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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: 86] [Impact Index Per Article: 43.0] [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.
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252
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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.
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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
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253
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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.
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254
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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.
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255
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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: 108] [Impact Index Per Article: 54.0] [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.
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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.
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256
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Verdiesen RM, von Berg J, Said MA, van der Harst P, Mahajan A, van Gils CH, van der Schouw YT, Onland-Moret NC. Anti-Müllerian Hormone and Cardiometabolic Disease in Women: A Two-Sample Mendelian Randomization Study. Rev Cardiovasc Med 2022; 23:269. [PMID: 39076620 PMCID: PMC11266950 DOI: 10.31083/j.rcm2308269] [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] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 07/31/2024] Open
Abstract
Background Higher age-specific circulating anti-Müllerian hormone (AMH) levels have been linked to a lower risk of cardiometabolic outcomes. However, whether AMH has a casual role in the etiology of these diseases is unknown. The objective of this study was therefore to explore if circulating AMH levels have a causal effect on risk of coronary artery disease (CAD), ischemic stroke and type 2 diabetes (T2D) in women, using a two-sample Mendelian randomization (MR) approach. Methods We used four single nucleotide polymorphisms (SNPs) from the most recent AMH GWAS meta-analysis as instrumental variables. Summary-level data for CAD (n = 149,752; 11,802 cases), ischemic stroke (n = 17,541; 4678 cases) and T2D (n = 464,389; 30,052 cases) were extracted from the UK Biobank, the Stroke Genetics Network, and DIAMANTE consortia, respectively. To assess the presence of potential pleiotropy we tested the association of the four AMH SNPs, both individually and combined in a weighted genetic risk score, with a range of cardiovascular risk factors and intermediate traits using UK Biobank data. Results MR estimates, i.e., inverse variance-weighted odds ratios ( OR IVW ), did not support a causal effect of circulating AMH levels on CAD ( OR IVW = 1.13, 95% CI: 0.95-1.35), ischemic stroke ( OR IVW = 1.11, 95% CI: 0.83-1.49), and T2D ( OR IVW = 0.98, 95% CI: 0.87-1.10). After adjustment for multiple testing, we observed associations between genetically predicted AMH and age at menopause, and age at menarche, but not with intermediate traits on the causal pathway between AMH and cardiometabolic health, such as atherosclerosis or glucose levels. Conclusions This study does not provide evidence for a causal effect of circulating AMH levels on CAD, ischemic stroke and T2D in women, although weak instrument bias cannot be excluded.
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Affiliation(s)
- Renée M.G. Verdiesen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
| | - Joanna von Berg
- Division Laboratory, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Center for Molecular Medicine, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - M. Abdullah Said
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, OX3 7BN Oxford, UK
| | - Carla H. van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
| | - Yvonne T. van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
| | - N. Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands
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257
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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.
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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
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258
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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.
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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.
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259
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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.
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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
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260
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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.
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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
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261
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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.
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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
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262
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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: 14] [Impact Index Per Article: 7.0] [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.
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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
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263
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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: 36] [Impact Index Per Article: 18.0] [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.
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264
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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.
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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
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265
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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: 7] [Impact Index Per Article: 3.5] [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.
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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.
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266
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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: 164] [Impact Index Per Article: 82.0] [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.
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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
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267
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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.
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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
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268
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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.
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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
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269
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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: 46] [Impact Index Per Article: 23.0] [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.
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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.
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270
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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.
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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.
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271
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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: 5] [Impact Index Per Article: 2.5] [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.
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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
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272
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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: 11] [Impact Index Per Article: 5.5] [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.
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273
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Thomas TR, Koomar T, Casten LG, Tener AJ, Bahl E, Michaelson JJ. Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population. Transl Psychiatry 2022; 12:247. [PMID: 35697691 PMCID: PMC9192633 DOI: 10.1038/s41398-022-01982-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/26/2022] [Accepted: 05/20/2022] [Indexed: 12/31/2022] Open
Abstract
The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is undetermined if autistic symptom domain severity underlying this heterogeneity is heritable and pleiotropic with other psychiatric and behavior traits in the same manner as autism case-control status. In N = 6064 autistic children in the SPARK cohort, we investigated the common genetic properties of twelve subscales from three clinical autism instruments measuring autistic traits: the Social Communication Questionnaire (SCQ), the Repetitive Behavior Scale-Revised (RBS-R), and the Developmental Coordination Disorder Questionnaire (DCDQ). Educational attainment polygenic scores (PGS) were significantly negatively correlated with eleven subscales, while ADHD and major depression PGS were positively correlated with ten and eight of the autism subscales, respectively. Loneliness and neuroticism PGS were also positively correlated with many subscales. Significant PGS by sex interactions were found-surprisingly, the autism case-control PGS was negatively correlated in females and had no strong correlation in males. SNP-heritability of the DCDQ subscales ranged from 0.04 to 0.08, RBS-R subscales ranged from 0.09 to 0.24, and SCQ subscales ranged from 0 to 0.12. GWAS in SPARK followed by estimation of polygenic scores (PGS) in the typically-developing ABCD cohort (N = 5285), revealed significant associations of RBS-R subscale PGS with autism-related behavioral traits, with several subscale PGS more strongly correlated than the autism case-control PGS. Overall, our analyses suggest that the clinical autism subscale traits show variability in SNP-heritability, PGS associations, and significant PGS by sex interactions, underscoring the heterogeneity in autistic traits at a genetic level. Furthermore, of the three instruments investigated, the RBS-R shows the greatest evidence of genetic signal in both (1) autistic samples (greater heritability) and (2) general population samples (strongest PGS associations).
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Affiliation(s)
- Taylor R Thomas
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Tanner Koomar
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Lucas G Casten
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Ashton J Tener
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Ethan Bahl
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Jacob J Michaelson
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA.
- Hawkeye Intellectual and Developmental Disabilities Research Center (Hawk-IDDRC), University of Iowa, Iowa City, IA, USA.
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274
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Borges MC, Haycock PC, Zheng J, Hemani G, Holmes MV, Davey Smith G, Hingorani AD, Lawlor DA. Role of circulating polyunsaturated fatty acids on cardiovascular diseases risk: analysis using Mendelian randomization and fatty acid genetic association data from over 114,000 UK Biobank participants. BMC Med 2022; 20:210. [PMID: 35692035 PMCID: PMC9190170 DOI: 10.1186/s12916-022-02399-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 05/09/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Despite early interest in the health effects of polyunsaturated fatty acids (PUFA), there is still substantial controversy and uncertainty on the evidence linking PUFA to cardiovascular diseases (CVDs). We investigated the effect of plasma concentration of omega-3 PUFA (i.e. docosahexaenoic acid (DHA) and total omega-3 PUFA) and omega-6 PUFA (i.e. linoleic acid and total omega-6 PUFA) on the risk of CVDs using Mendelian randomization. METHODS We conducted the largest genome-wide association study (GWAS) of circulating PUFA to date including a sample of 114,999 individuals and incorporated these data in a two-sample Mendelian randomization framework to investigate the involvement of circulating PUFA on a wide range of CVDs in up to 1,153,768 individuals of European ancestry (i.e. coronary artery disease, ischemic stroke, haemorrhagic stroke, heart failure, atrial fibrillation, peripheral arterial disease, aortic aneurysm, venous thromboembolism and aortic valve stenosis). RESULTS GWAS identified between 46 and 64 SNPs for the four PUFA traits, explaining 4.8-7.9% of circulating PUFA variance and with mean F statistics >100. Higher genetically predicted DHA (and total omega-3 fatty acids) concentration was related to higher risk of some cardiovascular endpoints; however, these findings did not pass our criteria for multiple testing correction and were attenuated when accounting for LDL-cholesterol through multivariable Mendelian randomization or excluding SNPs in the vicinity of the FADS locus. Estimates for the relation between higher genetically predicted linoleic acid (and total omega-6) concentration were inconsistent across different cardiovascular endpoints and Mendelian randomization methods. There was weak evidence of higher genetically predicted linoleic acid being related to lower risk of ischemic stroke and peripheral artery disease when accounting by LDL-cholesterol. CONCLUSIONS We have conducted the largest GWAS of circulating PUFA to date and the most comprehensive Mendelian randomization analyses. Overall, our Mendelian randomization findings do not support a protective role of circulating PUFA concentration on the risk of CVDs. However, horizontal pleiotropy via lipoprotein-related traits could be a key source of bias in our analyses.
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Affiliation(s)
- Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Philip C Haycock
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael V Holmes
- MRC Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- UCL BHF Research Accelerator, London, UK
- Health Data Research UK, Institute of Health Informatics, University College London, London, UK
- UCL NIHR Biomedical Research Centre, London, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
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275
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Xia K, Shabalin AA, Yin Z, Chung W, Sullivan PF, Wright FA, Styner M, Gilmore JH, Santelli RC, Zou F. TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies. Genetics 2022; 221:6605853. [PMID: 35689615 DOI: 10.1093/genetics/iyac088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.
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Affiliation(s)
- Kai Xia
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.,Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Andrey A Shabalin
- Department of Psychiatry, University of Utah, Salt Lake City, UT 84108, USA
| | - Zhaoyu Yin
- Gilead Sciences, Foster City, CA 94404, USA
| | - Wonil Chung
- School of Public Health, Harvard, Boston, MA 02115, USA
| | - Patrick F Sullivan
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Fred A Wright
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Rebecca C Santelli
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI 48912, USA
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
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276
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Mallard TT, Karlsson Linnér R, Grotzinger AD, Sanchez-Roige S, Seidlitz J, Okbay A, de Vlaming R, Meddens SFW, Palmer AA, Davis LK, Tucker-Drob EM, Kendler KS, Keller MC, Koellinger PD, Harden KP. Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities. CELL GENOMICS 2022; 2:S2666-979X(22)00073-8. [PMID: 35812988 PMCID: PMC9264403 DOI: 10.1016/j.xgen.2022.100140] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 10/25/2021] [Accepted: 05/10/2022] [Indexed: 02/07/2023]
Abstract
Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.
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Affiliation(s)
- Travis T. Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Richard Karlsson Linnér
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Autism and Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA
| | | | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ronald de Vlaming
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - S. Fleur W. Meddens
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Bipolar Disorder Working Group of the Psychiatric Genomics Consortium
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Autism and Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Medical College of Virginia/Virginia Commonwealth University, Richmond, VA, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Abraham A. Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lea K. Davis
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elliot M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Medical College of Virginia/Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew C. Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Philipp D. Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - K. Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
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277
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Khunsriraksakul C, McGuire D, Sauteraud R, Chen F, Yang L, Wang L, Hughey J, Eckert S, Dylan Weissenkampen J, Shenoy G, Marx O, Carrel L, Jiang B, Liu DJ. Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies. Nat Commun 2022; 13:3258. [PMID: 35672318 PMCID: PMC9171100 DOI: 10.1038/s41467-022-30956-7] [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] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 05/25/2022] [Indexed: 02/08/2023] Open
Abstract
Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.
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Affiliation(s)
- Chachrit Khunsriraksakul
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Daniel McGuire
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Renan Sauteraud
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Fang Chen
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Lina Yang
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Lida Wang
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Jordan Hughey
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Scott Eckert
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - J. Dylan Weissenkampen
- grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Ganesh Shenoy
- grid.29857.310000 0001 2097 4281Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Olivia Marx
- grid.29857.310000 0001 2097 4281Biomedical Science Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Laura Carrel
- grid.29857.310000 0001 2097 4281Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Bibo Jiang
- grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
| | - Dajiang J. Liu
- grid.29857.310000 0001 2097 4281Bioinformatics and Genomics Graduate Program, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA ,grid.29857.310000 0001 2097 4281Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
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278
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Sorokin EP, Basty N, Whitcher B, Liu Y, Bell JD, Cohen RL, Cule M, Thomas EL. Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis. Am J Hum Genet 2022; 109:1092-1104. [PMID: 35568031 PMCID: PMC9247824 DOI: 10.1016/j.ajhg.2022.04.013] [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] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.
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Affiliation(s)
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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279
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Ahmed WUR, Kleeman S, Ng M, Wang W, Auton A, Lee R, Handa A, Zondervan KT, Wiberg A, Furniss D. Genome-wide association analysis and replication in 810,625 individuals with varicose veins. Nat Commun 2022; 13:3065. [PMID: 35654884 PMCID: PMC9163161 DOI: 10.1038/s41467-022-30765-y] [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] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 05/17/2022] [Indexed: 12/30/2022] Open
Abstract
Varicose veins affect one-third of Western society, with a significant subset of patients developing venous ulceration, costing $14.9 billion annually in the USA. Current management consists of either compression stockings, or surgical ablation for more advanced disease. Most varicose veins patients report a positive family history, and heritability is ~17%. We describe the largest two-stage genome-wide association study of varicose veins in 401,656 individuals from UK Biobank, and replication in 408,969 individuals from 23andMe (total 135,514 cases and 675,111 controls). Forty-nine signals at 46 susceptibility loci were discovered. We map 237 genes to these loci, several of which are biologically plausible and tractable to therapeutic targeting. Pathway analysis identified enrichment in extracellular matrix biology, inflammation, (lymph)angiogenesis, vascular smooth muscle cell migration, and apoptosis. Using a polygenic risk score (PRS) derived in an independent cohort, we demonstrate its predictive utility and correlation with varicose veins surgery.
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Affiliation(s)
- Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | - Sam Kleeman
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Michael Ng
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | - Wei Wang
- 23andMe, Inc., Sunnyvale, CA, USA
| | | | | | - Regent Lee
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Ashok Handa
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Krina T Zondervan
- Nuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK.,Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Akira Wiberg
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK.,Department of Plastic and Reconstructive Surgery, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Dominic Furniss
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK. .,Department of Plastic and Reconstructive Surgery, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
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280
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Chung W, Hwang H, Park T. Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort. Genomics Inform 2022; 20:e16. [PMID: 35794696 DOI: 10.5808/gi.22022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/14/2022] [Indexed: 11/20/2022] Open
Abstract
Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.
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Affiliation(s)
- Wonil Chung
- Department of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea.,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Hyunji Hwang
- Department of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea
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281
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Genetic architecture of band neutrophil fraction in Iceland. Commun Biol 2022; 5:525. [PMID: 35650273 PMCID: PMC9160026 DOI: 10.1038/s42003-022-03462-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 05/09/2022] [Indexed: 11/22/2022] Open
Abstract
The characteristic lobulated nuclear morphology of granulocytes is partially determined by composition of nuclear envelope proteins. Abnormal nuclear morphology is primarily observed as an increased number of hypolobulated immature neutrophils, called band cells, during infection or in rare envelopathies like Pelger-Huët anomaly. To search for sequence variants affecting nuclear morphology of granulocytes, we performed a genome-wide association study using band neutrophil fraction from 88,101 Icelanders. We describe 13 sequence variants affecting band neutrophil fraction at nine loci. Five of the variants are at the Lamin B receptor (LBR) locus, encoding an inner nuclear membrane protein. Mutations in LBR are linked to Pelger-Huët anomaly. In addition, we identify cosegregation of a rare stop-gain sequence variant in LBR and Pelger Huët anomaly in an Icelandic eight generation pedigree, initially reported in 1963. Two of the other loci include genes which, like LBR, play a role in the nuclear membrane function and integrity. These GWAS results highlight the role proteins of the inner nuclear membrane have as important for neutrophil nuclear morphology. A GWAS in Iceland reveals that variants in inner nuclear membrane proteins are associated with nuclear morphology of granulocytes and band neutrophil fraction.
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282
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O’Nunain K, Park C, Urquijo H, Leyden GM, Hughes AD, Davey Smith G, Richardson TG. A lifecourse mendelian randomization study highlights the long-term influence of childhood body size on later life heart structure. PLoS Biol 2022; 20:e3001656. [PMID: 35679339 PMCID: PMC9182693 DOI: 10.1371/journal.pbio.3001656] [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: 03/30/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
Children with obesity typically have larger left ventricular heart dimensions during adulthood. However, whether this is due to a persistent effect of adiposity extending into adulthood is challenging to disentangle due to confounding factors throughout the lifecourse. We conducted a multivariable mendelian randomization (MR) study to separate the independent effects of childhood and adult body size on 4 magnetic resonance imaging (MRI) measures of heart structure and function in the UK Biobank (UKB) study. Strong evidence of a genetically predicted effect of childhood body size on all measures of adulthood heart structure was identified, which remained robust upon accounting for adult body size using a multivariable MR framework (e.g., left ventricular end-diastolic volume (LVEDV), Beta = 0.33, 95% confidence interval (CI) = 0.23 to 0.43, P = 4.6 × 10−10). Sensitivity analyses did not suggest that other lifecourse measures of body composition were responsible for these effects. Conversely, evidence of a genetically predicted effect of childhood body size on various other MRI-based measures, such as fat percentage in the liver (Beta = 0.14, 95% CI = 0.05 to 0.23, P = 0.002) and pancreas (Beta = 0.21, 95% CI = 0.10 to 0.33, P = 3.9 × 10−4), attenuated upon accounting for adult body size. Our findings suggest that childhood body size has a long-term (and potentially immutable) influence on heart structure in later life. In contrast, effects of childhood body size on other measures of adulthood organ size and fat percentage evaluated in this study are likely explained by the long-term consequence of remaining overweight throughout the lifecourse. Children with obesity typically have larger left ventricular heart dimensions during adulthood, but this is challenging to disentangle due to confounding factors throughout life. This study uses Mendelian randomization to provide evidence that being overweight in early life may indeed have a long-term influence on cardiac structure.
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Affiliation(s)
- Katie O’Nunain
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Chloe Park
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Helena Urquijo
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Genevieve M. Leyden
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, United Kingdom
| | - Alun D. Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - George Davey Smith
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Tom G. Richardson
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Novo Nordisk Research Centre, Headington, Oxford, United Kingdom
- * E-mail:
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283
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Wang F, Ghanzouri I, Leeper NJ, Tsao PS, Ross EG. Development of a polygenic risk score to improve detection of peripheral artery disease. Vasc Med 2022; 27:219-227. [PMID: 35287516 PMCID: PMC9254893 DOI: 10.1177/1358863x211067564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
INTRODUCTION Peripheral artery disease (PAD) is a major cause of cardiovascular morbidity and mortality, yet timely diagnosis is elusive. Larger genome-wide association studies (GWAS) have now provided the ability to evaluate whether genetic data, in the form of genome-wide polygenic risk scores (PRS), can help improve our ability to identify patients at high risk of having PAD. METHODS Using summary statistic data from the largest PAD GWAS from the Million Veteran Program, we developed PRSs with genome data from UK Biobank. We then evaluated the clinical utility of adding the best-performing PRS to a PAD clinical risk score. RESULTS A total of 487,320 participants (5759 PAD cases) were included in our final genetic analysis. Compared to participants in the lowest 10% of PRS, those in the highest decile had 3.1 higher odds of having PAD (95% CI, 3.06-3.21). Additionally, a PAD PRS was associated with increased risk of having coronary artery disease, congestive heart failure, and cerebrovascular disease. The PRS significantly improved a clinical risk model (Net Reclassification Index = 0.07, p < 0.001), with most of the performance seen in downgrading risk of controls. Combining clinical and genetic data to detect risk of PAD resulted in a model with an area under the curve of 0.76 (95% CI, 0.75-0.77). CONCLUSION We demonstrate that a genome-wide PRS can discriminate risk of PAD and other cardiovascular diseases. Adding a PAD PRS to clinical risk models may help improve detection of prevalent, but undiagnosed disease.
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Affiliation(s)
- Fudi Wang
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Ilies Ghanzouri
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas J Leeper
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Philip S Tsao
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Elsie Gyang Ross
- Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
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284
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Mendelian imputation of parental genotypes improves estimates of direct genetic effects. Nat Genet 2022; 54:897-905. [PMID: 35681053 PMCID: PMC9197765 DOI: 10.1038/s41588-022-01085-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/28/2022] [Indexed: 01/22/2023]
Abstract
Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.
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285
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Pirruccello JP, Di Achille P, Nauffal V, Nekoui M, Friedman SF, Klarqvist MDR, Chaffin MD, Weng LC, Cunningham JW, Khurshid S, Roselli C, Lin H, Koyama S, Ito K, Kamatani Y, Komuro I, Jurgens SJ, Benjamin EJ, Batra P, Natarajan P, Ng K, Hoffmann U, Lubitz SA, Ho JE, Lindsay ME, Philippakis AA, Ellinor PT. Genetic analysis of right heart structure and function in 40,000 people. Nat Genet 2022; 54:792-803. [PMID: 35697867 PMCID: PMC10313645 DOI: 10.1038/s41588-022-01090-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/26/2022] [Indexed: 01/29/2023]
Abstract
Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10-13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.
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Affiliation(s)
- James P Pirruccello
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Paolo Di Achille
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Victor Nauffal
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Samuel F Friedman
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marcus D R Klarqvist
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark D Chaffin
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan W Cunningham
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Shaan Khurshid
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Honghuang Lin
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, USA
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Sean J Jurgens
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Emelia J Benjamin
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, USA
- Department of Medicine, Cardiology and Preventive Medicine Sections, Boston University School of Medicine, Boston, MA, USA
- Epidemiology Department, Boston University School of Public Health, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Udo Hoffmann
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Lubitz
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Jennifer E Ho
- Harvard Medical School, Boston, MA, USA
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mark E Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Thoracic Aortic Center, Massachusetts General Hospital, Boston, MA, USA
| | | | - Patrick T Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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286
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Pirastu N, McDonnell C, Grzeszkowiak EJ, Mounier N, Imamura F, Merino J, Day FR, Zheng J, Taba N, Concas MP, Repetto L, Kentistou KA, Robino A, Esko T, Joshi PK, Fischer K, Ong KK, Gaunt TR, Kutalik Z, Perry JRB, Wilson JF. Using genetic variation to disentangle the complex relationship between food intake and health outcomes. PLoS Genet 2022; 18:e1010162. [PMID: 35653391 PMCID: PMC9162356 DOI: 10.1371/journal.pgen.1010162] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 03/22/2022] [Indexed: 02/02/2023] Open
Abstract
Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.
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Affiliation(s)
- Nicola Pirastu
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- Human Technopole, Milan, Italy
- * E-mail:
| | - Ciara McDonnell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- Centre for Cardiovascular Sciences, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Eryk J. Grzeszkowiak
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Ninon Mounier
- Centre for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Fumiaki Imamura
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Jordi Merino
- Diabetes Unit and Centre for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Felix R. Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School, Bristol, United Kingdom
| | - Nele Taba
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, Trieste, Italy
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Maria Pina Concas
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Linda Repetto
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Katherine A. Kentistou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- Centre for Cardiovascular Sciences, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Antonietta Robino
- Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Peter K. Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Krista Fischer
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ken K. Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, Bristol, United Kingdom
| | - Zoltán Kutalik
- Centre for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - John R. B. Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - James F. Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, Scotland, United Kingdom
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287
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Song Z, Li WD, Li H, Zhang X, Wang N, Fan Q. Genetic basis of job attainment characteristics and the genetic sharing with other SES indices and well-being. Sci Rep 2022; 12:8902. [PMID: 35618877 PMCID: PMC9135765 DOI: 10.1038/s41598-022-12905-y] [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: 11/03/2021] [Accepted: 04/07/2022] [Indexed: 12/29/2022] Open
Abstract
Job attainment is an important component of socioeconomic status (SES). There is currently a paucity of genomic research on an individual's job attainment, as well as how it is related to other SES variables and overall well-being at the whole genome level. By incorporating O*NET occupational information into the UK Biobank database, we performed GWAS analyses of six major job attainment characteristics-job complexity, autonomy, innovation, information demands, emotional demands, and physical demands-on 219,483 individuals of European ancestry. The job attainment characteristics had moderate to high pairwise genetic correlations, manifested by three latent factors: cognitive, emotional, and physical requirements. The latent factor of overall job requirement underlying the job attainment traits represented a critical genetic path from educational attainment to income (P < 0.001). Job attainment characteristics were genetically positively correlated with positive health and well-being outcomes (i.e., subject well-being, overall health rating, number of non-cancer illnesses etc. (|rg|: 0.14-0.51), similar to other SES indices; however, the genetic correlations exhibited opposite directions for physical demands (|rg|: 0.14-0.51) and were largely negligible for emotional demands. By adopting a finer-grained approach to capture specific job attainment phenotypes, our study represents an important step forward in understanding the shared genetic architecture among job attainment characteristics, other SES indices, and potential role in health and well-being outcomes.
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Affiliation(s)
- Zhaoli Song
- Department of Management and Organization, National University of Singapore, Singapore, Singapore.
| | - Wen-Dong Li
- Department of Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Hengtong Li
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
| | - Xin Zhang
- Department of Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Nan Wang
- Department of Management, Lingnan University, Hong Kong, China
| | - Qiao Fan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
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288
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Kweon H, Aydogan G, Dagher A, Bzdok D, Ruff CC, Nave G, Farah MJ, Koellinger PD. Human brain anatomy reflects separable genetic and environmental components of socioeconomic status. SCIENCE ADVANCES 2022; 8:eabm2923. [PMID: 35584223 PMCID: PMC9116589 DOI: 10.1126/sciadv.abm2923] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Socioeconomic status (SES) correlates with brain structure, a relation of interest given the long-observed relations of SES to cognitive abilities and health. Yet, major questions remain open, in particular, the pattern of causality that underlies this relation. In an unprecedently large study, here, we assess genetic and environmental contributions to SES differences in neuroanatomy. We first establish robust SES-gray matter relations across a number of brain regions, cortical and subcortical. These regional correlates are parsed into predominantly genetic factors and those potentially due to the environment. We show that genetic effects are stronger in some areas (prefrontal cortex, insula) than others. In areas showing less genetic effect (cerebellum, lateral temporal), environmental factors are likely to be influential. Our results imply a complex interplay of genetic and environmental factors that influence the SES-brain relation and may eventually provide insights relevant to policy.
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Affiliation(s)
- Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Gökhan Aydogan
- Zürich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, 8006 Zürich, Switzerland
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, QC H3A 2B4, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, QC H3A 2B4, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC H3A 2B4, Canada
- School of Computer Science, McGill University, Montreal, QC H3A 2A7, Canada
- Mila-Quebec Artificial Intelligence Institute, Montreal, QC H2S 3H1, Canada
| | - Christian C. Ruff
- Zürich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, 8006 Zürich, Switzerland
| | - Gideon Nave
- Marketing Department, the Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martha J. Farah
- Center for Neuroscience & Society, University of Pennsylvania, Philadelphia, PA 19104, USA
- Corresponding author. (M.J.F.); (P.D.K.)
| | - Philipp D. Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI 53706, USA
- Corresponding author. (M.J.F.); (P.D.K.)
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289
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Shared genetic architectures of subjective well-being in East Asian and European ancestry populations. Nat Hum Behav 2022; 6:1014-1026. [PMID: 35589828 DOI: 10.1038/s41562-022-01343-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/29/2022] [Indexed: 11/08/2022]
Abstract
Subjective well-being (SWB) has been explored in European ancestral populations; however, whether the SWB genetic architecture is shared across populations remains unclear. We conducted a cross-population genome-wide association study for SWB using samples from Korean (n = 110,919) and European (n = 563,176) ancestries. Five ancestry-specific loci and twelve cross-ancestry significant genomic loci were identified. One novel locus (rs12298541 near HMGA2) associated with SWB was also identified through the European meta-analysis. Significant cross-ancestry genetic correlation for SWB between samples was observed. Polygenic risk analysis in an independent Korean cohort (n = 22,455) demonstrated transferability between populations. Significant correlations between SWB and major depressive disorder, and significant enrichment of central nervous system-related polymorphisms heritability in both ancestry populations were found. Hence, large-scale cross-ancestry genome-wide association studies can advance our understanding of SWB genetic architecture and mental health.
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290
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Choudhury A, Brandenburg JT, Chikowore T, Sengupta D, Boua PR, Crowther NJ, Agongo G, Asiki G, Gómez-Olivé FX, Kisiangani I, Maimela E, Masemola-Maphutha M, Micklesfield LK, Nonterah EA, Norris SA, Sorgho H, Tinto H, Tollman S, Graham SE, Willer CJ, Hazelhurst S, Ramsay M. Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits. Nat Commun 2022; 13:2578. [PMID: 35546142 PMCID: PMC9095599 DOI: 10.1038/s41467-022-30098-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 04/18/2022] [Indexed: 12/30/2022] Open
Abstract
Genetic associations for lipid traits have identified hundreds of variants with clear differences across European, Asian and African studies. Based on a sub-Saharan-African GWAS for lipid traits in the population cross-sectional AWI-Gen cohort (N = 10,603) we report a novel LDL-C association in the GATB region (P-value=1.56 × 10−8). Meta-analysis with four other African cohorts (N = 23,718) provides supporting evidence for the LDL-C association with the GATB/FHIP1A region and identifies a novel triglyceride association signal close to the FHIT gene (P-value =2.66 × 10−8). Our data enable fine-mapping of several well-known lipid-trait loci including LDLR, PMFBP1 and LPA. The transferability of signals detected in two large global studies (GLGC and PAGE) consistently improves with an increase in the size of the African replication cohort. Polygenic risk score analysis shows increased predictive accuracy for LDL-C levels with the narrowing of genetic distance between the discovery dataset and our cohort. Novel discovery is enhanced with the inclusion of African data. Genetic associations and polygenic scores for lipid traits have low transferability to African individuals. Here, the authors perform a large sub-Sarahan African lipid GWAS and find that larger datasets and better global representation in discovery GWAS help to bridge this gap.
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Affiliation(s)
- Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,South African Medical Research Council/University of the Witwatersrand Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Dhriti Sengupta
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Palwende Romuald Boua
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santè, Nanoro, Burkina Faso
| | - Nigel J Crowther
- Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Godfred Agongo
- Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana.,C.K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
| | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
| | - F Xavier Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Eric Maimela
- Department of Public Health, School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Matshane Masemola-Maphutha
- Department of Pathology and Medical Sciences, School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Lisa K Micklesfield
- South African Medical Research Council/University of the Witwatersrand Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Shane A Norris
- South African Medical Research Council/University of the Witwatersrand Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santè, Nanoro, Burkina Faso
| | - Halidou Tinto
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santè, Nanoro, Burkina Faso
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48019, USA
| | | | | | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. .,Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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291
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Song S, Jiang W, Zhang Y, Hou L, Zhao H. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. Am J Hum Genet 2022; 109:802-811. [PMID: 35421325 PMCID: PMC9118121 DOI: 10.1016/j.ajhg.2022.03.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/18/2022] [Indexed: 12/17/2022] Open
Abstract
Heritability is a fundamental concept in genetic studies, measuring the genetic contribution to complex traits and bringing insights about disease mechanisms. The advance of high-throughput technologies has provided many resources for heritability estimation. Linkage disequilibrium (LD) score regression (LDSC) estimates both heritability and confounding biases, such as cryptic relatedness and population stratification, among single-nucleotide polymorphisms (SNPs) by using only summary statistics released from genome-wide association studies. However, only partial information in the LD matrix is utilized in LDSC, leading to loss in precision. In this study, we propose LD eigenvalue regression (LDER), an extension of LDSC, by making full use of the LD information. Compared to state-of-the-art heritability estimating methods, LDER provides more accurate estimates of SNP heritability and better distinguishes the inflation caused by polygenicity and confounding effects. We demonstrate the advantages of LDER both theoretically and with extensive simulations. We applied LDER to 814 complex traits from UK Biobank, and LDER identified 363 significantly heritable phenotypes, among which 97 were not identified by LDSC.
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Affiliation(s)
- Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Yiliang Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA.
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292
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Fan C, Mancuso N, Chiang CW. A genealogical estimate of genetic relationships. Am J Hum Genet 2022; 109:812-824. [PMID: 35417677 DOI: 10.1016/j.ajhg.2022.03.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/25/2022] [Indexed: 12/23/2022] Open
Abstract
The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM treats linked markers as independent and does not explicitly model the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework, namely the expected GRM (eGRM), to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations, we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed with ARG inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to SNP array genotypes from a population sample from Northern and Eastern Finland, we find that clustering analysis with the eGRM reveals population structure driven by subpopulations that would not be apparent via the canonical GRM and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.
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293
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Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies. PLoS Genet 2022; 18:e1010234. [PMID: 35639796 PMCID: PMC9187121 DOI: 10.1371/journal.pgen.1010234] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 06/10/2022] [Accepted: 05/03/2022] [Indexed: 12/30/2022] Open
Abstract
Sprague Dawley (SD) rats are among the most widely used outbred laboratory rat populations. Despite this, the genetic characteristics of SD rats have not been clearly described, and SD rats are rarely used for experiments aimed at exploring genotype-phenotype relationships. In order to use SD rats to perform a genome-wide association study (GWAS), we collected behavioral data from 4,625 SD rats that were predominantly obtained from two commercial vendors, Charles River Laboratories and Harlan Sprague Dawley Inc. Using double-digest genotyping-by-sequencing (ddGBS), we obtained dense, high-quality genotypes at 291,438 SNPs across 4,061 rats. This genetic data allowed us to characterize the variation present in Charles River vs. Harlan SD rats. We found that the two populations are highly diverged (FST > 0.4). Furthermore, even for rats obtained from the same vendor, there was strong population structure across breeding facilities and even between rooms at the same facility. We performed multiple separate GWAS by fitting a linear mixed model that accounted for population structure and using meta-analysis to jointly analyze all cohorts. Our study examined Pavlovian conditioned approach (PavCA) behavior, which assesses the propensity for rats to attribute incentive salience to reward-associated cues. We identified 46 significant associations for the various metrics used to define PavCA. The surprising degree of population structure among SD rats from different sources has important implications for their use in both genetic and non-genetic studies. Outbred Sprague Dawley rats are among the most commonly used rats for neuroscience, physiology and pharmacological research; in the year 2020, 4,188 publications contained the keyword “Sprague Dawley”. Rats identified as “Sprague Dawley” are sold by several commercial vendors, including Charles River Laboratories and Harlan Sprague Dawley Inc. (now Envigo). Despite their widespread use, little is known about the genetic diversity of SD. We genotyped more than 4,000 SD rats, which we used for a genome-wide association study (GWAS) and to characterize genetic differences between SD rats from Charles River Laboratories and Harlan. Our analysis revealed extensive population structure both between and within vendors. The GWAS for Pavlovian conditioned approach (PavCA) identified a number of genome-wide significant loci for that complex behavioral trait. Our results demonstrate that, despite sharing an identical name, SD rats that are obtained from different vendors are very different. Future studies should carefully define the exact source of SD rats being used and may exploit their genetic diversity for genetic studies of complex traits.
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294
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Wade KH, Yarmolinsky J, Giovannucci E, Lewis SJ, Millwood IY, Munafò MR, Meddens F, Burrows K, Bell JA, Davies NM, Mariosa D, Kanerva N, Vincent EE, Smith-Byrne K, Guida F, Gunter MJ, Sanderson E, Dudbridge F, Burgess S, Cornelis MC, Richardson TG, Borges MC, Bowden J, Hemani G, Cho Y, Spiller W, Richmond RC, Carter AR, Langdon R, Lawlor DA, Walters RG, Vimaleswaran KS, Anderson A, Sandu MR, Tilling K, Davey Smith G, Martin RM, Relton CL. Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer. Cancer Causes Control 2022; 33:631-652. [PMID: 35274198 PMCID: PMC9010389 DOI: 10.1007/s10552-022-01562-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 02/10/2022] [Indexed: 02/08/2023]
Abstract
Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.
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Affiliation(s)
- Kaitlin H Wade
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK.
| | - James Yarmolinsky
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Edward Giovannucci
- Departments of Nutrition and Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Sarah J Lewis
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) and the Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marcus R Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Fleur Meddens
- Department of Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Kimberley Burrows
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Joshua A Bell
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Neil M Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniela Mariosa
- International Agency for Research On Cancer (IARC), Lyon, France
| | | | - Emma E Vincent
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, UK
| | - Karl Smith-Byrne
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Florence Guida
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Marc J Gunter
- International Agency for Research On Cancer (IARC), Lyon, France
| | - Eleanor Sanderson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Tom G Richardson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Jack Bowden
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Research Innovation Learning and Development (RILD) Building, University of Exeter Medical School, Exeter, UK
| | - Gibran Hemani
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Yoonsu Cho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Wes Spiller
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Rebecca C Richmond
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Alice R Carter
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Ryan Langdon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) and the Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Annie Anderson
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland, UK
| | - Meda R Sandu
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, Bristol, UK
| | - Kate Tilling
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - George Davey Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
| | - Caroline L Relton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU) at the University of Bristol, Bristol, UK
- Bristol National Institute for Health Research (NIHR) Biomedical Research Centre, Bristol, UK
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295
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Jung J, Kim H. Shared genetic etiology and antagonistic relationship of plasma renin activity and systolic blood pressure in a Korean cohorts. Genomics 2022; 114:110334. [PMID: 35278618 DOI: 10.1016/j.ygeno.2022.110334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 01/14/2023]
Abstract
Despite extensive studies on blood pressure, its genetic risk factors remain uncertain. Even one of the most researched blood pressure-related traits - renin - is not fully understood genetically. Here, we determine the genetic relationship and associated predisposition between blood pressure and baseline renin. In 8840 Korean individuals, we observed a strong negative genome-wide genetic correlation (rg = -0.484) between systolic blood pressure (SBP) and plasma renin activity (PRA), suggesting that antagonistic genetic signals explain the variance in the two traits. We found 51 significant pleiotropic SNPs affecting the two traits, which could contribute to the Renin-Angiotensin-Aldosterone System (RAAS). Our findings provide insight into studies on RAAS by identifying the genome-wide relationship and susceptibility loci of SBP and PRA.
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Affiliation(s)
- Jaehoon Jung
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Republic of Korea; eGnome, 26 Beobwon-ro, Songpa-gu, Seoul 05836, Republic of Korea.
| | - Heebal Kim
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Republic of Korea; eGnome, 26 Beobwon-ro, Songpa-gu, Seoul 05836, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea.
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296
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Speed D, Kaphle A, Balding DJ. SNP-based heritability and selection analyses: Improved models and new results. Bioessays 2022; 44:e2100170. [PMID: 35279859 DOI: 10.1002/bies.202100170] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 01/15/2023]
Abstract
Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.
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Affiliation(s)
- Doug Speed
- Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.,Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark.,UCL Genetics Institute, University College London, London, UK
| | - Anubhav Kaphle
- Melbourne Integrative Genomics, School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
| | - David J Balding
- UCL Genetics Institute, University College London, London, UK.,Melbourne Integrative Genomics, School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Victoria, Australia
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297
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Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, van der Zee MD, Cheesman R, Mangino M, Wang Y, Li S, Klaric L, Ratliff SM, Bielak LF, Nygaard M, Giannelis A, Willoughby EA, Reynolds CA, Balbona JV, Andreassen OA, Ask H, Baras A, Bauer CR, Boomsma DI, Campbell A, Campbell H, Chen Z, Christofidou P, Corfield E, Dahm CC, Dokuru DR, Evans LM, de Geus EJC, Giddaluru S, Gordon SD, Harden KP, Hill WD, Hughes A, Kerr SM, Kim Y, Kweon H, Latvala A, Lawlor DA, Li L, Lin K, Magnus P, Magnusson PKE, Mallard TT, Martikainen P, Mills MC, Njølstad PR, Overton JD, Pedersen NL, Porteous DJ, Reid J, Silventoinen K, Southey MC, Stoltenberg C, Tucker-Drob EM, Wright MJ, Hewitt JK, Keller MC, Stallings MC, Lee JJ, Christensen K, Kardia SLR, Peyser PA, Smith JA, Wilson JF, Hopper JL, Hägg S, Spector TD, Pingault JB, Plomin R, Havdahl A, Bartels M, Martin NG, Oskarsson S, Justice AE, Millwood IY, Hveem K, Naess Ø, Willer CJ, Åsvold BO, Koellinger PD, Kaprio J, Medland SE, Walters RG, Benjamin DJ, Turley P, Evans DM, Davey Smith G, Hayward C, Brumpton B, Hemani G, Davies NM. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022; 54:581-592. [PMID: 35534559 PMCID: PMC9110300 DOI: 10.1038/s41588-022-01062-7] [Citation(s) in RCA: 121] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 03/25/2022] [Indexed: 02/01/2023]
Abstract
Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Michel G Nivard
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Humaira Rasheed
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Teemu Palviainen
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Matthijs D van der Zee
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, 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
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | | | | | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Jared V Balbona
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Christopher R Bauer
- BioMarin Pharmaceutical Inc., Novato, CA, USA
- Biomedical and Translational Informatics, Geisinger Health, Danville, PA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D), Amsterdam, the Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - Elizabeth Corfield
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | | | - Deepika R Dokuru
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
- Department of Ecology & Evolutionary Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sudheer Giddaluru
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Scott D Gordon
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - W David Hill
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shona M Kerr
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Antti Latvala
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
- Institute of Criminology and Legal Policy, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Skøyen, Oslo, Norway
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Pekka Martikainen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
- The Max Planck Institute for Demographic Research, Rostock, Germany
- Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
| | - Melinda C Mills
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Pål Rasmus Njølstad
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Karri Silventoinen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | | | | | - John K Hewitt
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Matthew C Keller
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - James J Lee
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jean-Baptiste Pingault
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Robert Plomin
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alexandra Havdahl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Meike Bartels
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Øyvind Naess
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Cristen J Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ben Brumpton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
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298
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Childhood body size directly increases type 1 diabetes risk based on a lifecourse Mendelian randomization approach. Nat Commun 2022; 13:2337. [PMID: 35484151 PMCID: PMC9051135 DOI: 10.1038/s41467-022-29932-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 04/08/2022] [Indexed: 12/14/2022] Open
Abstract
The rising prevalence of childhood obesity has been postulated as an explanation for the increasing rate of individuals diagnosed with type 1 diabetes (T1D). In this study, we use Mendelian randomization (MR) to provide evidence that childhood body size has an effect on T1D risk (OR = 2.05 per change in body size category, 95% CI = 1.20 to 3.50, P = 0.008), which remains after accounting for body size at birth and during adulthood using multivariable MR (OR = 2.32, 95% CI = 1.21 to 4.42, P = 0.013). We validate this direct effect of childhood body size using data from a large-scale T1D meta-analysis based on n = 15,573 cases and n = 158,408 controls (OR = 1.94, 95% CI = 1.21 to 3.12, P = 0.006). We also provide evidence that childhood body size influences risk of asthma, eczema and hypothyroidism, although multivariable MR suggested that these effects are mediated by body size in later life. Our findings support a causal role for higher childhood body size on risk of being diagnosed with T1D, whereas its influence on the other immune-associated diseases is likely explained by a long-term effect of remaining overweight for many years over the lifecourse. The rise in type 1 diabetes is thought to be related to increased childhood obesity, but this relationship is not well understood. In this study, the authors utilize Mendelian randomization to separate the direct and indirect effects of childhood body size on risk of type 1 diabetes and 7 other immune-associated disease outcomes.
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299
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Said S, Pazoki R, Karhunen V, Võsa U, Ligthart S, Bodinier B, Koskeridis F, Welsh P, Alizadeh BZ, Chasman DI, Sattar N, Chadeau-Hyam M, Evangelou E, Jarvelin MR, Elliott P, Tzoulaki I, Dehghan A. Genetic analysis of over half a million people characterises C-reactive protein loci. Nat Commun 2022; 13:2198. [PMID: 35459240 PMCID: PMC9033829 DOI: 10.1038/s41467-022-29650-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 03/25/2022] [Indexed: 01/08/2023] Open
Abstract
Chronic low-grade inflammation is linked to a multitude of chronic diseases. We report the largest genome-wide association study (GWAS) on C-reactive protein (CRP), a marker of systemic inflammation, in UK Biobank participants (N = 427,367, European descent) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (total N = 575,531 European descent). We identify 266 independent loci, of which 211 are not previously reported. Gene-set analysis highlighted 42 gene sets associated with CRP levels (p ≤ 3.2 ×10-6) and tissue expression analysis indicated a strong association of CRP related genes with liver and whole blood gene expression. Phenome-wide association study identified 27 clinical outcomes associated with genetically determined CRP and subsequent Mendelian randomisation analyses supported a causal association with schizophrenia, chronic airway obstruction and prostate cancer. Our findings identified genetic loci and functional properties of chronic low-grade inflammation and provided evidence for causal associations with a range of diseases.
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Affiliation(s)
- Saredo Said
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Raha Pazoki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Cardiovascular and Metabolic Research Group, Department of Life Sciences, Brunel University London, London, UK
- The Centre for Inflammation Research and Translational Medicine (CIRTM), Brunel University London, London, UK
- Centre for Health and Well-being Across the Life Course, Brunel University London, London, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Life Course Health Research, University of Oulu, Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Symen Ligthart
- Department of Intensive Care, University Hospital Antwerp, Antwerp, Belgium
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Fotios Koskeridis
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Paul Welsh
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, UK
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University of Groningen and University Medical Centre Groningen, Groningen, the Netherlands
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham & Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, UK
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Centre for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute at Imperial College London, Burlington Danes Building, Hammersmith Hospital, DuCane Road, London, W12 0NN, UK
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, London, W2 1PG, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute at Imperial College London, Burlington Danes Building, Hammersmith Hospital, DuCane Road, London, W12 0NN, UK
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK.
- UK Dementia Research Institute at Imperial College London, Burlington Danes Building, Hammersmith Hospital, DuCane Road, London, W12 0NN, UK.
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300
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He W, Han X, Ong JS, Hewitt AW, Mackey DA, Gharahkhani P, MacGregor S. Association of Novel Loci With Keratoconus Susceptibility in a Multitrait Genome-Wide Association Study of the UK Biobank Database and Canadian Longitudinal Study on Aging. JAMA Ophthalmol 2022; 140:568-576. [PMID: 35446358 PMCID: PMC9026225 DOI: 10.1001/jamaophthalmol.2022.0891] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Keratoconus can be a debilitating corneal ectasia in which the cornea thins, bulges, and steepens into a conical shape. Early features of keratoconus include myopia and irregular astigmatism, which affect vision and can be treated with contact lenses, collagen cross-linking, or, in advanced cases, corneal transplant. Recent estimates of the prevalence of keratoconus based on results of Scheimpflug imaging in young adults are as high as 1.2%. However, obtaining very large keratoconus data sets for a genome-wide association study (GWAS) is problematic because few population studies include Scheimpflug imaging and because severe keratoconus is relatively rare. Objective To identify novel keratoconus loci using corneal resistance factor (CRF) and central corneal thickness (CCT). Design, Setting, and Participants This multitrait GWAS used European ancestry CRF data from UK Biobank (UKB) (n = 105 427) and the Canadian Longitudinal Study on Aging (CLSA) (n = 18 307) and European ancestry CCT data from the International Glaucoma Genetics Consortium (IGGC) (n = 17 803). The CRF and CCT variants in published keratoconus data sets (4669 cases and 116 547 controls) were compared. The data set from UKB was compiled March 24, 2020; data were released from the CLSA in July 2020; and IGGC data were available from May 1, 2018. Main Outcomes and Measures Association of CRF and CCT variants with keratoconus risk. Results The GWAS included 4 cohorts: 105 427 UKB European ancestry (56 134 women [53.2%] and 49 293 men [46.7%]; mean [SD] age, 57 [8] years), 5029 UKB South Asian ancestry (2368 women [47.1%] and 2661 men [52.9%]; mean [SD] age, 54 [8] years), 902 UKB East Asian ancestry (622 women [68.9%] and 280 men [31.0%]; mean [SD] age, 53 [8] years), and 18 307 CLSA European ancestry (9260 women [50.6%] and 9047 men [49.4%]; mean [SD] age, 63 [10] years) participants. A total of 369 CRF and 233 CCT loci were identified, including 36 novel CRF loci and 114 novel CCT loci. Twenty-nine CRF loci and 24 CCT loci were associated with keratoconus. Polygenic risk scores (PRS) were constructed using CRF- and CCT-associated variants and published keratoconus variants. The PRS result showed that adding a CRF- or CCT-based PRS to the keratoconus PRS from previously published variants improved the prediction area under the receiver operating characteristic curve (from 0.705 to 0.756 for CRF and from 0.715 to 0.755 for CCT). Conclusions and Relevance These findings support the use of multitrait modeling of corneal parameters in a relatively large data set to identify new keratoconus risk loci and enhance polygenic risk score models.
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Affiliation(s)
- Weixiong He
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Medicine, University of Queensland, Brisbane, Australia
| | - Xikun Han
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Medicine, University of Queensland, Brisbane, Australia
| | - Jue-Sheng Ong
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Alex W Hewitt
- Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia.,School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - David A Mackey
- Lions Eye Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth, Australia
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