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Nabais MF, Laws SM, Lin T, Vallerga CL, Armstrong NJ, Blair IP, Kwok JB, Mather KA, Mellick GD, Sachdev PS, Wallace L, Henders AK, Zwamborn RAJ, Hop PJ, Lunnon K, Pishva E, Roubroeks JAY, Soininen H, Tsolaki M, Mecocci P, Lovestone S, Kłoszewska I, Vellas B, Furlong S, Garton FC, Henderson RD, Mathers S, McCombe PA, Needham M, Ngo ST, Nicholson G, Pamphlett R, Rowe DB, Steyn FJ, Williams KL, Anderson TJ, Bentley SR, Dalrymple-Alford J, Fowder J, Gratten J, Halliday G, Hickie IB, Kennedy M, Lewis SJG, Montgomery GW, Pearson J, Pitcher TL, Silburn P, Zhang F, Visscher PM, Yang J, Stevenson AJ, Hillary RF, Marioni RE, Harris SE, Deary IJ, Jones AR, Shatunov A, Iacoangeli A, van Rheenen W, van den Berg LH, Shaw PJ, Shaw CE, Morrison KE, Al-Chalabi A, Veldink JH, Hannon E, Mill J, Wray NR, McRae AF. Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders. Genome Biol 2021; 22:90. [PMID: 33771206 PMCID: PMC8004462 DOI: 10.1186/s13059-021-02275-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 01/20/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease. RESULTS We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson's disease (and none with Alzheimer's disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. CONCLUSIONS We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.
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Grants
- U24 AG021886 NIA NIH HHS
- U01 AG016976 NIA NIH HHS
- Department of Health
- U01 AG024904 NIA NIH HHS
- 108890/Z/15/Z Wellcome Trust
- 503480 Medical Research Council
- TURNER/OCT15/972-797 Motor Neurone Disease Association
- U01 AG032984 NIA NIH HHS
- R01 HL105756 NHLBI NIH HHS
- 082604/2/07/Z Wellcome Trust
- R01 AG033193 NIA NIH HHS
- National Health and Medical Research Council
- Motor Neurone Disease Research Institute of Australia Ice Bucket Challenge
- Medical Research Council (UK)
- Economic and Social Research Council
- National Institute for Health Research (NIHR)
- the European Community’s Health Seventh Framework Programme
- Horizon 2020 Programme
- MND Association and the Wellcome Trust.
- European Research Council (ERC)
- EU Joint Programme – Neurodegenerative Disease Research ()
- EU Joint Programme - Neurodegenerative Disease Research (JPND)
- Australian Research Council
- Mater Foundation
- ForeFront - NHMRC
- Australian National Health and Medical Research Council
- University of Otago Research Grant, together with financial support from the Jim and Mary Carney Charitable Trust
- Commonwealth Scientific Industrial and research Organization (CSIRO), Edith Cowan University (ECU), Mental Health Research institute (MHRI), National Ageing Research Institute (NARI), Austin Health, CogState Ltd
- National Health and Medical Research Council and the Dementia Collaborative Research Centres program (DCRC2), as well as funding from the Science and Industry Endowment Fund (SIEF) and the Cooperative Research Centre (CRC) for Mental Health – funded throug
- EU Joint Programme - Neurodegenerative Disease Research (JPND), co-funded through the Australian National Health and Medical Research (NHMRC) Council, Motor Neurone Disease Research Institute of Australia Ice Bucket Challenge,
- EU Joint Programme - Neurodegenerative Disease Research (JPND), United Kingdom Medical Research Council, Economic and Social Research Council, Motor Neuro Disease Association (GB), National Institute for Health Research (NIHR) Biomedical Research Centre at
- EU Joint Programme - Neurodegenerative Disease Research (JPND), European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program, PPP Allowance made available by Health~Holland, Top Sector Life Sciences & Health, Unit
- National Health and Medical Research Council, Australian Research Council, Mater Foundation,
- Australian National Health and Medical Research Council (
- University of Otago Research Grant, Jim and Mary Carney Charitable Trust
- Commonwealth Scientific Industrial and research Organization (CSIRO), Edith Cowan University (ECU), Mental Health Research institute (MHRI), National Ageing Research Institute (NARI), Austin Health, CogState Ltd., National Health and Medical Research Counc
- EFPIA companies and SMEs as part of InnoMed (Innovative Medicines in Europe), an Integrated Project funded by the European Union of the Sixth Framework program
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Affiliation(s)
- Marta F Nabais
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Simon M Laws
- School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Dr, Joondalup, WA, 6027, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Costanza L Vallerga
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Department of Internal Medicine, Erasmus MC, University Medical Center, 3015GD, Rotterdam, The Netherlands
| | | | - Ian P Blair
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, SA, 5001, Australia
| | - John B Kwok
- Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2031, Australia
- Neuroscience Research Australia Institute, Randwick, NSW, 2031, Australia
| | - George D Mellick
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2031, Australia
- Neuropsychiatric Institute, The Prince of Wales Hospital, UNSW, Randwick, NSW, 2031, Australia
| | - Leanne Wallace
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Anjali K Henders
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ramona A J Zwamborn
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Paul J Hop
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Katie Lunnon
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Ehsan Pishva
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Janou A Y Roubroeks
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Magda Tsolaki
- 1st Department of Neurology, Memory and Dementia Unit, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Patrizia Mecocci
- Department of Medicine, Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Simon Lovestone
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | | | - Bruno Vellas
- INSERM U 558, University of Toulouse, Toulouse, France
| | - Sarah Furlong
- Centre for Motor Neuron Disease Research, Macquarie University, Sydney, NSW, 2109, Australia
| | - Fleur C Garton
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Robert D Henderson
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4019, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, 4029, Australia
| | - Susan Mathers
- Calvary Health Care Bethlehem, Parkdale, VIC, 3195, Australia
| | - Pamela A McCombe
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4019, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, 4029, Australia
| | - Merrilee Needham
- Fiona Stanley Hospital, Perth, WA, 6150, Australia
- Notre Dame University, Fremantle, WA, 6160, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, 6150, Australia
| | - Shyuan T Ngo
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4019, Australia
- The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Garth Nicholson
- ANZAC Research Institute, Concord Repatriation General Hospital, Sydney, NSW, 2139, Australia
| | - Roger Pamphlett
- Discipline of Pathology and Department of Neuropathology, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2050, Australia
| | - Dominic B Rowe
- Centre for Motor Neuron Disease Research, Macquarie University, Sydney, NSW, 2109, Australia
| | - Frederik J Steyn
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, 4029, Australia
- School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Kelly L Williams
- Centre for Motor Neuron Disease Research, Macquarie University, Sydney, NSW, 2109, Australia
| | - Tim J Anderson
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Steven R Bentley
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Australia
| | - John Dalrymple-Alford
- New Zealand Brain Research Institute, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Javed Fowder
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Jacob Gratten
- Mater Research, Translational Research Institute, Brisbane, Australia
- Mater Research Institute, The University of Queensland, Brisbane, Australia
| | - Glenda Halliday
- Brain and Mind Research Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Ian B Hickie
- Brain and Mind Research Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Martin Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Simon J G Lewis
- Brain and Mind Research Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - John Pearson
- Department of Pathology, University of Otago, Christchurch, New Zealand
| | - Toni L Pitcher
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Peter Silburn
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Sarah E Harris
- Department of Psychology, Lothian Birth Cohorts group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Ian J Deary
- Department of Psychology, Lothian Birth Cohorts group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Ashley R Jones
- Department of Basic and Clinical Neuroscience, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 9RX, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 9RX, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 9RX, UK
| | - Wouter van Rheenen
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | | | - Cristopher E Shaw
- Department of Basic and Clinical Neuroscience, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 9RX, UK
| | | | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 9RX, UK
- King's College Hospital, London, SE5 9RS, UK
| | - Jan H Veldink
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Eilis Hannon
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Jonathan Mill
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
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52
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Guo J, Bakshi A, Wang Y, Jiang L, Yengo L, Goddard ME, Visscher PM, Yang J. Quantifying genetic heterogeneity between continental populations for human height and body mass index. Sci Rep 2021; 11:5240. [PMID: 33664403 PMCID: PMC7933291 DOI: 10.1038/s41598-021-84739-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/22/2021] [Indexed: 12/26/2022] Open
Abstract
Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (\documentclass[12pt]{minimal}
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\begin{document}$$r_{g}$$\end{document}rg) or genome-wide significant SNPs (\documentclass[12pt]{minimal}
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\begin{document}$$r_{{g\left( {GWS} \right)}}$$\end{document}rgGWS) for height and body mass index (BMI) in samples of European (EUR; \documentclass[12pt]{minimal}
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\begin{document}$$n = 49,839$$\end{document}n=49,839) and African (AFR; \documentclass[12pt]{minimal}
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\begin{document}$$n = 17,426$$\end{document}n=17,426) ancestry. The \documentclass[12pt]{minimal}
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\begin{document}$$\hat{r}_{g}$$\end{document}r^g between EUR and AFR was 0.75 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.035$$\end{document}s.e.=0.035) for height and 0.68 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.062$$\end{document}s.e.=0.062) for BMI, and the corresponding \documentclass[12pt]{minimal}
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\begin{document}$$\hat{r}_{{g\left( {GWS} \right)}}$$\end{document}r^gGWS was 0.82 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.030$$\end{document}s.e.=0.030) for height and 0.87 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.064$$\end{document}s.e.=0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that \documentclass[12pt]{minimal}
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\begin{document}$$\hat{r}_{g}$$\end{document}r^g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.
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Affiliation(s)
- Jing Guo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
| | - Andrew Bakshi
- Monash Partners Comprehensive Cancer Consortium, Monash Biomedicine Discovery Institute Cancer Program, Prostate Cancer Research Group, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,School of Life Sciences, Westlake University, Hangzhou, 310024, Zhejiang, China. .,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, Zhejiang, China.
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53
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Wu Y, Murray GK, Byrne EM, Sidorenko J, Visscher PM, Wray NR. GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression. Nat Commun 2021; 12:1146. [PMID: 33608531 PMCID: PMC7895976 DOI: 10.1038/s41467-021-21280-7] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/06/2021] [Indexed: 01/31/2023] Open
Abstract
Genetic factors are recognized to contribute to peptic ulcer disease (PUD) and other gastrointestinal diseases, such as gastro-oesophageal reflux disease (GORD), irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). Here, genome-wide association study (GWAS) analyses based on 456,327 UK Biobank (UKB) individuals identify 8 independent and significant loci for PUD at, or near, genes MUC1, MUC6, FUT2, PSCA, ABO, CDX2, GAST and CCKBR. There are previously established roles in susceptibility to Helicobacter pylori infection, response to counteract infection-related damage, gastric acid secretion or gastrointestinal motility for these genes. Only two associations have been previously reported for duodenal ulcer, here replicated trans-ancestrally. The results highlight the role of host genetic susceptibility to infection. Post-GWAS analyses for PUD, GORD, IBS and IBD add insights into relationships between these gastrointestinal diseases and their relationships with depression, a commonly comorbid disorder.
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Affiliation(s)
- Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
| | - Graham K Murray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
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54
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Stevenson AJ, Gadd DA, Hillary RF, McCartney DL, Campbell A, Walker RM, Evans KL, Harris SE, Spires-Jones TL, McRae AF, Visscher PM, McIntosh AM, Deary IJ, Marioni RE. Creating and validating a DNA methylation-based proxy for interleukin-6. J Gerontol A Biol Sci Med Sci 2021; 76:2284-2292. [PMID: 33595649 PMCID: PMC8599002 DOI: 10.1093/gerona/glab046] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Indexed: 01/28/2023] Open
Abstract
Background Studies evaluating the relationship between chronic inflammation and cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals’ persisting levels of inflammation. DNA methylation (DNAm) has shown utility in indexing environmental exposures and could be leveraged to provide proxy signatures of chronic inflammation. Method We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 875 older adults (Lothian Birth Cohort 1936; mean age: 70 years) to develop a DNAm-based predictor. The predictor was tested in an independent cohort (Generation Scotland; N = 7028 [417 with measured IL-6], mean age: 51 years). Results A weighted score from 35 CpG sites optimally predicted IL-6 in the independent test set (Generation Scotland; R2 = 4.4%, p = 2.1 × 10−5). In the independent test cohort, both measured IL-6 and the DNAm proxy increased with age (serum IL-6: n = 417, β = 0.02, SE = 0.004, p = 1.3 × 10−7; DNAm IL-6 score: N = 7028, β = 0.02, SE = 0.0009, p < 2 × 10−16). Serum IL-6 did not associate with cognitive ability (n = 417, β = −0.06, SE = 0.05, p = .19); however, an inverse association was identified between the DNAm score and cognitive functioning (N = 7028, β = −0.16, SE = 0.02, pFDR < 2 × 10−16). Conclusions These results suggest methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for further insight into the relationship between inflammation and pertinent health outcomes.
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Affiliation(s)
- Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, Chancellor's Building, Little France Crescent, Edinburgh BioQuarter, Edinburgh
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tara L Spires-Jones
- UK Dementia Research Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.,Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
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Kemper KE, Yengo L, Zheng Z, Abdellaoui A, Keller MC, Goddard ME, Wray NR, Yang J, Visscher PM. Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals. Nat Commun 2021; 12:1050. [PMID: 33594080 PMCID: PMC7886899 DOI: 10.1038/s41467-021-21283-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 12/10/2020] [Indexed: 01/18/2023] Open
Abstract
Attributing the similarity between individuals to genetic and non-genetic factors is central to genetic analyses. In this paper we use the genomic relationship ([Formula: see text]) among 417,060 individuals to investigate the phenotypic covariance between pairs of individuals for 32 traits across the spectrum of relatedness, from unrelated pairs through to identical twins. We find linear relationships between phenotypic covariance and [Formula: see text] that agree with the SNP-based heritability ([Formula: see text]) in unrelated pairs ([Formula: see text]), and with pedigree-estimated heritability in close relatives ([Formula: see text]). The covariance increases faster than [Formula: see text] in distant relatives ([Formula: see text]), and we attribute this to imperfect linkage disequilibrium between causal variants and the common variants used to construct [Formula: see text]. We also examine the effect of assortative mating on heritability estimates from different experimental designs. We find that full-sib identity-by-descent regression estimates for height (0.66 s.e. 0.07) are consistent with estimates from close relatives (0.82 s.e. 0.04) after accounting for the effect of assortative mating.
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Affiliation(s)
- Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - 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 E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia. .,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
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56
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Yap CX, Alvares GA, Henders AK, Lin T, Wallace L, Farrelly A, McLaren T, Berry J, Vinkhuyzen AAE, Trzaskowski M, Zeng J, Yang Y, Cleary D, Grove R, Hafekost C, Harun A, Holdsworth H, Jellett R, Khan F, Lawson L, Leslie J, Levis Frenk M, Masi A, Mathew NE, Muniandy M, Nothard M, Visscher PM, Dawson PA, Dissanayake C, Eapen V, Heussler HS, Whitehouse AJO, Wray NR, Gratten J. Analysis of common genetic variation and rare CNVs in the Australian Autism Biobank. Mol Autism 2021; 12:12. [PMID: 33568206 PMCID: PMC7874616 DOI: 10.1186/s13229-020-00407-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/17/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism. METHODS Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals (after quality control) from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European (n = 1,964 European individuals). We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain. RESULTS The ASD (p = 6.1e-13), sibling (p = 4.9e-3) and unrelated (p = 3.0e-3) groups had significantly higher ASD PGS than UK Biobank controls, whereas this was not the case for height-a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children (r = 0.24, p = 2.1e-3) and parents (r = 0.17, p = 8.0e-7; 4.0% of variance), but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group (r = 0.13, p = 1.9e-3; 1.3% of variance). In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identified on the basis of de novo variants. LIMITATIONS This dataset is modest in size, and the publicly-available genome-wide-association-study (GWAS) summary statistics used to calculate PGS for ASD and other traits are relatively underpowered. CONCLUSIONS We report on common genetic variation and rare CNVs within the AAB. Prediction analyses using currently available GWAS summary statistics are largely consistent with expected relationships based on published studies. As the size of publicly-available GWAS summary statistics grows, the phenotypic depth of the AAB dataset will provide many opportunities for analyses of autism profiles and co-occurring conditions, including when integrated with other omics datasets generated from AAB biospecimens (blood, urine, stool, hair).
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Affiliation(s)
- Chloe X Yap
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
| | - Gail A Alvares
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Anjali K Henders
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Leanne Wallace
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Alaina Farrelly
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Tiana McLaren
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Jolene Berry
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anna A E Vinkhuyzen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Maciej Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Max Kelsen, Fortitude Valley, QLD, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Yuanhao Yang
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Dominique Cleary
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Rachel Grove
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Claire Hafekost
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Alexis Harun
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Helen Holdsworth
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Rachel Jellett
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
| | - Feroza Khan
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Lauren Lawson
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
| | - Jodie Leslie
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Mira Levis Frenk
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Anne Masi
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Nisha E Mathew
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Melanie Muniandy
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
| | - Michaela Nothard
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Paul A Dawson
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
| | - Cheryl Dissanayake
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, VIC, Australia
| | - Valsamma Eapen
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Academic Unit of Child Psychiatry South West Sydney, Ingham Institute, Liverpool Hospital, Sydney, NSW, Australia
| | - Helen S Heussler
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
- Child Development Program, Children's Health Queensland, Brisbane, QLD, Australia
| | - Andrew J O Whitehouse
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Jacob Gratten
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
- Cooperative Research Centre for Living With Autism (Autism CRC), Long Pocket, Brisbane, QLD, Australia.
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Xue A, Jiang L, Zhu Z, Wray NR, Visscher PM, Zeng J, Yang J. Publisher Correction: Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes. Nat Commun 2021; 12:988. [PMID: 33558551 PMCID: PMC7870948 DOI: 10.1038/s41467-021-21294-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21294-1.
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Affiliation(s)
- Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,School of Life Sciences, Westlake University, Hangzhou, Zhejiang, 310024, China. .,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China.
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58
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Palmer RHC, Benca-Bachman CE, Huggett SB, Bubier JA, McGeary JE, Ramgiri N, Srijeyanthan J, Yang J, Visscher PM, Yang J, Knopik VS, Chesler EJ. Multi-omic and multi-species meta-analyses of nicotine consumption. Transl Psychiatry 2021; 11:98. [PMID: 33542196 PMCID: PMC7862377 DOI: 10.1038/s41398-021-01231-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 12/15/2020] [Accepted: 12/17/2020] [Indexed: 12/23/2022] Open
Abstract
Cross-species translational approaches to human genomic analyses are lacking. The present study uses an integrative framework to investigate how genes associated with nicotine use in model organisms contribute to the genetic architecture of human tobacco consumption. First, we created a model organism geneset by collecting results from five animal models of nicotine exposure (RNA expression changes in brain) and then tested the relevance of these genes and flanking genetic variation using genetic data from human cigarettes per day (UK BioBank N = 123,844; all European Ancestry). We tested three hypotheses: (1) DNA variation in, or around, the 'model organism geneset' will contribute to the heritability to human tobacco consumption, (2) that the model organism genes will be enriched for genes associated with human tobacco consumption, and (3) that a polygenic score based off our model organism geneset will predict tobacco consumption in the AddHealth sample (N = 1667; all European Ancestry). Our results suggested that: (1) model organism genes accounted for ~5-36% of the observed SNP-heritability in human tobacco consumption (enrichment: 1.60-31.45), (2) model organism genes, but not negative control genes, were enriched for the gene-based associations (MAGMA, H-MAGMA, SMultiXcan) for human cigarettes per day, and (3) polygenic scores based on our model organism geneset predicted cigarettes per day in an independent sample. Altogether, these findings highlight the advantages of using multiple species evidence to isolate genetic factors to better understand the etiological complexity of tobacco and other nicotine consumption.
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Affiliation(s)
- Rohan H. C. Palmer
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Chelsie E. Benca-Bachman
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Spencer B. Huggett
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Jason A. Bubier
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory, Bar Harbor, ME USA
| | - John E. McGeary
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Brown University, Providence, RI USA ,grid.413904.b0000 0004 0420 4094Providence Veterans Affairs Medical Center, Providence, RI USA
| | - Nikhil Ramgiri
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Jenani Srijeyanthan
- grid.189967.80000 0001 0941 6502Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA USA
| | - Jingjing Yang
- grid.189967.80000 0001 0941 6502Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA
| | - Peter M. Visscher
- grid.1003.20000 0000 9320 7537Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD Australia
| | - Jian Yang
- grid.1003.20000 0000 9320 7537Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD Australia
| | - Valerie S. Knopik
- grid.169077.e0000 0004 1937 2197Department of Human Development and Family Studies, Purdue University, West Lafayette, IN USA
| | - Elissa J. Chesler
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory, Bar Harbor, ME USA
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Xue A, Jiang L, Zhu Z, Wray NR, Visscher PM, Zeng J, Yang J. Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes. Nat Commun 2021; 12:20211. [PMID: 33436567 PMCID: PMC7804181 DOI: 10.1038/s41467-020-20237-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/20/2020] [Indexed: 01/28/2023] Open
Abstract
Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank (n = 455,607) are more likely to misreport or reduce their alcohol consumption levels, and propose a correction procedure to mitigate the MLC-induced biases. The alcohol consumption GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between alcohol consumption and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of alcohol consumption on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.
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Affiliation(s)
- Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, 310024, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China.
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60
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Mur J, McCartney DL, Chasman DI, Visscher PM, Muniz-Terrera G, Cox SR, Russ TC, Marioni RE. Variation in VKORC1 Is Associated with Vascular Dementia. J Alzheimers Dis 2021; 80:1329-1337. [PMID: 33682710 PMCID: PMC8150662 DOI: 10.3233/jad-201256] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND The genetic variant rs9923231 (VKORC1) is associated with differences in the coagulation of blood and consequentially with sensitivity to the drug warfarin. Variation in VKORC1 has been linked in a gene-based test to dementia/Alzheimer's disease in the parents of participants, with suggestive evidence for an association for rs9923231 (p = 1.8×10-7), which was included in the genome-wide significant KAT8 locus. OBJECTIVE Our study aimed to investigate whether the relationship between rs9923231 and dementia persists only for certain dementia sub-types, and if those taking warfarin are at greater risk. METHODS We used logistic regression and data from 238,195 participants from UK Biobank to examine the relationship between VKORC1, risk of dementia, and the interplay with warfarin use. RESULTS Parental history of dementia, APOE variant, atrial fibrillation, diabetes, hypertension, and hypercholesterolemia all had strong associations with vascular dementia (p < 4.6×10-6). The T-allele in rs9923231 was linked to a lower warfarin dose (βperT - allele = -0.29, p < 2×10-16) and risk of vascular dementia (OR = 1.17, p = 0.010), but not other dementia sub-types. However, the risk of vascular dementia was not affected by warfarin use in carriers of the T-allele. CONCLUSION Our study reports for the first time an association between rs9923231 and vascular dementia, but further research is warranted to explore potential mechanisms and specify the relationship between rs9923231 and features of vascular dementia.
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Affiliation(s)
- Jure Mur
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Daniel L. McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, MA, USA
| | - Peter M. Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Simon R. Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tom C. Russ
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Science, University of Edinburgh, Edinburgh, UK
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
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61
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Abstract
IMPORTANCE Polygenic risk scores (PRS) are predictors of the genetic susceptibilities of individuals to diseases. All individuals have DNA risk variants for all common diseases, but genetic susceptibility differences between people reflect the cumulative burden of these. Polygenic risk scores for an individual are calculated as weighted counts of thousands of risk variants that they carry, where the risk variants and their weights have been identified in genome-wide association studies. Here, we review the underlying basic science of PRS, providing a foundation for understanding the potential clinical utility and limitations of PRS. OBSERVATIONS Polygenic risk scores can be calculated for a wide range of diseases from a saliva or blood sample using genotyping technologies that are inexpensive. While genotyping only needs to be done once for each individual in their lifetime, the PRS can be recalculated as identification of risk variants improves. On their own, PRS will never be able to establish or definitively predict future diagnoses of common complex conditions because genetic factors only contribute part of the risk, and PRS will only ever capture part of the genetic contributions. Nonetheless, just as clinical medicine uses a multitude of other predictive measures, PRS either on their own or as part of multivariable predictive algorithms could play a role. CONCLUSIONS AND RELEVANCE Utility of PRS in clinical medicine and ethical issues related to their use should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. For different diseases, PRS could have utility in community settings (stratification to better triage people into established screening programs) or could contribute to clinical decision-making for those presenting with symptoms but where formal diagnosis is unclear. In principle, PRS could contribute to treatment choices, but more data are needed to allow development of PRS in this context.
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Affiliation(s)
- Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jehannine Austin
- Departments of Psychiatry and Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada.,BC Mental Health and Substance Use Services Research Institute, Vancouver, British Columbia, Canada
| | - John J McGrath
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, Queensland, Australia.,National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Ian B Hickie
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England
| | - Graham K Murray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England.,Department of Psychiatry, University of Cambridge, Cambridge, England
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
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Banos DT, McCartney DL, Patxot M, Anchieri L, Battram T, Christiansen C, Costeira R, Walker RM, Morris SW, Campbell A, Zhang Q, Porteous DJ, McRae AF, Wray NR, Visscher PM, Haley CS, Evans KL, Deary IJ, McIntosh AM, Hemani G, Bell JT, Marioni RE, Robinson MR. Author Correction: Bayesian reassessment of the epigenetic architecture of complex traits. Nat Commun 2020; 11:5186. [PMID: 33037227 PMCID: PMC7547071 DOI: 10.1038/s41467-020-19099-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Daniel Trejo Banos
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Lucas Anchieri
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Thomas Battram
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Colette Christiansen
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Stewart W Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Qian Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Chris S Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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63
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Svardal H, Rusuwa B, Linderoth T, Kiran A, Charmantier A, Lattorff HMG, Cagan A, Ommeh SC, Kamng'ona A, Katongo C, Santos ME, Durbin R, Kumwenda B, Visscher PM, von der Heyden S. Evolutionary Genomics at the Human-Environment Interface in Africa. Mol Biol Evol 2020; 37:3076-3080. [PMID: 32442309 DOI: 10.1093/molbev/msaa132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We report on the first meeting of SMBE in Africa. SMBE Malawi was initiated to bring together African and international researchers who use genetics or genomics to study natural systems impacted by human activities. The goals of this conference were 1) to reach a world-class standard of science with a large number of contributions from Africa, 2) to initiate exchange between African and international researchers, and 3) to identify challenges and opportunities for evolutionary genomics research in Africa. As repored, we think that we have achieved these goals and make suggestions on the way forward for African evolutionary genomics research.
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Affiliation(s)
- Hannes Svardal
- Department of Biology, University of Antwerp, Antwerp, Belgium
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | - Bosco Rusuwa
- Department of Biological Sciences, Chancellor College, University of Malawi, Zomba, Malawi
| | - Tyler Linderoth
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Anmol Kiran
- Malawi-Liverpool Wellcome Trust Clinical Research Project, Blantyre, Malawi
| | - Anne Charmantier
- CEFE, Université de Montpellier, CNRS, Université de Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, France
| | | | - Alex Cagan
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Sheila C Ommeh
- Institute for Biotechnology Research, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Arox Kamng'ona
- Department of Biomedical Sciences, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Cyprian Katongo
- Department of Biological Sciences, University of Zambia, Lusaka, Zambia
| | - M Emília Santos
- Department of Zoology, University of Cambridge, Cambridge, United Kingdom
| | - Richard Durbin
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Benjamin Kumwenda
- Department of Biomedical Sciences, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Sophie von der Heyden
- Department of Botany and Zoology, University of Stellenbosch, Stellenbosch, South Africa
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64
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Couvy-Duchesne B, Strike LT, Zhang F, Holtz Y, Zheng Z, Kemper KE, Yengo L, Colliot O, Wright MJ, Wray NR, Yang J, Visscher PM. A unified framework for association and prediction from vertex-wise grey-matter structure. Hum Brain Mapp 2020; 41:4062-4076. [PMID: 32687259 PMCID: PMC7469763 DOI: 10.1002/hbm.25109] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/11/2020] [Accepted: 06/14/2020] [Indexed: 01/29/2023] Open
Abstract
The recent availability of large‐scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex‐wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey‐matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case–control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex‐wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex‐wise complexity; (b) comparison of the information retained by different MRI processings.
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Affiliation(s)
- Baptiste Couvy-Duchesne
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia
| | - Lachlan T Strike
- Queensland Brain Institute, the University of Queensland, St Lucia, Queensland, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia
| | - Yan Holtz
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.,Queensland Brain Institute, the University of Queensland, St Lucia, Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.,Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia
| | - Olivier Colliot
- ARAMIS, Inria, Paris, France.,ARAMIS, Paris Brain Institute, Paris, France.,ARAMIS, Inserm, Paris, France.,ARAMIS, CNRS, Paris, France.,ARAMIS, Sorbonne University, Paris, France
| | - Margaret J Wright
- Queensland Brain Institute, the University of Queensland, St Lucia, Queensland, Australia.,Centre for Advanced Imaging, the University of Queensland, St Lucia, Queensland, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.,Queensland Brain Institute, the University of Queensland, St Lucia, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.,Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland, Australia.,Queensland Brain Institute, the University of Queensland, St Lucia, Queensland, Australia
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65
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Zhang Q, Sidorenko J, Couvy-Duchesne B, Marioni RE, Wright MJ, Goate AM, Marcora E, Huang KL, Porter T, Laws SM, Sachdev PS, Mather KA, Armstrong NJ, Thalamuthu A, Brodaty H, Yengo L, Yang J, Wray NR, McRae AF, Visscher PM. Risk prediction of late-onset Alzheimer's disease implies an oligogenic architecture. Nat Commun 2020; 11:4799. [PMID: 32968074 PMCID: PMC7511365 DOI: 10.1038/s41467-020-18534-1] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/25/2020] [Indexed: 01/09/2023] Open
Abstract
Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer's disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.
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Affiliation(s)
- Qian Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Baptiste Couvy-Duchesne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Edoardo Marcora
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kuan-Lin Huang
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Tenielle Porter
- Collaborative Genomics Group, Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Simon M Laws
- Collaborative Genomics Group, Centre of Excellence for Alzheimer's Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - Nicola J Armstrong
- Department of Mathematics and Statistics, Murdoch University, Perth, WA, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
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66
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Raymond B, Yengo L, Costilla R, Schrooten C, Bouwman AC, Hayes BJ, Veerkamp RF, Visscher PM. Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock. PLoS Genet 2020; 16:e1008780. [PMID: 32925905 PMCID: PMC7514049 DOI: 10.1371/journal.pgen.1008780] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 09/24/2020] [Accepted: 07/21/2020] [Indexed: 01/13/2023] Open
Abstract
Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.
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Affiliation(s)
- Biaty Raymond
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Biometris, Wageningen University and Research, Wageningen, The Netherlands
- * E-mail:
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
| | - Roy Costilla
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
| | | | - Aniek C. Bouwman
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
| | - Roel F. Veerkamp
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Peter M. Visscher
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
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67
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Wang Y, Guo J, Ni G, Yang J, Visscher PM, Yengo L. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat Commun 2020; 11:3865. [PMID: 32737319 PMCID: PMC7395791 DOI: 10.1038/s41467-020-17719-y] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 07/10/2020] [Indexed: 02/03/2023] Open
Abstract
Polygenic scores (PGS) have been widely used to predict disease risk using variants identified from genome-wide association studies (GWAS). To date, most GWAS have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European ancestry populations. Here, we derive a theoretical model of the relative accuracy (RA) of PGS across ancestries. We show through extensive simulations that the RA of PGS based on genome-wide significant SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of causal SNP effects and heritability. We find that LD and MAF differences between ancestries can explain between 70 and 80% of the loss of RA of European-based PGS in African ancestry for traits like body mass index and type 2 diabetes. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWAS are mostly shared across continents.
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Affiliation(s)
- Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jing Guo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Guiyan Ni
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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68
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Colicino E, Marioni R, Ward-Caviness C, Gondalia R, Guan W, Chen B, Tsai PC, Huan T, Xu G, Golareh A, Schwartz J, Vokonas P, Just A, Starr JM, McRae AF, Wray NR, Visscher PM, Bressler J, Zhang W, Tanaka T, Moore AZ, Pilling LC, Zhang G, Stewart JD, Li Y, Hou L, Castillo-Fernandez J, Spector T, Kiel DP, Murabito JM, Liu C, Mendelson M, Assimes T, Absher D, Tsaho PS, Lu AT, Ferrucci L, Wilson R, Waldenberger M, Prokisch H, Bandinelli S, Bell JT, Levy D, Deary IJ, Horvath S, Pankow J, Peters A, Whitsel EA, Baccarelli A. Blood DNA methylation sites predict death risk in a longitudinal study of 12, 300 individuals. Aging (Albany NY) 2020; 12:14092-14124. [PMID: 32697766 PMCID: PMC7425458 DOI: 10.18632/aging.103408] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/25/2020] [Indexed: 12/22/2022]
Abstract
DNA methylation has fundamental roles in gene programming and aging that may help predict mortality. However, no large-scale study has investigated whether site-specific DNA methylation predicts all-cause mortality. We used the Illumina-HumanMethylation450-BeadChip to identify blood DNA methylation sites associated with all-cause mortality for 12, 300 participants in 12 Cohorts of the Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium. Over an average 10-year follow-up, there were 2,561 deaths across the cohorts. Nine sites mapping to three intergenic and six gene-specific regions were associated with mortality (P < 9.3x10-7) independently of age and other mortality predictors. Six sites (cg14866069, cg23666362, cg20045320, cg07839457, cg07677157, cg09615688)—mapping respectively to BMPR1B, MIR1973, IFITM3, NLRC5, and two intergenic regions—were associated with reduced mortality risk. The remaining three sites (cg17086398, cg12619262, cg18424841)—mapping respectively to SERINC2, CHST12, and an intergenic region—were associated with increased mortality risk. DNA methylation at each site predicted 5%–15% of all deaths. We also assessed the causal association of those sites to age-related chronic diseases by using Mendelian randomization, identifying weak causal relationship between cg18424841 and cg09615688 with coronary heart disease. Of the nine sites, three (cg20045320, cg07839457, cg07677157) were associated with lower incidence of heart disease risk and two (cg20045320, cg07839457) with smoking and inflammation in prior CHARGE analyses. Methylation of cg20045320, cg07839457, and cg17086398 was associated with decreased expression of nearby genes (IFITM3, IRF, NLRC5, MT1, MT2, MARCKSL1) linked to immune responses and cardiometabolic diseases. These sites may serve as useful clinical tools for mortality risk assessment and preventative care.
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Affiliation(s)
- Elena Colicino
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Riccardo Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Cavin Ward-Caviness
- US Environmental Protection Agency, Chapel Hill, NC 27514, USA.,Institute for Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg D-85764, Germany
| | - Rahul Gondalia
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Brian Chen
- Longitudinal Study Section, Translational Gerontology Branch, National Institute of Aging, Bethesda, MD 20892, USA
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Tianxiao Huan
- National Heart, Lung, and Blood Institute, Bethesda, MD 20892, USA
| | - Gao Xu
- Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - Agha Golareh
- Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - Joel Schwartz
- Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Pantel Vokonas
- VA Boston Healthcare System and Boston University Schools of Public Health and Medicine, Boston, MA 02115, USA
| | - Allan Just
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John M Starr
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Jan Bressler
- University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Wen Zhang
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Toshiko Tanaka
- Longitudinal Study Section, Translational Gerontology Branch, National Institute of Aging, Bethesda, MD 20892, USA
| | - Ann Zenobia Moore
- Longitudinal Study Section, Translational Gerontology Branch, National Institute of Aging, Bethesda, MD 20892, USA
| | - Luke C Pilling
- Epidemiology and Public Health Group, University of Exeter Medical School, Exeter, UK
| | - Guosheng Zhang
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
| | - James D Stewart
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Yun Li
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Lifang Hou
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Juan Castillo-Fernandez
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Douglas P Kiel
- Hebrew SeniorLife Institute for Aging Research and Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School Boston, MA 02215, USA
| | - Joanne M Murabito
- Section General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Chunyu Liu
- Boston University School of Public Health, Boston, MA 02215, USA
| | - Mike Mendelson
- Boston University School of Medicine, Boston, MA 02215, USA
| | - Tim Assimes
- Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Devin Absher
- Hudson Alpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Phil S Tsaho
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | | | - Rory Wilson
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg D-85764, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg D-85764, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg S-85764, Germany
| | | | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA 01702, USA
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jim Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Annette Peters
- Institute for Epidemiology II, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg D-85764, Germany
| | - Eric A Whitsel
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Andrea Baccarelli
- Columbia University Mailman School of Public Health, New York, NY 10032, USA
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69
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Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Genome Med 2020; 12:60. [PMID: 32641083 PMCID: PMC7346642 DOI: 10.1186/s13073-020-00754-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/10/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets. METHODS In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). RESULTS We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn's disease. CONCLUSIONS Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.
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Affiliation(s)
- Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Daniel Trejo-Banos
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Athanasios Kousathanas
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Sarah E Harris
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Sven Erik Ojavee
- Department of Computational Biology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Qian Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - David C Liewald
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, Austin, TX, 78712, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Matthew R Robinson
- Institute of Science and Technology Austria, 3400, Klosterneuburg, Austria.
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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70
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Revez JA, Lin T, Qiao Z, Xue A, Holtz Y, Zhu Z, Zeng J, Wang H, Sidorenko J, Kemper KE, Vinkhuyzen AAE, Frater J, Eyles D, Burne THJ, Mitchell B, Martin NG, Zhu G, Visscher PM, Yang J, Wray NR, McGrath JJ. Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration. Nat Commun 2020; 11:1647. [PMID: 32242144 PMCID: PMC7118120 DOI: 10.1038/s41467-020-15421-7] [Citation(s) in RCA: 185] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 03/03/2020] [Indexed: 02/07/2023] Open
Abstract
Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study.
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Affiliation(s)
- Joana A Revez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Zhen Qiao
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Yan Holtz
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Huanwei Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anna A E Vinkhuyzen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Julanne Frater
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Darryl Eyles
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Thomas H J Burne
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Brittany Mitchell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Gu Zhu
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
| | - John J McGrath
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia.
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
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71
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Kassam I, Wu Y, Yang J, Visscher PM, McRae AF. Tissue-specific sex differences in human gene expression. Hum Mol Genet 2020; 28:2976-2986. [PMID: 31044242 DOI: 10.1093/hmg/ddz090] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 04/12/2019] [Accepted: 04/24/2019] [Indexed: 02/07/2023] Open
Abstract
Despite extensive sex differences in human complex traits and disease, the male and female genomes differ only in the sex chromosomes. This implies that most sex-differentiated traits are the result of differences in the expression of genes that are common to both sexes. While sex differences in gene expression have been observed in a range of different tissues, the biological mechanisms for tissue-specific sex differences (TSSDs) in gene expression are not well understood. A total of 30 640 autosomal and 1021 X-linked transcripts were tested for heterogeneity in sex difference effect sizes in n = 617 individuals across 40 tissue types in Genotype-Tissue Expression (GTEx). This identified 65 autosomal and 66 X-linked TSSD transcripts (corresponding to unique genes) at a stringent significance threshold. Results for X-linked TSSD transcripts showed mainly concordant direction of sex differences across tissues and replicate previous findings. Autosomal TSSD transcripts had mainly discordant direction of sex differences across tissues. The top cis-expression quantitative trait loci (eQTLs) across tissues for autosomal TSSD transcripts are located a similar distance away from the nearest androgen and estrogen binding motifs and the nearest enhancer, as compared to cis-eQTLs for transcripts with stable sex differences in gene expression across tissue types. Enhancer regions that overlap top cis-eQTLs for TSSD transcripts, however, were found to be more dispersed across tissues. These observations suggest that androgen and estrogen regulatory elements in a cis region may play a common role in sex differences in gene expression, but TSSD in gene expression may additionally be due to causal variants located in tissue-specific enhancer regions.
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Affiliation(s)
| | - Yang Wu
- Institute for Molecular Bioscience
| | - Jian Yang
- Institute for Molecular Bioscience.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
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72
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Vallerga CL, Zhang F, Fowdar J, McRae AF, Qi T, Nabais MF, Zhang Q, Kassam I, Henders AK, Wallace L, Montgomery G, Chuang YH, Horvath S, Ritz B, Halliday G, Hickie I, Kwok JB, Pearson J, Pitcher T, Kennedy M, Bentley SR, Silburn PA, Yang J, Wray NR, Lewis SJG, Anderson T, Dalrymple-Alford J, Mellick GD, Visscher PM, Gratten J. Analysis of DNA methylation associates the cystine-glutamate antiporter SLC7A11 with risk of Parkinson's disease. Nat Commun 2020; 11:1238. [PMID: 32144264 PMCID: PMC7060318 DOI: 10.1038/s41467-020-15065-7] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 02/17/2020] [Indexed: 11/09/2022] Open
Abstract
An improved understanding of etiological mechanisms in Parkinson's disease (PD) is urgently needed because the number of affected individuals is projected to increase rapidly as populations age. We present results from a blood-based methylome-wide association study of PD involving meta-analysis of 229 K CpG probes in 1,132 cases and 999 controls from two independent cohorts. We identify two previously unreported epigenome-wide significant associations with PD, including cg06690548 on chromosome 4. We demonstrate that cg06690548 hypermethylation in PD is associated with down-regulation of the SLC7A11 gene and show this is consistent with an environmental exposure, as opposed to medications or genetic factors with effects on DNA methylation or gene expression. These findings are notable because SLC7A11 codes for a cysteine-glutamate anti-porter regulating levels of the antioxidant glutathione, and it is a known target of the environmental neurotoxin β-methylamino-L-alanine (BMAA). Our study identifies the SLC7A11 gene as a plausible biological target in PD.
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Affiliation(s)
- Costanza L Vallerga
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Javed Fowdar
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Ting Qi
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Marta F Nabais
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.,University of Exeter Medical School, Exeter EX2 5DW, Devon, UK
| | - Qian Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Irfahan Kassam
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Anjali K Henders
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Leanne Wallace
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Grant Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Yu-Hsuan Chuang
- Department of Epidemiology, Fielding School of Public Health, UCLA, Los Angeles, CA, USA
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA.,Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA, USA
| | - Beate Ritz
- Department of Epidemiology, Fielding School of Public Health, UCLA, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.,Department of Environmental Health, Fielding School of Public Health, UCLA, Los Angeles, CA, USA
| | - Glenda Halliday
- Brain and Mind Centre & Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Ian Hickie
- Brain and Mind Centre & Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - John B Kwok
- Brain and Mind Centre & Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.,School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - John Pearson
- Department of Pathology, University of Otago, Christchurch, New Zealand
| | - Toni Pitcher
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Martin Kennedy
- Department of Pathology, University of Otago, Christchurch, New Zealand
| | - Steven R Bentley
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Peter A Silburn
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Simon J G Lewis
- Brain and Mind Centre & Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Tim Anderson
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - John Dalrymple-Alford
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Psychology, University of Canterbury, Christchurch, New Zealand
| | - George D Mellick
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
| | - Jacob Gratten
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. .,Mater Research Institute, The University of Queensland, Brisbane, Australia.
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73
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Nabais MF, Lin T, Benyamin B, Williams KL, Garton FC, Vinkhuyzen AAE, Zhang F, Vallerga CL, Restuadi R, Freydenzon A, Zwamborn RAJ, Hop PJ, Robinson MR, Gratten J, Visscher PM, Hannon E, Mill J, Brown MA, Laing NG, Mather KA, Sachdev PS, Ngo ST, Steyn FJ, Wallace L, Henders AK, Needham M, Veldink JH, Mathers S, Nicholson G, Rowe DB, Henderson RD, McCombe PA, Pamphlett R, Yang J, Blair IP, McRae AF, Wray NR. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. NPJ Genom Med 2020; 5:10. [PMID: 32140259 PMCID: PMC7046630 DOI: 10.1038/s41525-020-0118-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 12/18/2019] [Indexed: 12/11/2022] Open
Abstract
We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion was included in the predictor.
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Affiliation(s)
- Marta F Nabais
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,2University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, Devon EX2 5DW UK
| | - Tian Lin
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Beben Benyamin
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,3Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, SA 5001 Australia
| | - Kelly L Williams
- 4Centre for Motor Neuron Disease Research, Macquarie University, Sydney, NSW 2109 Australia
| | - Fleur C Garton
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Anna A E Vinkhuyzen
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Futao Zhang
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Costanza L Vallerga
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Restuadi Restuadi
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Anna Freydenzon
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Ramona A J Zwamborn
- 5Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG Netherlands
| | - Paul J Hop
- 5Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG Netherlands
| | - Matthew R Robinson
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Jacob Gratten
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,6Mater Research Institute, The University of Queensland, Brisbane, QLD 4101 Australia
| | - Peter M Visscher
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,7Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Eilis Hannon
- 2University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, Devon EX2 5DW UK
| | - Jonathan Mill
- 2University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, Devon EX2 5DW UK.,8Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF UK
| | - Matthew A Brown
- 9Australian Translational Genomics Centre, Queensland University of Technology, Brisbane, QLD 4102 Australia
| | - Nigel G Laing
- 10The Centre for Medical Research, Faculty of Health and Medical Sciences, The University of Western Australia, Nedlands, WA 6009 Australia.,11Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, WA 6009 Australia
| | - Karen A Mather
- 12Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2031 Australia.,13Neuroscience Research Australia Institute, Randwick, NSW 2031 Australia
| | - Perminder S Sachdev
- 12Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2031 Australia.,14Neuropsychiatric Institute, The Prince of Wales Hospital, University of New South Wales, Randwick, NSW 2031 Australia
| | - Shyuan T Ngo
- 7Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia.,15The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072 Australia.,16Centre for Clinical Research, The University of Queensland, Brisbane, QLD 4019 Australia
| | - Frederik J Steyn
- 15The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072 Australia.,16Centre for Clinical Research, The University of Queensland, Brisbane, QLD 4019 Australia
| | - Leanne Wallace
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Anjali K Henders
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Merrilee Needham
- 17Fiona Stanley Hospital, Perth, WA 6150 Australia.,18The University of Notre Dame Australia, Fremantle, WA 6160 Australia.,19Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA 6150 Australia
| | - Jan H Veldink
- 5Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, 3584 CG Netherlands
| | - Susan Mathers
- 20Calvary Health Care Bethlehem, Parkdale, VIC 3195 Australia
| | - Garth Nicholson
- 21ANZAC Research Institute, Concord Repatriation General Hospital, Sydney, NSW 2139 Australia
| | - Dominic B Rowe
- 4Centre for Motor Neuron Disease Research, Macquarie University, Sydney, NSW 2109 Australia
| | - Robert D Henderson
- 7Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia.,16Centre for Clinical Research, The University of Queensland, Brisbane, QLD 4019 Australia.,22Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD 4029 Australia
| | - Pamela A McCombe
- 16Centre for Clinical Research, The University of Queensland, Brisbane, QLD 4019 Australia.,22Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD 4029 Australia
| | - Roger Pamphlett
- 23Discipline of Pathology and Department of Neuropathology, Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050 Australia
| | - Jian Yang
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,7Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Ian P Blair
- 4Centre for Motor Neuron Disease Research, Macquarie University, Sydney, NSW 2109 Australia
| | - Allan F McRae
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,7Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia
| | - Naomi R Wray
- 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia.,7Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072 Australia
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74
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Bond TA, Karhunen V, Wielscher M, Auvinen J, Männikkö M, Keinänen-Kiukaanniemi S, Gunter MJ, Felix JF, Prokopenko I, Yang J, Visscher PM, Evans DM, Sebert S, Lewin A, O’Reilly PF, Lawlor DA, Jarvelin MR. Exploring the role of genetic confounding in the association between maternal and offspring body mass index: evidence from three birth cohorts. Int J Epidemiol 2020; 49:233-243. [PMID: 31074781 PMCID: PMC7245052 DOI: 10.1093/ije/dyz095] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Maternal pre-pregnancy body mass index (BMI) is positively associated with offspring birth weight (BW) and BMI in childhood and adulthood. Each of these associations could be due to causal intrauterine effects, or confounding (genetic or environmental), or some combination of these. Here we estimate the extent to which the association between maternal BMI and offspring body size is explained by offspring genotype, as a first step towards establishing the importance of genetic confounding. METHODS We examined the associations of maternal pre-pregnancy BMI with offspring BW and BMI at 1, 5, 10 and 15 years, in three European birth cohorts (n ≤11 498). Bivariate Genomic-relatedness-based Restricted Maximum Likelihood implemented in the GCTA software (GCTA-GREML) was used to estimate the extent to which phenotypic covariance was explained by offspring genotype as captured by common imputed single nucleotide polymorphisms (SNPs). We merged individual participant data from all cohorts, enabling calculation of pooled estimates. RESULTS Phenotypic covariance (equivalent here to Pearson's correlation coefficient) between maternal BMI and offspring phenotype was 0.15 [95% confidence interval (CI): 0.13, 0.17] for offspring BW, increasing to 0.29 (95% CI: 0.26, 0.31) for offspring 15 year BMI. Covariance explained by offspring genotype was negligible for BW [-0.04 (95% CI: -0.09, 0.01)], but increased to 0.12 (95% CI: 0.04, 0.21) at 15 years, which is equivalent to 43% (95% CI: 15%, 72%) of the phenotypic covariance. Sensitivity analyses using weight, BMI and ponderal index as the offspring phenotype at all ages showed similar results. CONCLUSIONS Offspring genotype explains a substantial fraction of the covariance between maternal BMI and offspring adolescent BMI. This is consistent with a potentially important role for genetic confounding as a driver of the maternal BMI-offspring BMI association.
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Affiliation(s)
- Tom A Bond
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Matthias Wielscher
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Juha Auvinen
- Oulunkaari Health Center, Ii, Finland
- Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland
- Center for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Minna Männikkö
- Northern Finland Birth Cohort, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Sirkka Keinänen-Kiukaanniemi
- Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland
- Center for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Healthcare and Social Services of Selänne, Pyhäjärvi, Finland
| | - Marc J Gunter
- Section of Nutrition and Metabolism, IARC, Lyon, France
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Inga Prokopenko
- Section of Genomics of Common Disease, Department of Medicine, Imperial College London, London, UK
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - David M Evans
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Sylvain Sebert
- Northern Finland Birth Cohort, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Alex Lewin
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Paul F O’Reilly
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, Bristol, UK
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Northern Finland Birth Cohort, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
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Shah S, Henry A, Roselli C, Lin H, Sveinbjörnsson G, Fatemifar G, Hedman ÅK, Wilk JB, Morley MP, Chaffin MD, Helgadottir A, Verweij N, Dehghan A, Almgren P, Andersson C, Aragam KG, Ärnlöv J, Backman JD, Biggs ML, Bloom HL, Brandimarto J, Brown MR, Buckbinder L, Carey DJ, Chasman DI, Chen X, Chen X, Chung J, Chutkow W, Cook JP, Delgado GE, Denaxas S, Doney AS, Dörr M, Dudley SC, Dunn ME, Engström G, Esko T, Felix SB, Finan C, Ford I, Ghanbari M, Ghasemi S, Giedraitis V, Giulianini F, Gottdiener JS, Gross S, Guðbjartsson DF, Gutmann R, Haggerty CM, van der Harst P, Hyde CL, Ingelsson E, Jukema JW, Kavousi M, Khaw KT, Kleber ME, Køber L, Koekemoer A, Langenberg C, Lind L, Lindgren CM, London B, Lotta LA, Lovering RC, Luan J, Magnusson P, Mahajan A, Margulies KB, März W, Melander O, Mordi IR, Morgan T, Morris AD, Morris AP, Morrison AC, Nagle MW, Nelson CP, Niessner A, Niiranen T, O'Donoghue ML, Owens AT, Palmer CNA, Parry HM, Perola M, Portilla-Fernandez E, Psaty BM, Rice KM, Ridker PM, Romaine SPR, Rotter JI, Salo P, Salomaa V, van Setten J, Shalaby AA, Smelser DT, Smith NL, Stender S, Stott DJ, Svensson P, Tammesoo ML, Taylor KD, Teder-Laving M, Teumer A, Thorgeirsson G, Thorsteinsdottir U, Torp-Pedersen C, Trompet S, Tyl B, Uitterlinden AG, Veluchamy A, Völker U, Voors AA, Wang X, Wareham NJ, Waterworth D, Weeke PE, Weiss R, Wiggins KL, Xing H, Yerges-Armstrong LM, Yu B, Zannad F, Zhao JH, Hemingway H, Samani NJ, McMurray JJV, Yang J, Visscher PM, Newton-Cheh C, Malarstig A, Holm H, Lubitz SA, Sattar N, Holmes MV, Cappola TP, Asselbergs FW, Hingorani AD, Kuchenbaecker K, Ellinor PT, Lang CC, Stefansson K, Smith JG, Vasan RS, Swerdlow DI, Lumbers RT. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat Commun 2020; 11:163. [PMID: 31919418 PMCID: PMC6952380 DOI: 10.1038/s41467-019-13690-5] [Citation(s) in RCA: 360] [Impact Index Per Article: 90.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.
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Affiliation(s)
- Sonia Shah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Albert Henry
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Carolina Roselli
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
| | | | - Ghazaleh Fatemifar
- British Heart Foundation Research Accelerator, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Åsa K Hedman
- Cardiovascular Medicine unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Jemma B Wilk
- Pfizer Worldwide Research & Development, 1 Portland St, Cambridge, MA, USA
| | - Michael P Morley
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark D Chaffin
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna Helgadottir
- deCODE genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
| | - Niek Verweij
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, W2 1PG, UK
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, W2 1PG, UK
| | - Peter Almgren
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Charlotte Andersson
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Department of Cardiology, Herlev Gentofte Hospital, Herlev Ringvej 57, 2650, Herlev, Denmark
| | - Krishna G Aragam
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society/ Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Sciences, Dalarna University, Falun, Sweden
| | - Joshua D Backman
- Regeneron Genetics Center, 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Heather L Bloom
- Division of Cardiology, Department of Medicine, Emory University Medical Center, Atlanta, GA, USA
| | - Jeffrey Brandimarto
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael R Brown
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, USA
| | - Leonard Buckbinder
- Pfizer Worldwide Research & Development, 1 Portland St, Cambridge, MA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, 02215, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Xing Chen
- Pfizer Worldwide Research & Development, 1 Portland St, Cambridge, MA, USA
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jonathan Chung
- Regeneron Genetics Center, 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - William Chutkow
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Spiros Denaxas
- British Heart Foundation Research Accelerator, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK London, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- The Alan Turing Institute, London, United Kingdom
| | - Alexander S Doney
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Samuel C Dudley
- Cardiovascular Division, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Michael E Dunn
- Regeneron Pharmaceuticals, Cardiovascular Research, 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Gunnar Engström
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Tõnu Esko
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Stephan B Felix
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Ian Ford
- Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sahar Ghasemi
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, 75185, Sweden
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, 02215, USA
| | - John S Gottdiener
- Department of Medicine, Division of Cardiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stefan Gross
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Daníel F Guðbjartsson
- deCODE genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, 101, Reykjavik, Iceland
| | - Rebecca Gutmann
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | | | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
| | - Craig L Hyde
- Pfizer Worldwide Research & Development, 1 Portland St, Cambridge, MA, USA
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, 94305, USA
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, LUMC, Leiden, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Lars Køber
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Andrea Koekemoer
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Barry London
- Division of Cardiovascular Medicine and Abboud Cardiovascular Research Center, University of Iowa, Iowa City, IA, USA
| | - Luca A Lotta
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Ruth C Lovering
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Patrik Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Kenneth B Margulies
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Winfried März
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Olle Melander
- Department of Internal Medicine, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden
| | - Ify R Mordi
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Thomas Morgan
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Andrew D Morris
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, USA
| | - Michael W Nagle
- Pfizer Worldwide Research & Development, 1 Portland St, Cambridge, MA, USA
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Alexander Niessner
- Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria
| | - Teemu Niiranen
- National Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Michelle L O'Donoghue
- TIMI Study Group, Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Anjali T Owens
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Colin N A Palmer
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Helen M Parry
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
| | - Eliana Portilla-Fernandez
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Division of Vascular Medicine and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bruce M Psaty
- Department of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, 02215, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Simon P R Romaine
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Perttu Salo
- National Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Jessica van Setten
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Alaa A Shalaby
- Division of Cardiology, Department of Medicine, University of Pittsburgh Medical Center and VA Pittsburgh HCS, Pittsburgh, PA, USA
| | - Diane T Smelser
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Nicholas L Smith
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research & Development, Seattle, WA, USA
| | - Steen Stender
- Department of Clinical Biochemistry, Copenhagen University Hospital, Herlev and Gentofte, København, Denmark
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Per Svensson
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - Mari-Liis Tammesoo
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, LABiomed and Departments of Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Alexander Teumer
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Guðmundur Thorgeirsson
- deCODE genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
- Division of Cardiology, Department of Internal Medicine, Landspitali, National University Hospital of Iceland, Hringbraut, 101, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
- Faculty of Medicine, Department of Medicine, University of Iceland, Saemundargata 2, 101, Reykjavik, Iceland
| | - Christian Torp-Pedersen
- Department of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
- Department of Health, Science and Technology, Aalborg University Hospital, Aalborg, Denmark
- Departments of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Benoit Tyl
- Translational and Clinical Research, Servier Cardiovascular Center for Therapeutic Innovation, 50 rue Carnot, 92284, Suresnes, France
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Abirami Veluchamy
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Adriaan A Voors
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Xiaosong Wang
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | | | - Peter E Weeke
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Raul Weiss
- Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University Medical Center, Columbus, OH, USA
| | - Kerri L Wiggins
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Heming Xing
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, USA
| | - Faiez Zannad
- Université de Lorraine, CHU de Nancy, Inserm and INI-CRCT (F-CRIN), Institut Lorrain du Coeur et des Vaisseaux, 54500, Vandoeuvre Lès, Nancy, France
| | - Jing Hua Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Harry Hemingway
- British Heart Foundation Research Accelerator, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK London, University College London, London, UK
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - John J V McMurray
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Christopher Newton-Cheh
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - Anders Malarstig
- Cardiovascular Medicine unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
- Pfizer Worldwide Research & Development, 1 Portland St, Cambridge, MA, USA
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
| | - Steven A Lubitz
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Naveed Sattar
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
| | - Michael V Holmes
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Thomas P Cappola
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Folkert W Asselbergs
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College of London, London, W1T 7NF, UK
- UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Chim C Lang
- Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Sturlugata 8, 101, Reykjavik, Iceland
- Faculty of Medicine, Department of Medicine, University of Iceland, Saemundargata 2, 101, Reykjavik, Iceland
| | - J Gustav Smith
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, Sweden
| | - Ramachandran S Vasan
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
- Sections of Cardiology, Preventive Medicine and Epidemiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - Daniel I Swerdlow
- Institute of Cardiovascular Science, University College London, London, UK
| | - R Thomas Lumbers
- British Heart Foundation Research Accelerator, University College London, London, UK.
- Institute of Health Informatics, University College London, London, UK.
- Health Data Research UK London, University College London, London, UK.
- Bart's Heart Centre, St. Bartholomew's Hospital, London, UK.
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McCartney DL, Zhang F, Hillary RF, Zhang Q, Stevenson AJ, Walker RM, Bermingham ML, Boutin T, Morris SW, Campbell A, Murray AD, Whalley HC, Porteous DJ, Hayward C, Evans KL, Chandra T, Deary IJ, McIntosh AM, Yang J, Visscher PM, McRae AF, Marioni RE. An epigenome-wide association study of sex-specific chronological ageing. Genome Med 2019; 12:1. [PMID: 31892350 PMCID: PMC6938636 DOI: 10.1186/s13073-019-0693-z] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 11/15/2019] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Advanced age is associated with cognitive and physical decline and is a major risk factor for a multitude of disorders. There is also a gap in life expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in blood-based DNA methylation in an unrelated cohort of 2586 individuals between the ages of 18 and 87 years, with replication in a further 4450 individuals between the ages of 18 and 93 years. METHODS Linear regression models were applied, with stringent genome-wide significance thresholds (p < 3.6 × 10-8) used in both the discovery and replication data. A second, highly conservative mixed linear model method that better controls the false-positive rate was also applied, using the same genome-wide significance thresholds. RESULTS Using the linear regression method, 52 autosomal and 597 X-linked CpG sites, mapping to 251 unique genes, replicated with concordant effect size directions in the age-by-sex interaction analysis. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation by age r = 0.02) but decreased across female adult age range (DNA methylation by age r = - 0.61). One site (cg23722529) with a significant age-by-sex interaction also had a quantitative trait locus (rs17321482) that is a genome-wide significant variant for prostate cancer. The mixed linear model method identified 11 CpG sites associated with the age-by-sex interaction. CONCLUSION The majority of differences in age-associated DNA methylation trajectories between sexes are present on the X chromosome. Several of these differences occur within genes that have been implicated in sexually dimorphic traits.
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Affiliation(s)
- Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Futao Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Qian Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Mairead L Bermingham
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Thibaud Boutin
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Stewart W Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, Scotland, UK
| | - Heather C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, Scotland, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Tamir Chandra
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
- Department of Psychology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, Scotland, UK
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang, 325027, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland, UK.
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK.
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Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, Tan M, Kia DA, Noyce AJ, Xue A, Bras J, Young E, von Coelln R, Simón-Sánchez J, Schulte C, Sharma M, Krohn L, Pihlstrøm L, Siitonen A, Iwaki H, Leonard H, Faghri F, Gibbs JR, Hernandez DG, Scholz SW, Botia JA, Martinez M, Corvol JC, Lesage S, Jankovic J, Shulman LM, Sutherland M, Tienari P, Majamaa K, Toft M, Andreassen OA, Bangale T, Brice A, Yang J, Gan-Or Z, Gasser T, Heutink P, Shulman JM, Wood NW, Hinds DA, Hardy JA, Morris HR, Gratten J, Visscher PM, Graham RR, Singleton AB. Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet Neurol 2019; 18:1091-1102. [PMID: 31701892 PMCID: PMC8422160 DOI: 10.1016/s1474-4422(19)30320-5] [Citation(s) in RCA: 1095] [Impact Index Per Article: 219.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 07/19/2019] [Accepted: 07/29/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. METHODS We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. FINDINGS Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10-7). INTERPRETATION These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. FUNDING The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).
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Affiliation(s)
- Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA.
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Costanza L Vallerga
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | | | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Diana Chang
- Department of Human Genetics, Genentech, South San Francisco, CA, USA
| | - Manuela Tan
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK; Department of Clinical and Movement Neuroscience and UCL Movement Disorders Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Demis A Kia
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK; Department of Clinical and Movement Neuroscience and UCL Movement Disorders Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Alastair J Noyce
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK; Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Angli Xue
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Jose Bras
- Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, London, UK; Center for Neurodegenerative Science, Van Andel Research Institute, Grand Rapids, MI, USA
| | - Emily Young
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Javier Simón-Sánchez
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Claudia Schulte
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Lynne Krohn
- Department of Human Genetics, McGill University, Montreal, QC, Canada; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ari Siitonen
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland; Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA; The Michael J Fox Foundation, New York, NY, USA
| | - Hampton Leonard
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA
| | - Faraz Faghri
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - J Raphael Gibbs
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Sonja W Scholz
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Juan A Botia
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK; Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Spain
| | - Maria Martinez
- Institut national de la santé et de la recherche médicale Unité mixte de recherche 1220, Toulouse, France; Paul Sabatier University, Toulouse, France
| | - Jean-Christophe Corvol
- Institut national de la santé et de la recherche médicale U1127, CNRS UMR 7225, Paris, France; Sorbonne Université centre national de la recherche médicale, unité mixte de recherche 1127, Paris, France; Assistance Publique Hôpitaux de Paris, Paris, France; Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Suzanne Lesage
- Institut national de la santé et de la recherche médicale U1127, CNRS UMR 7225, Paris, France; Sorbonne Université centre national de la recherche médicale, unité mixte de recherche 1127, Paris, France; Assistance Publique Hôpitaux de Paris, Paris, France; Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Joseph Jankovic
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Lisa M Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Margaret Sutherland
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Pentti Tienari
- Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, Finland; Helsinki University Hospital, Helsinki, Finland
| | - Kari Majamaa
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland; Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Jebsen Centre for Psychosis Research, University of Oslo, Oslo, Norway
| | - Tushar Bangale
- Department of Human Genetics, Genentech, South San Francisco, CA, USA
| | - Alexis Brice
- Institut national de la santé et de la recherche médicale U1127, CNRS UMR 7225, Paris, France; Sorbonne Université centre national de la recherche médicale, unité mixte de recherche 1127, Paris, France; Assistance Publique Hôpitaux de Paris, Paris, France; Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Jian Yang
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montreal, QC, Canada; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Thomas Gasser
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Peter Heutink
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Joshua M Shulman
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
| | - Nicholas W Wood
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK; Department of Clinical and Movement Neuroscience and UCL Movement Disorders Centre, UCL Queen Square Institute of Neurology, London, UK
| | | | - John A Hardy
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK
| | - Huw R Morris
- Department of Clinical and Movement Neuroscience and UCL Movement Disorders Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Jacob Gratten
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Robert R Graham
- Department of Human Genetics, Genentech, South San Francisco, CA, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
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Yengo L, Sidari M, Verweij KJH, Visscher PM, Keller MC, Zietsch BP. No Evidence for Social Genetic Effects or Genetic Similarity Among Friends Beyond that Due to Population Stratification: A Reappraisal of Domingue et al (2018). Behav Genet 2019; 50:67-71. [PMID: 31713005 DOI: 10.1007/s10519-019-09979-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 10/24/2019] [Indexed: 11/30/2022]
Abstract
Using data from 5500 adolescents from the National Longitudinal Study of Adolescent to Adult Health, Domingue et al. (Proc Natl Acad Sci 25:256., 2018) claimed to show that friends are genetically more similar to one another than randomly selected peers, beyond the confounding effects of population stratification by ancestry. The authors also claimed to show 'social-genetic' effects, whereby individuals' educational attainment (EA) is influenced by their friends' genes. We argue that neither claim is justified by the data. Mathematically we show that (1) the genetic similarity reported between friends is far larger than theoretically possible if it was caused by phenotypic assortment as the authors claim; uncontrolled population stratification is a likely reason for the genetic similarity they observed, and (2) significant association between individuals' EA and their friends' polygenic scores for EA is a necessary consequence of EA similarity among friends, and does not provide evidence for social-genetic effects. Going forward, we urge caution in the analysis and interpretation of data at the intersection of human genetics and the social sciences.
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Affiliation(s)
- Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, Australia
| | - Morgan Sidari
- School of Psychology, The University of Queensland, Brisbane, 4072, Australia
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, Australia
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA
| | - Brendan P Zietsch
- School of Psychology, The University of Queensland, Brisbane, 4072, Australia.
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79
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Abdellaoui A, Hugh-Jones D, Yengo L, Kemper KE, Nivard MG, Veul L, Holtz Y, Zietsch BP, Frayling TM, Wray NR, Yang J, Verweij KJH, Visscher PM. Genetic correlates of social stratification in Great Britain. Nat Hum Behav 2019; 3:1332-1342. [PMID: 31636407 DOI: 10.1038/s41562-019-0757-5] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 09/18/2019] [Indexed: 02/07/2023]
Abstract
Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.
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Affiliation(s)
- Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| | | | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Michel G Nivard
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Laura Veul
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Yan Holtz
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Brendan P Zietsch
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia. .,Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.
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80
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Wainschtein P, Jain DP, Yengo L, Zheng Z, Cupples LA, Shadyab AH, McKnight B, Shoemaker BM, Mitchell BD, Psaty BM, Kooperberg C, Roden D, Darbar D, Arnett DK, Regan EA, Boerwinkle E, Rotter JI, Allison MA, McDonald MLN, Chung MK, Smith NL, Ellinor PT, Vasan RS, Mathias RA, Rich SS, Heckbert SR, Redline S, Guo X, Chen YDI, Liu CT, Andrade MD, Yanek LR, Albert CM, Hernandez RD, McGarvey ST, North KE, Lange LA, Weir BS, Laurie CC, Yang J, Visscher PM. Recovery of trait heritability from whole genome sequence data. ACTA ACUST UNITED AC 2019. [DOI: 10.1530/ey.16.14.15] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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81
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Zhang Q, Vallerga CL, Walker RM, Lin T, Henders AK, Montgomery GW, He J, Fan D, Fowdar J, Kennedy M, Pitcher T, Pearson J, Halliday G, Kwok JB, Hickie I, Lewis S, Anderson T, Silburn PA, Mellick GD, Harris SE, Redmond P, Murray AD, Porteous DJ, Haley CS, Evans KL, McIntosh AM, Yang J, Gratten J, Marioni RE, Wray NR, Deary IJ, McRae AF, Visscher PM. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med 2019; 11:54. [PMID: 31443728 PMCID: PMC6708158 DOI: 10.1186/s13073-019-0667-1] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/16/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed 'epigenetic clocks'. The deviation of predicted age from the actual age ('age acceleration residual', AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association. METHODS In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues. RESULTS We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91-1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79-1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor. CONCLUSIONS This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.
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Affiliation(s)
- Qian Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Costanza L Vallerga
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Anjali K Henders
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Ji He
- Department of Neurology, Peking University, Third Hospital, No. 49, North Garden Road, Haidian District, Beijing, 100191, China
| | - Dongsheng Fan
- Department of Neurology, Peking University, Third Hospital, No. 49, North Garden Road, Haidian District, Beijing, 100191, China
| | - Javed Fowdar
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Martin Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Toni Pitcher
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - John Pearson
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Glenda Halliday
- Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - John B Kwok
- Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Ian Hickie
- Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Simon Lewis
- Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Tim Anderson
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Peter A Silburn
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - George D Mellick
- Griffith Institute for Drug Discovery (GRIDD), Griffith University, Brisbane, Australia
| | - Sarah E Harris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Paul Redmond
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Christopher S Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF, UK
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Jacob Gratten
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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82
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Wang H, Zhang F, Zeng J, Wu Y, Kemper KE, Xue A, Zhang M, Powell JE, Goddard ME, Wray NR, Visscher PM, McRae AF, Yang J. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Sci Adv 2019; 5:eaaw3538. [PMID: 31453325 PMCID: PMC6693916 DOI: 10.1126/sciadv.aaw3538] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 07/11/2019] [Indexed: 05/17/2023]
Abstract
Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.
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Affiliation(s)
- Huanwei Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Min Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Joseph E. Powell
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute for Medical Research, Sydney, New South Wales 2010, Australia
- Faculty of Medicine, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Michael E. Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia
- Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Allan F. McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
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83
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Hillary RF, McCartney DL, Harris SE, Stevenson AJ, Seeboth A, Zhang Q, Liewald DC, Evans KL, Ritchie CW, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Marioni RE. Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936. Nat Commun 2019; 10:3160. [PMID: 31320639 PMCID: PMC6639385 DOI: 10.1038/s41467-019-11177-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/24/2019] [Indexed: 02/06/2023] Open
Abstract
Although plasma proteins may serve as markers of neurological disease risk, the molecular mechanisms responsible for inter-individual variation in plasma protein levels are poorly understood. Therefore, we conduct genome- and epigenome-wide association studies on the levels of 92 neurological proteins to identify genetic and epigenetic loci associated with their plasma concentrations (n = 750 healthy older adults). We identify 41 independent genome-wide significant (P < 5.4 × 10-10) loci for 33 proteins and 26 epigenome-wide significant (P < 3.9 × 10-10) sites associated with the levels of 9 proteins. Using this information, we identify biological pathways in which putative neurological biomarkers are implicated (neurological, immunological and extracellular matrix metabolic pathways). We also observe causal relationships (by Mendelian randomisation analysis) between changes in gene expression (DRAXIN, MDGA1 and KYNU), or DNA methylation profiles (MATN3, MDGA1 and NEP), and altered plasma protein levels. Together, this may help inform causal relationships between biomarkers and neurological diseases.
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Affiliation(s)
- Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Anne Seeboth
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Qian Zhang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, 4072, Australia
| | - David C Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, Austin, TX, 78712, USA.,Population Research Center, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. .,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK.
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84
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Sidorenko J, Kassam I, Kemper KE, Zeng J, Lloyd-Jones LR, Montgomery GW, Gibson G, Metspalu A, Esko T, Yang J, McRae AF, Visscher PM. The effect of X-linked dosage compensation on complex trait variation. Nat Commun 2019; 10:3009. [PMID: 31285442 PMCID: PMC6614401 DOI: 10.1038/s41467-019-10598-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 05/09/2019] [Indexed: 12/21/2022] Open
Abstract
Quantitative genetics theory predicts that X-chromosome dosage compensation (DC) will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of DC in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and 1600 gene expression traits from a sample of 2000 individuals as well as across-tissue gene expression from the GTEx resource. We find approximately twice as much X-linked genetic variation across the UK Biobank traits in males (mean h2SNP = 0.63%) compared to females (mean h2SNP = 0.30%), confirming the predicted DC effect. Our DC estimates for complex traits and gene expression are consistent with a small proportion of genes escaping X-inactivation in a trait- and tissue-dependent manner. Finally, we highlight examples of biologically relevant X-linked heterogeneity between the sexes that bias DC estimates if unaccounted for. Dosage compensation (DC) on the X chromosome has predictable effects on genetic and phenotypic trait variance. Here, the authors use information for 20 quantitative traits in the UK Biobank and across-tissue gene expression to compare X-linked heritability and the effects of trait-associated SNPs between the sexes.
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Affiliation(s)
- Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia.
| | - Irfahan Kassam
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Luke R Lloyd-Jones
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Grant W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Greg Gibson
- School of Biology and Centre for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Tonu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, QLD, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, 4072, QLD, Australia.
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85
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Visscher PM, Wray NR, Haley CS. "Arte et Labore"-A Blackburn Rovers fan's legacy in human complex trait genetics. J Anim Breed Genet 2019; 136:273-278. [PMID: 31247684 DOI: 10.1111/jbg.12384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 01/22/2019] [Accepted: 01/23/2019] [Indexed: 11/26/2022]
Abstract
Through his own research contributions on the modelling and genetic analysis of quantitative traits and through his former students and postdocs, Robin Thompson has indirectly left a major legacy in human genetics. In this short note, we highlight examples of the long-lasting relevance and impact of Robin's work in human genetics. A lone early study of marker-assisted selection developed many of the tools and approaches later exploited (often after reinvention) by the human genetics community in GWAS studies and for prediction. Furthermore, a particularly clear example of the pervasive impact of Robin's work is that REML has become the default method to estimate variance components and that genetic predictions exploiting linkage disequilibrium in the population are starting to become used in precision medicine applications.
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Affiliation(s)
- Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Chris S Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, UK
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86
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Zeng B, Lloyd-Jones LR, Montgomery GW, Metspalu A, Esko T, Franke L, Vosa U, Claringbould A, Brigham KL, Quyyumi AA, Idaghdour Y, Yang J, Visscher PM, Powell JE, Gibson G. Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation. Genetics 2019; 212:905-918. [PMID: 31123039 PMCID: PMC6614888 DOI: 10.1534/genetics.119.302091] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 05/13/2019] [Indexed: 02/07/2023] Open
Abstract
Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary cis-eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of cis-eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ∼40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although <20% of the peak signals across platforms fine map to the same credible interval, the colocalization analysis finds that as many as 50-60% of the primary eQTL are actually shared. Subsequently, colocalization of eQTL signals with GWAS hits detected 1349 genes whose expression in peripheral blood is associated with 591 human phenotype traits or diseases, including enrichment for genes with regulatory functions. At least 10%, and possibly as many as 40%, of eQTL-trait colocalized signals are due to nonprimary cis-eQTL peaks, but just one-quarter of these colocalization signals replicated across the gene expression datasets. Our results are provided as a web-based resource for visualization of multi-site regulation of gene expression and its association with human complex traits and disease states.
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Affiliation(s)
- Biao Zeng
- School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia 30332
| | - Luke R Lloyd-Jones
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Grant W Montgomery
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | | | - Tonu Esko
- Estonian Genome Center, University of Tartu, 51010, Estonia
| | - Lude Franke
- University Medical Center, Rijksuniversiteit, 9700 RB Groningen, The Netherlands
| | - Urmo Vosa
- University Medical Center, Rijksuniversiteit, 9700 RB Groningen, The Netherlands
| | - Annique Claringbould
- University Medical Center, Rijksuniversiteit, 9700 RB Groningen, The Netherlands
| | - Kenneth L Brigham
- Department of Medicine (Emeritus), Emory University, Atlanta, Georgia 30322
| | - Arshed A Quyyumi
- Department of Medicine, Division of Cardiology, Emory University, Atlanta, Georgia 30322
| | - Youssef Idaghdour
- Biology Program, New York University Abu Dhabi, PO Box 129188, United Arab Emirates
| | - Jian Yang
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Joseph E Powell
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia
- Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
| | - Greg Gibson
- School of Biological Sciences and Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, Georgia 30332
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87
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Hickey J, Hill WG, Blasco A, Cameron N, Cullis B, McGuirk B, Mäntysaari E, Ruane J, Simm G, Veerkamp R, Visscher PM, Wray NR. Students', colleagues' and research partners' experience about work and accomplishments from collaborating with Robin Thompson. J Anim Breed Genet 2019; 136:301-309. [PMID: 31247683 DOI: 10.1111/jbg.12418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- John Hickey
- The Roslin Institute, University of Edinburgh, Midlothian, UK
| | - William G Hill
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Agustin Blasco
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| | | | - Brian Cullis
- Faculty of Engineering and Information Sciences, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, New South Wales, Australia
| | | | - Esa Mäntysaari
- Natural Resources Institute Finland (Luke), Production Systems, Animal Genetics, Jokioinen, Finland
| | - John Ruane
- FAO, Viale delle Terme di Caracalla, Rome, Italy
| | - Geoff Simm
- Global Academy of Agriculture and Food Security, The Royal (Dick) School of Veterinary Studies and The Roslin Institute, University of Edinburgh, Midlothian, UK
| | - Roel Veerkamp
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
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88
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Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, Nguyen-Viet TA, Wedow R, Zacher M, Furlotte NA, Magnusson P, Oskarsson S, Johannesson M, Visscher PM, Laibson D, Cesarini D, Neale BM, Benjamin DJ. Author Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 2019; 51:1295. [DOI: 10.1038/s41588-019-0469-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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89
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Blauwendraat C, Heilbron K, Vallerga CL, Bandres-Ciga S, von Coelln R, Pihlstrøm L, Simón-Sánchez J, Schulte C, Sharma M, Krohn L, Siitonen A, Iwaki H, Leonard H, Noyce AJ, Tan M, Gibbs JR, Hernandez DG, Scholz SW, Jankovic J, Shulman LM, Lesage S, Corvol JC, Brice A, van Hilten JJ, Marinus J, Tienari P, Majamaa K, Toft M, Grosset DG, Gasser T, Heutink P, Shulman JM, Wood N, Hardy J, Morris HR, Hinds DA, Gratten J, Visscher PM, Gan-Or Z, Nalls MA, Singleton AB. Parkinson's disease age at onset genome-wide association study: Defining heritability, genetic loci, and α-synuclein mechanisms. Mov Disord 2019; 34:866-875. [PMID: 30957308 PMCID: PMC6579628 DOI: 10.1002/mds.27659] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 01/02/2019] [Accepted: 01/21/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Increasing evidence supports an extensive and complex genetic contribution to PD. Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age at onset are largely unknown. OBJECTIVES To identify the genetic determinants of PD age at onset. METHODS Using genetic data of 28,568 PD cases, we performed a genome-wide association study based on PD age at onset. RESULTS We estimated that the heritability of PD age at onset attributed to common genetic variation was ∼0.11, lower than the overall heritability of risk for PD (∼0.27), likely, in part, because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni-corrected significant effect at other known PD risk loci, GBA, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. Notably, SNCA, TMEM175, SCARB2, BAG3, and GBA have all been shown to be implicated in α-synuclein aggregation pathways. Remarkably, other well-established PD risk loci, such as GCH1 and MAPT, did not show a significant effect on age at onset of PD. CONCLUSIONS Overall, we have performed the largest age at onset of PD genome-wide association studies to date, and our results show that not all PD risk loci influence age at onset with significant differences between risk alleles for age at onset. This provides a compelling picture, both within the context of functional characterization of disease-linked genetic variability and in defining differences between risk alleles for age at onset, or frank risk for disease. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Karl Heilbron
- 23andMe, Inc., 899 W Evelyn Avenue, Mountain View, CA, USA
| | - Costanza L. Vallerga
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Javier Simón-Sánchez
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Claudia Schulte
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tubingen, Germany
| | - Lynne Krohn
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ari Siitonen
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland
- Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- The Michael J Fox Foundation for Parkinson’s Research, NY, USA
| | - Hampton Leonard
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Alastair J. Noyce
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
| | - Manuela Tan
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
| | - J. Raphael Gibbs
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Dena G. Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Sonja W. Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Joseph Jankovic
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Lisa M. Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Suzanne Lesage
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Jean-Christophe Corvol
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Alexis Brice
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | | | - Johan Marinus
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Pentti Tienari
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland
- Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Kari Majamaa
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland
- Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Donald G. Grosset
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
- Institute of Neuroscience & Psychology, University of Glasgow, Glasgow, UK
| | - Thomas Gasser
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Peter Heutink
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Joshua M Shulman
- Departments of Molecular & Human Genetics and Neuroscience, Baylor College of Medicine, Houston, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, USA
| | - Nicolas Wood
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - John Hardy
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Huw R Morris
- Department of Clinical Neuroscience, UCL Institute of Neurology, London UK
- UCL Movement Disorders Centre, UCL Institute of Neurology, London, UK
| | - David A. Hinds
- 23andMe, Inc., 899 W Evelyn Avenue, Mountain View, CA, USA
| | - Jacob Gratten
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Mike A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Andrew B. Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
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90
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Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, Nguyen-Viet TA, Wedow R, Zacher M, Furlotte NA, Magnusson P, Oskarsson S, Johannesson M, Visscher PM, Laibson D, Cesarini D, Neale BM, Benjamin DJ. Publisher Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 2019; 51:1190. [PMID: 31147634 DOI: 10.1038/s41588-019-0444-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- Patrick Turley
- Broad Institute, Cambridge, MA, USA. .,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA.
| | - Raymond K Walters
- Broad Institute, Cambridge, MA, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA
| | - Omeed Maghzian
- Department of Economics, Harvard University, Cambridge, MA, USA
| | - Aysu Okbay
- Department of Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - James J Lee
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | | | - Tuan Anh Nguyen-Viet
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Robbee Wedow
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.,Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA.,Department of Sociology, University of Colorado Boulder, Boulder, CO, USA
| | - Meghan Zacher
- Department of Sociology, Harvard University, Cambridge, MA, USA
| | | | | | | | - Patrik Magnusson
- Institutionen för Medicinsk Epidemiologi och Biostatistik, Karolinska Institutet, Stockholm, Sweden
| | - Sven Oskarsson
- Department of Government, Uppsala Universitet, Uppsala, Sweden
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - David Laibson
- Department of Economics, Harvard University, Cambridge, MA, USA.,National Bureau of Economic Research, Cambridge, MA, USA
| | - David Cesarini
- National Bureau of Economic Research, Cambridge, MA, USA. .,Department of Economics and Center for Experimental Social Science, New York University, New York, NY, USA. .,Institutet för Näringslivsforskning, Stockholm, Sweden.
| | - Benjamin M Neale
- Broad Institute, Cambridge, MA, USA. .,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA.
| | - Daniel J Benjamin
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA. .,National Bureau of Economic Research, Cambridge, MA, USA. .,Department of Economics, University of Southern California, Los Angeles, CA, USA.
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91
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Zhang F, Chen W, Zhu Z, Zhang Q, Nabais MF, Qi T, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J. OSCA: a tool for omic-data-based complex trait analysis. Genome Biol 2019; 20:107. [PMID: 31138268 PMCID: PMC6537380 DOI: 10.1186/s13059-019-1718-z] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 05/20/2019] [Indexed: 12/11/2022] Open
Abstract
The rapid increase of omic data has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we propose a mixed-linear-model-based method called MOMENT that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for unobserved confounders. We demonstrate by simulations that MOMENT shows a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package called OSCA together with a number of other implementations for omic-data-based analyses.
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Affiliation(s)
- Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Wenhan Chen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Qian Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Marta F Nabais
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- University of Exeter Medical School, Devon, EX2 5DW, UK
| | - Ting Qi
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia.
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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92
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Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, Frayling TM, Hirschhorn J, Yang J, Visscher PM. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet 2019; 27:3641-3649. [PMID: 30124842 DOI: 10.1093/hmg/ddy271] [Citation(s) in RCA: 1134] [Impact Index Per Article: 226.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 07/19/2018] [Indexed: 12/15/2022] Open
Abstract
Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.
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Affiliation(s)
- Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Australia.,Estonian Genome Center, Institute of Genomics, University of Tartu, Estonia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Australia
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, UK
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, UK
| | | | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Australia
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93
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Gorman KM, Meyer E, Grozeva D, Spinelli E, McTague A, Sanchis-Juan A, Carss KJ, Bryant E, Reich A, Schneider AL, Pressler RM, Simpson MA, Debelle GD, Wassmer E, Morton J, Sieciechowicz D, Jan-Kamsteeg E, Paciorkowski AR, King MD, Cross JH, Poduri A, Mefford HC, Scheffer IE, Haack TB, McCullagh G, Millichap JJ, Carvill GL, Clayton-Smith J, Maher ER, Raymond FL, Kurian MA, McRae JF, Clayton S, Fitzgerald TW, Kaplanis J, Prigmore E, Rajan D, Sifrim A, Aitken S, Akawi N, Alvi M, Ambridge K, Barrett DM, Bayzetinova T, Jones P, Jones WD, King D, Krishnappa N, Mason LE, Singh T, Tivey AR, Ahmed M, Anjum U, Archer H, Armstrong R, Awada J, Balasubramanian M, Banka S, Baralle D, Barnicoat A, Batstone P, Baty D, Bennett C, Berg J, Bernhard B, Bevan AP, Bitner-Glindzicz M, Blair E, Blyth M, Bohanna D, Bourdon L, Bourn D, Bradley L, Brady A, Brent S, Brewer C, Brunstrom K, Bunyan DJ, Burn J, Canham N, Castle B, Chandler K, Chatzimichali E, Cilliers D, Clarke A, Clasper S, Clayton-Smith J, Clowes V, Coates A, Cole T, Colgiu I, Collins A, Collinson MN, Connell F, Cooper N, Cox H, Cresswell L, Cross G, Crow Y, D’Alessandro M, Dabir T, Davidson R, Davies S, de Vries D, Dean J, Deshpande C, Devlin G, Dixit A, Dobbie A, Donaldson A, Donnai D, Donnelly D, Donnelly C, Douglas A, Douzgou S, Duncan A, Eason J, Ellard S, Ellis I, Elmslie F, Evans K, Everest S, Fendick T, Fisher R, Flinter F, Foulds N, Fry A, Fryer A, Gardiner C, Gaunt L, Ghali N, Gibbons R, Gill H, Goodship J, Goudie D, Gray E, Green A, Greene P, Greenhalgh L, Gribble S, Harrison R, Harrison L, Harrison V, Hawkins R, He L, Hellens S, Henderson A, Hewitt S, Hildyard L, Hobson E, Holden S, Holder M, Holder S, Hollingsworth G, Homfray T, Humphreys M, Hurst J, Hutton B, Ingram S, Irving M, Islam L, Jackson A, Jarvis J, Jenkins L, Johnson D, Jones E, Josifova D, Joss S, Kaemba B, Kazembe S, Kelsell R, Kerr B, Kingston H, Kini U, Kinning E, Kirby G, Kirk C, Kivuva E, Kraus A, Kumar D, Kumar VKA, Lachlan K, Lam W, Lampe A, Langman C, Lees M, Lim D, Longman C, Lowther G, Lynch SA, Magee A, Maher E, Male A, Mansour S, Marks K, Martin K, Maye U, McCann E, McConnell V, McEntagart M, McGowan R, McKay K, McKee S, McMullan DJ, McNerlan S, McWilliam C, Mehta S, Metcalfe K, Middleton A, Miedzybrodzka Z, Miles E, Mohammed S, Montgomery T, Moore D, Morgan S, Morton J, Mugalaasi H, Murday V, Murphy H, Naik S, Nemeth A, Nevitt L, Newbury-Ecob R, Norman A, O’Shea R, Ogilvie C, Ong KR, Park SM, Parker MJ, Patel C, Paterson J, Payne S, Perrett D, Phipps J, Pilz DT, Pollard M, Pottinger C, Poulton J, Pratt N, Prescott K, Price S, Pridham A, Procter A, Purnell H, Quarrell O, Ragge N, Rahbari R, Randall J, Rankin J, Raymond L, Rice D, Robert L, Roberts E, Roberts J, Roberts P, Roberts G, Ross A, Rosser E, Saggar A, Samant S, Sampson J, Sandford R, Sarkar A, Schweiger S, Scott R, Scurr I, Selby A, Seller A, Sequeira C, Shannon N, Sharif S, Shaw-Smith C, Shearing E, Shears D, Sheridan E, Simonic I, Singzon R, Skitt Z, Smith A, Smith K, Smithson S, Sneddon L, Splitt M, Squires M, Stewart F, Stewart H, Straub V, Suri M, Sutton V, Swaminathan GJ, Sweeney E, Tatton-Brown K, Taylor C, Taylor R, Tein M, Temple IK, Thomson J, Tischkowitz M, Tomkins S, Torokwa A, Treacy B, Turner C, Turnpenny P, Tysoe C, Vandersteen A, Varghese V, Vasudevan P, Vijayarangakannan P, Vogt J, Wakeling E, Wallwark S, Waters J, Weber A, Wellesley D, Whiteford M, Widaa S, Wilcox S, Wilkinson E, Williams D, Williams N, Wilson L, Woods G, Wragg C, Wright M, Yates L, Yau M, Nellåker C, Parker M, Firth HV, Wright CF, FitzPatrick DR, Barrett JC, Hurles ME, Al Turki S, Anderson C, Anney R, Antony D, Artigas MS, Ayub M, Balasubramaniam S, Barrett JC, Barroso I, Beales P, Bentham J, Bhattacharya S, Birney E, Blackwood D, Bobrow M, Bochukova E, Bolton P, Bounds R, Boustred C, Breen G, Calissano M, Carss K, Chatterjee K, Chen L, Ciampi A, Cirak S, Clapham P, Clement G, Coates G, Collier D, Cosgrove C, Cox T, Craddock N, Crooks L, Curran S, Curtis D, Daly A, Day-Williams A, Day IN, Down T, Du Y, Dunham I, Edkins S, Ellis P, Evans D, Faroogi S, Fatemifar G, Fitzpatrick DR, Flicek P, Flyod J, Foley AR, Franklin CS, Futema M, Gallagher L, Geihs M, Geschwind D, Griffin H, Grozeva D, Guo X, Guo X, Gurling H, Hart D, Hendricks A, Holmans P, Howie B, Huang L, Hubbard T, Humphries SE, Hurles ME, Hysi P, Jackson DK, Jamshidi Y, Jing T, Joyce C, Kaye J, Keane T, Keogh J, Kemp J, Kennedy K, Kolb-Kokocinski A, Lachance G, Langford C, Lawson D, Lee I, Lek M, Liang J, Lin H, Li R, Li Y, Liu R, Lönnqvist J, Lopes M, Iotchkova V, MacArthur D, Marchini J, Maslen J, Massimo M, Mathieson I, Marenne G, McGuffin P, McIntosh A, McKechanie AG, McQuillin A, Metrustry S, Mitchison H, Moayyeri A, Morris J, Muntoni F, Northstone K, O'Donnovan M, Onoufriadis A, O'Rahilly S, Oualkacha K, Owen MJ, Palotie A, Panoutsopoulou K, Parker V, Parr JR, Paternoster L, Paunio T, Payne F, Pietilainen O, Plagnol V, Quaye L, Quail MA, Raymond L, Rehnström K, Ring S, Ritchie GR, Roberts N, Savage DB, Scambler P, Schiffels S, Schmidts M, Schoenmakers N, Semple RK, Serra E, Sharp SI, Shin SY, Skuse D, Small K, Southam L, Spasic-Boskovic O, St Clair D, Stalker J, Stevens E, St Pourcian B, Sun J, Suvisaari J, Tachmazidou I, Tobin MD, Valdes A, Van Kogelenberg M, Vijayarangakannan P, Visscher PM, Wain LV, Walters JT, Wang G, Wang J, Wang Y, Ward K, Wheeler E, Whyte T, Williams H, Williamson KA, Wilson C, Wong K, Xu C, Yang J, Zhang F, Zhang P, Aitman T, Alachkar H, Ali S, Allen L, Allsup D, Ambegaonkar G, Anderson J, Antrobus R, Armstrong R, Arno G, Arumugakani G, Ashford S, Astle W, Attwood A, Austin S, Bacchelli C, Bakchoul T, Bariana TK, Baxendale H, Bennett D, Bethune C, Bibi S, Bitner-Glindzicz M, Bleda M, Boggard H, Bolton-Maggs P, Booth C, Bradley JR, Brady A, Brown M, Browning M, Bryson C, Burns S, Calleja P, Canham N, Carmichael J, Carss K, Caulfield M, Chalmers E, Chandra A, Chinnery P, Chitre M, Church C, Clement E, Clements-Brod N, Clowes V, Coghlan G, Collins P, Cooper N, Creaser-Myers A, DaCosta R, Daugherty L, Davies S, Davis J, De Vries M, Deegan P, Deevi SV, Deshpande C, Devlin L, Dewhurst E, Doffinger R, Dormand N, Drewe E, Edgar D, Egner W, Erber WN, Erwood M, Everington T, Favier R, Firth H, Fletcher D, Flinter F, Fox JC, Frary A, Freson K, Furie B, Furnell A, Gale D, Gardham A, Gattens M, Ghali N, Ghataorhe PK, Ghurye R, Gibbs S, Gilmour K, Gissen P, Goddard S, Gomez K, Gordins P, Gräf S, Greene D, Greenhalgh A, Greinacher A, Grigoriadou S, Grozeva D, Hackett S, Hadinnapola C, Hague R, Haimel M, Halmagyi C, Hammerton T, Hart D, Hayman G, Heemskerk JW, Henderson R, Hensiek A, Henskens Y, Herwadkar A, Holden S, Holder M, Holder S, Hu F, Huissoon A, Humbert M, Hurst J, James R, Jolles S, Josifova D, Kazmi R, Keeling D, Kelleher P, Kelly AM, Kennedy F, Kiely D, Kingston N, Koziell A, Krishnakumar D, Kuijpers TW, Kumararatne D, Kurian M, Laffan MA, Lambert MP, Allen HL, Lawrie A, Lear S, Lees M, Lentaigne C, Liesner R, Linger R, Longhurst H, Lorenzo L, Machado R, Mackenzie R, MacLaren R, Maher E, Maimaris J, Mangles S, Manson A, Mapeta R, Markus HS, Martin J, Masati L, Mathias M, Matser V, Maw A, McDermott E, McJannet C, Meacham S, Meehan S, Megy K, Mehta S, Michaelides M, Millar CM, Moledina S, Moore A, Morrell N, Mumford A, Murng S, Murphy E, Nejentsev S, Noorani S, Nurden P, Oksenhendler E, Ouwehand WH, Papadia S, Park SM, Parker A, Pasi J, Patch C, Paterson J, Payne J, Peacock A, Peerlinck K, Penkett CJ, Pepke-Zaba J, Perry DJ, Pollock V, Polwarth G, Ponsford M, Qasim W, Quinti I, Rankin S, Rankin J, Raymond FL, Rehnstrom K, Reid E, Rhodes CJ, Richards M, Richardson S, Richter A, Roberts I, Rondina M, Rosser E, Roughley C, Rue-Albrecht K, Samarghitean C, Sanchis-Juan A, Sandford R, Santra S, Sargur R, Savic S, Schulman S, Schulze H, Scott R, Scully M, Seneviratne S, Sewell C, Shamardina O, Shipley D, Simeoni I, Sivapalaratnam S, Smith K, Sohal A, Southgate L, Staines S, Staples E, Stauss H, Stein P, Stephens J, Stirrups K, Stock S, Suntharalingam J, Tait RC, Talks K, Tan Y, Thachil J, Thaventhiran J, Thomas E, Thomas M, Thompson D, Thrasher A, Tischkowitz M, Titterton C, Toh CH, Toshner M, Treacy C, Trembath R, Tuna S, Turek W, Turro E, Van Geet C, Veltman M, Vogt J, von Ziegenweldt J, Vonk Noordegraaf A, Wakeling E, Wanjiku I, Warner TQ, Wassmer E, Watkins H, Webster A, Welch S, Westbury S, Wharton J, Whitehorn D, Wilkins M, Willcocks L, Williamson C, Woods G, Wort J, Yeatman N, Yong P, Young T, Yu P. Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia. Am J Hum Genet 2019; 104:948-956. [PMID: 30982612 DOI: 10.1016/j.ajhg.2019.03.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 03/04/2019] [Indexed: 12/11/2022] Open
Abstract
The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.
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94
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Wu Y, Byrne EM, Zheng Z, Kemper KE, Yengo L, Mallett AJ, Yang J, Visscher PM, Wray NR. Genome-wide association study of medication-use and associated disease in the UK Biobank. Nat Commun 2019; 10:1891. [PMID: 31015401 PMCID: PMC6478889 DOI: 10.1038/s41467-019-09572-5] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 03/11/2019] [Indexed: 12/21/2022] Open
Abstract
Genome-wide association studies (GWASs) of medication use may contribute to understanding of disease etiology, could generate new leads relevant for drug discovery and can be used to quantify future risk of medication taking. Here, we conduct GWASs of self-reported medication use from 23 medication categories in approximately 320,000 individuals from the UK Biobank. A total of 505 independent genetic loci that meet stringent criteria (P < 10−8/23) for statistical significance are identified. We investigate the implications of these GWAS findings in relation to biological mechanism, potential drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes are 16 known therapeutic-effect target genes for medications from 9 categories. Two of the medication classes studied are for disorders that have not previously been subject to large GWAS (hypothyroidism and gastro-oesophageal reflux disease). An understanding of the genetic variants associated with medication use may shed light on the underlying biological pathways of disease, and aid in drug development. Here, Wu and colleagues conduct a GWAS for self-reported medication-use in the UK Biobank, finding more than 500 independent variants and many promising leads for future work.
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Affiliation(s)
- Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Enda M Byrne
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Institute for Advanced Research, Wenzhou Medical University, 325027, Wenzhou, Zhejiang, China
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Andrew J Mallett
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Department of Renal Medicine, Royal Brisbane and Women's Hospital, Herston, QLD, 4029, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Institute for Advanced Research, Wenzhou Medical University, 325027, Wenzhou, Zhejiang, China.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
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95
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Wray NR, Kemper KE, Hayes BJ, Goddard ME, Visscher PM. Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction. Genetics 2019; 211:1131-1141. [PMID: 30967442 PMCID: PMC6456317 DOI: 10.1534/genetics.119.301859] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 01/20/2019] [Indexed: 12/20/2022] Open
Abstract
In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship <0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent "related," and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.
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Affiliation(s)
- Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4067, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4067, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4067, Australia
| | - Benjamin J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Michael E Goddard
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
- Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4067, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4067, Australia
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96
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Abstract
The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher's 1918 paper in the context of how and why it is relevant in today's genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection.
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Affiliation(s)
- Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia 4072
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia 4072
| | - Michael E Goddard
- Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia 3083
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Victoria, Australia 3004
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97
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Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM. Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model. Cell 2019; 173:1573-1580. [PMID: 29906445 DOI: 10.1016/j.cell.2018.05.051] [Citation(s) in RCA: 186] [Impact Index Per Article: 37.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 02/19/2018] [Accepted: 05/24/2018] [Indexed: 12/19/2022]
Abstract
The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.
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Affiliation(s)
- Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, P.O. Box 30001, 9700 RB Groningen, Netherlands
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, Australia
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98
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Visscher PM, Bruce Walsh J. Commentary: Fisher 1918: the foundation of the genetics and analysis of complex traits. Int J Epidemiol 2019; 48:10-12. [PMID: 29040514 DOI: 10.1093/ije/dyx129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2017] [Indexed: 11/12/2022] Open
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99
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Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, Yengo L, Ferreira T, Marouli E, Ji Y, Yang J, Jones S, Beaumont R, Croteau-Chonka DC, Winkler TW, Hattersley AT, Loos RJF, Hirschhorn JN, Visscher PM, Frayling TM, Yaghootkar H, Lindgren CM. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 2019; 28:166-174. [PMID: 30239722 PMCID: PMC6298238 DOI: 10.1093/hmg/ddy327] [Citation(s) in RCA: 590] [Impact Index Per Article: 118.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 07/27/2018] [Accepted: 09/03/2018] [Indexed: 01/08/2023] Open
Abstract
More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.
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Affiliation(s)
- Sara L Pulit
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
| | - Charli Stoneman
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Andrew P Morris
- Biostatistics Department, University of Liverpool, Liverpool, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Andrew R Wood
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Craig A Glastonbury
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
| | - Jessica Tyrrell
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Loïc Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yingjie Ji
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Samuel Jones
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Robin Beaumont
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Damien C Croteau-Chonka
- Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | | | - Andrew T Hattersley
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Timothy M Frayling
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Hanieh Yaghootkar
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
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100
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Yengo L, Visscher PM. Assortative mating on complex traits revisited: Double first cousins and the X-chromosome. Theor Popul Biol 2018; 124:51-60. [PMID: 30316741 PMCID: PMC6296780 DOI: 10.1016/j.tpb.2018.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 09/12/2018] [Accepted: 09/14/2018] [Indexed: 11/23/2022]
Abstract
Mate choice through direct assortment on heritable traits, assortative mating (AM), is predicted in theory to inflate the genetic variance in a population and the correlation between relatives. Here, we revisit the theory of AM, first established in the landmark 1918 paper from RA Fisher, and provide new theory and analytical results. In particular, we shed light on inconsistencies in the literature regarding the correlation between double first cousins under AM and provide a solution. We derive new theory for AM due to X-chromosome loci. We show in the latter case that the inflation of genetic variance induced under AM is twice as large in females compared to males. These two theoretical contributions are verified and illustrated through simulations. We also provide a more general unified framework for the correlation between relatives in a non-inbred population.
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Affiliation(s)
- Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia.
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia; Queensland Brain Institute, The University of Queensland, Brisbane 4072, Australia
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