exerc 2022; 54:1534-1545. [PMID: 35482759 DOI: 10.1249/mss.0000000000002951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE Low cardiorespiratory fitness (CRF) is a major risk factor for cardiovascular disease (CVD) and a stronger predictor of CVD morbidity and mortality than established risk factors. The genetic component of CRF, quantified as peak oxygen uptake (V̇O 2peak ), is estimated to be ~60%. Unfortunately, current studies on genetic markers for CRF have been limited by small sample sizes and using estimated CRF. To overcome these limitations, we performed a large-scale systematic screening for genetic variants associated with V̇O 2peak . METHODS A genome-wide association study was performed with BOLT-LMM including directly measured V̇O 2peak from 4525 participants in the HUNT3 Fitness study and 14 million single-nucleotide polymorphisms (SNP). For validation, similar analyses were performed in the United Kingdom Biobank (UKB), where CRF was assessed through a submaximal bicycle test, including ~60,000 participants and ~60 million SNP. Functional mapping and annotation of the genome-wide association study results was conducted using FUMA. RESULTS In HUNT, two genome-wide significant SNP associated with V̇O 2peak were identified in the total population, two in males, and 35 in females. Two SNP in the female population showed nominally significant association in the UKB. One of the replicated SNP is located in PIK3R5 , shown to be of importance for cardiac function and CVD. Bioinformatic analyses of the total and male population revealed candidate SNP in PPP3CA , previously associated with CRF. CONCLUSIONS We identified 38 novel SNP associated with V̇O 2peak in HUNT. Two SNP were nominally replicated in UKB. Several interesting genes emerged from the functional analyses, among them one previously reported to be associated with CVD and another with CRF.
Collapse
Affiliation(s)
| | - Ada N Nordeidet
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, NORWAY
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, NORWAY
| | | | | | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, NORWAY
| | | |
Collapse
|
228
|
Harlow CE, Gandawijaya J, Bamford RA, Martin ER, Wood AR, van der Most PJ, Tanaka T, Leonard HL, Etheridge AS, Innocenti F, Beaumont RN, Tyrrell J, Nalls MA, Simonsick EM, Garimella PS, Shiroma EJ, Verweij N, van der Meer P, Gansevoort RT, Snieder H, Gallins PJ, Jima DD, Wright F, Zhou YH, Ferrucci L, Bandinelli S, Hernandez DG, van der Harst P, Patel VV, Waterworth DM, Chu AY, Oguro-Ando A, Frayling TM. Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies. Am J Hum Genet 2022; 109:1638-1652. [PMID: 36055212 PMCID: PMC9502050 DOI: 10.1016/j.ajhg.2022.08.004] [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: 04/14/2022] [Accepted: 08/03/2022] [Indexed: 11/30/2022] Open
Abstract
Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD.
Collapse
Affiliation(s)
- Charli E Harlow
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Josan Gandawijaya
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Rosemary A Bamford
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Emily-Rose Martin
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Andrew R Wood
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Peter J van der Most
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen 9713, the Netherlands
| | - Toshiko Tanaka
- Longitudinal Studies Section, Translation Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Hampton L Leonard
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA; Data Tecnica International, Glen Echo, MD 20812, USA; Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD 20892, USA
| | - Amy S Etheridge
- Eshelman School of Pharmacy and Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, USA
| | | | - Robin N Beaumont
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Jessica Tyrrell
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA; Data Tecnica International, Glen Echo, MD 20812, USA; Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eleanor M Simonsick
- Longitudinal Studies Section, Translation Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Pranav S Garimella
- Division of Nephrology-Hypertension, University of California San Diego, San Diego, CA, USA
| | - Eric J Shiroma
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, MD 20892, USA
| | - Niek Verweij
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen 9713, the Netherlands
| | - Peter van der Meer
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen 9713, the Netherlands
| | - Ron T Gansevoort
- University of Groningen, University Medical Center Groningen, Department of Nephrology, Groningen 9713, the Netherlands
| | - Harold Snieder
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen 9713, the Netherlands
| | - Paul J Gallins
- Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, NC 27695, USA
| | - Dereje D Jima
- Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, NC 27695, USA; Center for Human Health and the Environment, North Carolina State University, Raleigh, NC 27606, USA
| | - Fred Wright
- Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, NC 27695, USA
| | - Yi-Hui Zhou
- Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, NC 27695, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translation Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | | | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht 3584, the Netherlands
| | | | | | | | - Asami Oguro-Ando
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK.
| | - Timothy M Frayling
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter EX2 5DW, UK.
| |
Collapse
|
229
|
Xiu X, Zhang H, Xue A, Cooper DN, Yan L, Yang Y, Yang Y, Zhao H. Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians. BMC Med 2022; 20:300. [PMID: 36042491 PMCID: PMC9429730 DOI: 10.1186/s12916-022-02476-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Observational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups. METHODS By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: Ncase = 21,926, Ncontrol = 342,747; East Asian-based: Ncase = 36,614, Ncontrol = 155,150) and PAD (European-based: Ncase = 5673, Ncontrol = 359,551; East Asian-based: Ncase = 3593, Ncontrol = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD. RESULTS We observed a strong genetic correlation (rg) between T2D and PAD in both Europeans (rg = 0.51; p-value = 9.34 × 10-15) and East Asians (rg = 0.46; p-value = 1.67 × 10-12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians. CONCLUSIONS Our study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.
Collapse
Affiliation(s)
- Xuehao Xiu
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Haoyang Zhang
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.,School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510000, China
| | - Angli Xue
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Li Yan
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510000, China.
| | - Yuanhao Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. .,Mater Research Institute, Translational Research Institute, Brisbane, QLD, Australia.
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.
| |
Collapse
|
230
|
O'Sullivan JW, Raghavan S, Marquez-Luna C, Luzum JA, Damrauer SM, Ashley EA, O'Donnell CJ, Willer CJ, Natarajan P. Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 2022; 146:e93-e118. [PMID: 35862132 PMCID: PMC9847481 DOI: 10.1161/cir.0000000000001077] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.
Collapse
|
231
|
Korologou-Linden R, Bhatta L, Brumpton BM, Howe LD, Millard LAC, Kolaric K, Ben-Shlomo Y, Williams DM, Smith GD, Anderson EL, Stergiakouli E, Davies NM. The causes and consequences of Alzheimer's disease: phenome-wide evidence from Mendelian randomization. Nat Commun 2022; 13:4726. [PMID: 35953482 PMCID: PMC9372151 DOI: 10.1038/s41467-022-32183-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 07/20/2022] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) has no proven causal and modifiable risk factors, or effective interventions. We report a phenome-wide association study (PheWAS) of genetic liability for AD in 334,968 participants of the UK Biobank study, stratified by age. We also examined the effects of AD genetic liability on previously implicated risk factors. We replicated these analyses in the HUNT study. PheWAS hits and previously implicated risk factors were followed up in a Mendelian randomization (MR) framework to identify the causal effect of each risk factor on AD risk. A higher genetic liability for AD was associated with medical history and cognitive, lifestyle, physical and blood-based measures as early as 39 years of age. These effects were largely driven by the APOE gene. The follow-up MR analyses were primarily null, implying that most of these associations are likely to be a consequence of prodromal disease or selection bias, rather than the risk factor causing the disease.
Collapse
Affiliation(s)
- Roxanna Korologou-Linden
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Louise A C Millard
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK
| | - Katarina Kolaric
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Emma L Anderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
232
|
Genome-wide associations of aortic distensibility suggest causality for aortic aneurysms and brain white matter hyperintensities. Nat Commun 2022; 13:4505. [PMID: 35922433 PMCID: PMC9349177 DOI: 10.1038/s41467-022-32219-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 07/20/2022] [Indexed: 12/13/2022] Open
Abstract
Aortic dimensions and distensibility are key risk factors for aortic aneurysms and dissections, as well as for other cardiovascular and cerebrovascular diseases. We present genome-wide associations of ascending and descending aortic distensibility and area derived from cardiac magnetic resonance imaging (MRI) data of up to 32,590 Caucasian individuals in UK Biobank. We identify 102 loci (including 27 novel associations) tagging genes related to cardiovascular development, extracellular matrix production, smooth muscle cell contraction and heritable aortic diseases. Functional analyses highlight four signalling pathways associated with aortic distensibility (TGF-β, IGF, VEGF and PDGF). We identify distinct sex-specific associations with aortic traits. We develop co-expression networks associated with aortic traits and apply phenome-wide Mendelian randomization (MR-PheWAS), generating evidence for a causal role for aortic distensibility in development of aortic aneurysms. Multivariable MR suggests a causal relationship between aortic distensibility and cerebral white matter hyperintensities, mechanistically linking aortic traits and brain small vessel disease.
Collapse
|
233
|
Abstract
Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency-linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated [Formula: see text]. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average [Formula: see text] value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.
Collapse
|
234
|
Kar SP, Quiros PM, Gu M, Jiang T, Mitchell J, Langdon R, Iyer V, Barcena C, Vijayabaskar MS, Fabre MA, Carter P, Petrovski S, Burgess S, Vassiliou GS. Genome-wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis. Nat Genet 2022; 54:1155-1166. [PMID: 35835912 PMCID: PMC9355874 DOI: 10.1038/s41588-022-01121-z] [Citation(s) in RCA: 104] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 06/06/2022] [Indexed: 12/14/2022]
Abstract
Clonal hematopoiesis (CH), the clonal expansion of a blood stem cell and its progeny driven by somatic driver mutations, affects over a third of people, yet remains poorly understood. Here we analyze genetic data from 200,453 UK Biobank participants to map the landscape of inherited predisposition to CH, increasing the number of germline associations with CH in European-ancestry populations from 4 to 14. Genes at new loci implicate DNA damage repair (PARP1, ATM, CHEK2), hematopoietic stem cell migration/homing (CD164) and myeloid oncogenesis (SETBP1). Several associations were CH-subtype-specific including variants at TCL1A and CD164 that had opposite associations with DNMT3A- versus TET2-mutant CH, the two most common CH subtypes, proposing key roles for these two loci in CH development. Mendelian randomization analyses showed that smoking and longer leukocyte telomere length are causal risk factors for CH and that genetic predisposition to CH increases risks of myeloproliferative neoplasia, nonhematological malignancies, atrial fibrillation and blood epigenetic ageing.
Collapse
Affiliation(s)
- Siddhartha P Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Section of Translational Epidemiology, Division of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Pedro M Quiros
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.
| | - Muxin Gu
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tao Jiang
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Jonathan Mitchell
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ryan Langdon
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Section of Translational Epidemiology, Division of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Vivek Iyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Clea Barcena
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - M S Vijayabaskar
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Margarete A Fabre
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Haematology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Paul Carter
- Division of Cardiovascular Medicine, Department of Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - George S Vassiliou
- Department of Haematology, Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
- Department of Haematology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| |
Collapse
|
235
|
Cuomo ASE, Heinen T, Vagiaki D, Horta D, Marioni JC, Stegle O. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol Syst Biol 2022; 18:e10663. [PMID: 35972065 PMCID: PMC9380406 DOI: 10.15252/msb.202110663] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/11/2022] Open
Abstract
Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.
Collapse
Affiliation(s)
- Anna S E Cuomo
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.,Wellcome Sanger Institute, Cambridge, UK
| | - Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Genome Biology, Heidelberg, Germany.,Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Danai Vagiaki
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Genome Biology, Heidelberg, Germany.,Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Danilo Horta
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - John C Marioni
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.,Wellcome Sanger Institute, Cambridge, UK.,Cancer Research UK, Cambridge Institute, Cambridge, UK
| | - Oliver Stegle
- European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.,Wellcome Sanger Institute, Cambridge, UK.,Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Genome Biology, Heidelberg, Germany
| |
Collapse
|
236
|
Kuo FC, Neville MJ, Sabaratnam R, Wesolowska-Andersen A, Phillips D, Wittemans LBL, van Dam AD, Loh NY, Todorčević M, Denton N, Kentistou KA, Joshi PK, Christodoulides C, Langenberg C, Collas P, Karpe F, Pinnick KE. HOTAIR interacts with PRC2 complex regulating the regional preadipocyte transcriptome and human fat distribution. Cell Rep 2022; 40:111136. [PMID: 35905723 PMCID: PMC10073411 DOI: 10.1016/j.celrep.2022.111136] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/06/2022] [Accepted: 07/01/2022] [Indexed: 12/12/2022] Open
Abstract
Mechanisms governing regional human adipose tissue (AT) development remain undefined. Here, we show that the long non-coding RNA HOTAIR (HOX transcript antisense RNA) is exclusively expressed in gluteofemoral AT, where it is essential for adipocyte development. We find that HOTAIR interacts with polycomb repressive complex 2 (PRC2) and we identify core HOTAIR-PRC2 target genes involved in adipocyte lineage determination. Repression of target genes coincides with PRC2 promoter occupancy and H3K27 trimethylation. HOTAIR is also involved in modifying the gluteal adipocyte transcriptome through alternative splicing. Gluteal-specific expression of HOTAIR is maintained by defined regions of open chromatin across the HOTAIR promoter. HOTAIR expression levels can be modified by hormonal (estrogen, glucocorticoids) and genetic variation (rs1443512 is a HOTAIR eQTL associated with reduced gynoid fat mass). These data identify HOTAIR as a dynamic regulator of the gluteal adipocyte transcriptome and epigenome with functional importance for human regional AT development.
Collapse
Affiliation(s)
- Feng-Chih Kuo
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defence Medical Centre, Taipei, Taiwan
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, OUH Foundation Trust, Oxford, UK
| | - Rugivan Sabaratnam
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; Institute of Clinical Research, University of Southern Denmark, 5000 Odense C, Denmark; Steno Diabetes Center Odense, Odense University Hospital, 5000 Odense C, Denmark
| | | | - Daniel Phillips
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Andrea D van Dam
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Nellie Y Loh
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Marijana Todorčević
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Nathan Denton
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK; Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK
| | - Constantinos Christodoulides
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Philippe Collas
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Immunology, Oslo University Hospital, Oslo, Norway
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, OUH Foundation Trust, Oxford, UK.
| | - Katherine E Pinnick
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK.
| |
Collapse
|
237
|
Rowland B, Venkatesh S, Tardaguila M, Wen J, Rosen JD, Tapia AL, Sun Q, Graff M, Vuckovic D, Lettre G, Sankaran VG, Voloudakis G, Roussos P, Huffman JE, Reiner AP, Soranzo N, Raffield LM, Li Y. Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits. Hum Mol Genet 2022; 31:2333-2347. [PMID: 35138379 PMCID: PMC9307312 DOI: 10.1093/hmg/ddac011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/15/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.
Collapse
Affiliation(s)
- Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sanan Venkatesh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Manuel Tardaguila
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jonathan D Rosen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Amanda L Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dragana Vuckovic
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Guillaume Lettre
- Montreal Heart Institute, Université de Montréal, Montreal, Quebec, Canada
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Georgios Voloudakis
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Panos Roussos
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Jennifer E Huffman
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| |
Collapse
|
238
|
Naret O, Kutalik Z, Hodel F, Xu ZM, Marques-Vidal P, Fellay J. Improving polygenic prediction with genetically inferred ancestry. HGG ADVANCES 2022; 3:100109. [PMID: 35571679 PMCID: PMC9095896 DOI: 10.1016/j.xhgg.2022.100109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Genome-wide association studies (GWASs) have demonstrated that most common diseases have a strong genetic component from many genetic variants each with a small effect size. GWAS summary statistics have allowed the construction of polygenic scores (PGSs) estimating part of the individual risk for common diseases. Here, we propose to improve PGS-based risk estimation by incorporating genetic ancestry derived from genome-wide genotyping data. Our method involves three cohorts: a base (or discovery) for association studies, a target for phenotype/risk prediction, and a map for ancestry mapping; successively, (1) it generates for each individual in the base and target cohorts a set of principal components based on the map cohort-called mapped PCs, (2) it associates in the base cohort the phenotype with the mapped-PCs, and (3) it uses the mapped PCs in the target cohort to generate a phenotypic predictor called the ancestry score. We evaluated the ancestry score by comparing a predictive model using a PGS with one combining a PGS and an ancestry score. First, we performed simulations and found that the ancestry score has a greater impact on traits that correlate with ancestry-specific variants. Second, we showed, using UK Biobank data, that the ancestry score improves genetic prediction for our nine phenotypes to very different degrees. Third, we performed simulations and found that the more heterogeneous the base and target cohorts, the more beneficial the ancestry score is. Finally, we validated our approach under realistic conditions with UK Biobank as the base cohort and Swiss individuals from the CoLaus|PsyCoLaus study as the target cohort.
Collapse
Affiliation(s)
- Olivier Naret
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Flavia Hodel
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zhi Ming Xu
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jacques Fellay
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
239
|
Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
Collapse
Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Corresponding author
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| |
Collapse
|
240
|
Hujoel ML, Loh PR, Neale BM, Price AL. Incorporating family history of disease improves polygenic risk scores in diverse populations. CELL GENOMICS 2022; 2:100152. [PMID: 35935918 PMCID: PMC9351615 DOI: 10.1016/j.xgen.2022.100152] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/22/2022] [Accepted: 06/09/2022] [Indexed: 01/04/2023]
Abstract
Polygenic risk scores (PRSs) derived from genotype data and family history (FH) of disease provide valuable information for predicting disease risk, but PRSs perform poorly when applied to diverse populations. Here, we explore methods for combining both types of information (PRS-FH) in UK Biobank data. PRSs were trained using all British individuals (n = 409,000), and target samples consisted of unrelated non-British Europeans (n = 42,000), South Asians (n = 7,000), or Africans (n = 7,000). We evaluated PRS, FH, and PRS-FH using liability-scale R 2, primarily focusing on 3 well-powered diseases (type 2 diabetes, hypertension, and depression). PRS attained average prediction R 2s of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R 2s of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans. In conclusion, including family history improves the accuracy of polygenic risk scores, particularly in diverse populations.
Collapse
Affiliation(s)
- Margaux L.A. Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| |
Collapse
|
241
|
Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proc Natl Acad Sci U S A 2022; 119:e2106858119. [PMID: 35787050 PMCID: PMC9282238 DOI: 10.1073/pnas.2106858119] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.
Collapse
|
242
|
Kirchler M, Konigorski S, Norden M, Meltendorf C, Kloft M, Schurmann C, Lippert C. transferGWAS: GWAS of images using deep transfer learning. Bioinformatics 2022; 38:3621-3628. [PMID: 35640976 DOI: 10.1093/bioinformatics/btac369] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 05/05/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. RESULTS We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. AVAILABILITY AND IMPLEMENTATION Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Matthias Kirchler
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Stefan Konigorski
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthias Norden
- Digital Health & Personalized Medicine Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Department of Anesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Christian Meltendorf
- Department of Electrical Engineering - Mechatronics - Optometry, Beuth University of Applied Sciences Berlin, 13353 Berlin, Germany
| | - Marius Kloft
- Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Claudia Schurmann
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Digital Health & Personalized Medicine Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
| | - Christoph Lippert
- Digital Health-Machine Learning Research Group, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| |
Collapse
|
243
|
Xiao J, Cai M, Yu X, Hu X, Chen G, Wan X, Yang C. Leveraging the local genetic structure for trans-ancestry association mapping. Am J Hum Genet 2022; 109:1317-1337. [PMID: 35714612 PMCID: PMC9300880 DOI: 10.1016/j.ajhg.2022.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/23/2022] [Indexed: 01/09/2023] Open
Abstract
Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.
Collapse
Affiliation(s)
- Jiashun Xiao
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xinyi Yu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China; Pazhou Lab, Guangzhou 510330, China.
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
| |
Collapse
|
244
|
Halldorsson BV, Eggertsson HP, Moore KHS, Hauswedell H, Eiriksson O, Ulfarsson MO, Palsson G, Hardarson MT, Oddsson A, Jensson BO, Kristmundsdottir S, Sigurpalsdottir BD, Stefansson OA, Beyter D, Holley G, Tragante V, Gylfason A, Olason PI, Zink F, Asgeirsdottir M, Sverrisson ST, Sigurdsson B, Gudjonsson SA, Sigurdsson GT, Halldorsson GH, Sveinbjornsson G, Norland K, Styrkarsdottir U, Magnusdottir DN, Snorradottir S, Kristinsson K, Sobech E, Jonsson H, Geirsson AJ, Olafsson I, Jonsson P, Pedersen OB, Erikstrup C, Brunak S, Ostrowski SR, Thorleifsson G, Jonsson F, Melsted P, Jonsdottir I, Rafnar T, Holm H, Stefansson H, Saemundsdottir J, Gudbjartsson DF, Magnusson OT, Masson G, Thorsteinsdottir U, Helgason A, Jonsson H, Sulem P, Stefansson K. The sequences of 150,119 genomes in the UK Biobank. Nature 2022; 607:732-740. [PMID: 35859178 PMCID: PMC9329122 DOI: 10.1038/s41586-022-04965-x] [Citation(s) in RCA: 149] [Impact Index Per Article: 74.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 06/10/2022] [Indexed: 12/25/2022]
Abstract
Detailed knowledge of how diversity in the sequence of the human genome affects phenotypic diversity depends on a comprehensive and reliable characterization of both sequences and phenotypic variation. Over the past decade, insights into this relationship have been obtained from whole-exome sequencing or whole-genome sequencing of large cohorts with rich phenotypic data1,2. Here we describe the analysis of whole-genome sequencing of 150,119 individuals from the UK Biobank3. This constitutes a set of high-quality variants, including 585,040,410 single-nucleotide polymorphisms, representing 7.0% of all possible human single-nucleotide polymorphisms, and 58,707,036 indels. This large set of variants allows us to characterize selection based on sequence variation within a population through a depletion rank score of windows along the genome. Depletion rank analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UK Biobank: a large British Irish cohort, a smaller African cohort and a South Asian cohort. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large-scale whole-genome sequencing studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on whole-exome sequencing and/or imputation.
Collapse
Affiliation(s)
- Bjarni V Halldorsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland. .,School of Technology, Reykjavik University, Reykjavik, Iceland.
| | | | | | | | | | - Magnus O Ulfarsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Marteinn T Hardarson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | | | - Snaedis Kristmundsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Brynja D Sigurpalsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Helgi Jonsson
- Landspitali-University Hospital, Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Palmi Jonsson
- Landspitali-University Hospital, Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Ole Birger Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Christian Erikstrup
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Clinical Sciences, Copenhagen University, Copenhagen, Denmark
| | | | | | | | - Pall Melsted
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Ingileif Jonsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | | | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Agnar Helgason
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Department of Anthropology, University of Iceland, Reykjavik, Iceland
| | | | | | | |
Collapse
|
245
|
Verma V, Drury GL, Parisien M, Özdağ Acarli AN, Al-Aubodah TA, Nijnik A, wen X, Tugarinov N, Verner M, Klares R, Linton A, Krock E, Morado Urbina CE, Winsvold B, Fritsche LG, Fors EA, Piccirillo C, Khoutorsky A, Svensson CI, Fitzcharles MA, Ingelmo PM, Bernard NF, Dupuy FP, Üçeyler N, Sommer C, King IL, Meloto CB, Diatchenko L. Unbiased immune profiling reveals a natural killer cell-peripheral nerve axis in fibromyalgia. Pain 2022; 163:e821-e836. [PMID: 34913882 PMCID: PMC8942876 DOI: 10.1097/j.pain.0000000000002498] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/13/2021] [Indexed: 11/25/2022]
Abstract
ABSTRACT The pathophysiology of fibromyalgia syndrome (FMS) remains elusive, leading to a lack of objective diagnostic criteria and targeted treatment. We globally evaluated immune system changes in FMS by conducting multiparametric flow cytometry analyses of peripheral blood mononuclear cells and identified a natural killer (NK) cell decrease in patients with FMS. Circulating NK cells in FMS were exhausted yet activated, evidenced by lower surface expression of CD16, CD96, and CD226 and more CD107a and TIGIT. These NK cells were hyperresponsive, with increased CCL4 production and expression of CD107a when co-cultured with human leukocyte antigen null target cells. Genetic and transcriptomic pathway analyses identified significant enrichment of cell activation pathways in FMS driven by NK cells. Skin biopsies showed increased expression of NK activation ligand, unique long 16-binding protein, on subepidermal nerves of patients FMS and the presence of NK cells near peripheral nerves. Collectively, our results suggest that chronic activation and redistribution of circulating NK cells to the peripheral nerves contribute to the immunopathology associated with FMS.
Collapse
Affiliation(s)
- Vivek Verma
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montréal, Canada
| | - Gillian L. Drury
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Marc Parisien
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Ayşe N. Özdağ Acarli
- Department of Neurology, Faculty of Medicine, Istanbul University, Istanbul, Turkey
- Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Tho-Alfakar Al-Aubodah
- Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montréal, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Anastasia Nijnik
- Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada
- McGill Research Centre on Complex Traits, McGill University, Montréal, Canada
| | - Xia wen
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Nicol Tugarinov
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Maria Verner
- Faculty of Dentistry, McGill University, Montréal, Canada
| | - Richie Klares
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Alexander Linton
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Emerson Krock
- Department of Physiology and Pharmacology, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Carlos E. Morado Urbina
- Department of Physiology and Pharmacology, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Bendik Winsvold
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Lars G. Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Egil A. Fors
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ciriaco Piccirillo
- Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montréal, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Arkady Khoutorsky
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Faculty of Dentistry, McGill University, Montréal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montréal, Canada
| | - Camilla I. Svensson
- Department of Physiology and Pharmacology, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mary A. Fitzcharles
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Division of Rheumatology, Faculty of Medicine, McGill University, Montréal, Canada
| | - Pablo M. Ingelmo
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montréal, Canada
| | - Nicole F. Bernard
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Canada
| | - Franck P. Dupuy
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Nurcan Üçeyler
- Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Claudia Sommer
- Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Irah L. King
- Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montréal, Canada
- Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Carolina B. Meloto
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Faculty of Dentistry, McGill University, Montréal, Canada
| | - Luda Diatchenko
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Faculty of Dentistry, McGill University, Montréal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montréal, Canada
| |
Collapse
|
246
|
Trastulla L, Noorbakhsh J, Vazquez F, McFarland J, Iorio F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol Syst Biol 2022; 18:e11017. [PMID: 35822563 PMCID: PMC9277610 DOI: 10.15252/msb.202211017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.
Collapse
Affiliation(s)
- Lucia Trastulla
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
| | | | - Francisca Vazquez
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Francesco Iorio
- Human Technopole, Milano, Italy.,Open Targets, Cambridge, UK
| |
Collapse
|
247
|
Agrawal S, Wang M, Klarqvist MDR, Smith K, Shin J, Dashti H, Diamant N, Choi SH, Jurgens SJ, Ellinor PT, Philippakis A, Claussnitzer M, Ng K, Udler MS, Batra P, Khera AV. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat Commun 2022; 13:3771. [PMID: 35773277 PMCID: PMC9247093 DOI: 10.1038/s41467-022-30931-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/25/2022] [Indexed: 12/11/2022] Open
Abstract
For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.
Collapse
Affiliation(s)
- Saaket Agrawal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | | | - Kirk Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joseph Shin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hesam Dashti
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sean J Jurgens
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Melina Claussnitzer
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Miriam S Udler
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit V Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Cambridge, MA, USA.
| |
Collapse
|
248
|
Ren D, Cai X, Lin Q, Ye H, Teng J, Li J, Ding X, Zhang Z. Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation. Genet Sel Evol 2022; 54:47. [PMID: 35761182 PMCID: PMC9235212 DOI: 10.1186/s12711-022-00737-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. METHODS The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. RESULTS Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by - 0.1 to ~ 6.9% for real traits with the medium-density panel. CONCLUSIONS Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.
Collapse
Affiliation(s)
- Duanyang Ren
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiaodian Cai
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Qing Lin
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Haoqiang Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
| |
Collapse
|
249
|
von Berg J, ten Dam M, van der Laan SW, de Ridder J. PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics. Bioinformatics 2022; 38:i212-i219. [PMID: 35758773 PMCID: PMC9235478 DOI: 10.1093/bioinformatics/btac228] [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] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from genome-wide association studies (GWAS) summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis, in the case of two traits). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a P-value per SNP that can be used for further analysis. RESULTS We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. We show how PolarMorphism can be used to gain insight into relationships between traits and trait domains and contrast it with genetic correlation. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods. AVAILABILITY AND IMPLEMENTATION code: https://github.com/UMCUGenetics/PolarMorphism, results: 10.5281/zenodo.5844193. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Joanna von Berg
- Center for Molecular Medicine, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| | - Michelle ten Dam
- Center for Molecular Medicine, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| |
Collapse
|
250
|
Genome-wide meta-analysis of iron status biomarkers and the effect of iron on all-cause mortality in HUNT. Commun Biol 2022; 5:591. [PMID: 35710628 PMCID: PMC9203493 DOI: 10.1038/s42003-022-03529-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/24/2022] [Indexed: 01/19/2023] Open
Abstract
Iron is essential for many biological processes, but iron levels must be tightly regulated to avoid harmful effects of both iron deficiency and overload. Here, we perform genome-wide association studies on four iron-related biomarkers (serum iron, serum ferritin, transferrin saturation, total iron-binding capacity) in the Trøndelag Health Study (HUNT), the Michigan Genomics Initiative (MGI), and the SardiNIA study, followed by their meta-analysis with publicly available summary statistics, analyzing up to 257,953 individuals. We identify 123 genetic loci associated with iron traits. Among 19 novel protein-altering variants, we observe a rare missense variant (rs367731784) in HUNT, which suggests a role for DNAJC13 in transferrin recycling. We further validate recently published results using genetic risk scores for each biomarker in HUNT (6% variance in serum iron explained) and present linear and non-linear Mendelian randomization analyses of the traits on all-cause mortality. We find evidence of a harmful effect of increased serum iron and transferrin saturation in linear analyses that estimate population-averaged effects. However, there was weak evidence of a protective effect of increasing serum iron at the very low end of its distribution. Our findings contribute to our understanding of the genes affecting iron status and its consequences on human health. A GWAS on the four major iron-related biomarkers is conducted using data from the Trøndelag Health Study (HUNT), Michigan Genomics Initiative (MGI), and SardiNIA study, to identify 123 loci associated with iron homeostasis and effect on all-cause mortality.
Collapse
|