Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry

Human Molecular Genetics - Tập 28 Số 1 - Trang 166-174 - 2019
Sara L. Pulit1,2,3, Charli Stoneman4, Andrew P. Morris5,6, Andrew R. Wood4, Craig A. Glastonbury1, Jessica Tyrrell4, Loïc Yengo7, Teresa Ferreira1, Eirini Marouli8, Yingjie Ji4, Jian Yang7,9, Samuel E. Jones4, Robin N. Beaumont4, Damien C. Croteau‐Chonka10, Thomas W. Winkler11, Andrew T. Hattersley4, Ruth J. F. Loos12, Joel N. Hirschhorn13,14,15,16, Peter M. Visscher7,9, Timothy M. Frayling4, Hanieh Yaghootkar4, Cecilia M. Lindgren1,3,6
1Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
2Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
3Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
4University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
5Biostatistics Department, University of Liverpool, Liverpool, UK
6Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
7Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
8William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
9Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
10Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
11Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
12The 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
13Broad Institute of MIT and Harvard, Cambridge, MA, USA.
14Department of Genetics, Harvard Medical School, Boston, MA, USA
15Department of Pediatrics, Harvard Medical School, Boston, MA, USA;
16Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA

Tóm tắt

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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Tài liệu tham khảo

GBD 2015 Obesity Collaborators, 2017, Health effects of overweight and obesity in 195 countries over 25 years, N. Engl. J. Med., 377, 13, 10.1056/NEJMoa1614362

WHO | Obesity and overweight, 2018

Heymsfield, 2017, Mechanisms, pathophysiology, and management of obesity, N. Engl. J. Med., 376, 254, 10.1056/NEJMra1514009

Wang, 2005, Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men1–3, Am. J. Clin. Nutr., 81, 555, 10.1093/ajcn/81.3.555

Shungin, 2015, New genetic loci link adipose and insulin biology to body fat distribution, Nature, 518, 187, 10.1038/nature14132

Rose, 1998, Genetic and behavioral determinants of waist-hip ratio and waist circumference in women twins, Obes. Res., 6, 383, 10.1002/j.1550-8528.1998.tb00369.x

Emdin, 2017, Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease, JAMA, 317, 626, 10.1001/jama.2016.21042

Sudlow, 2015, UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age, PLoS Med., 12, e1001779, 10.1371/journal.pmed.1001779

Pulit, 2017, Sexual dimorphisms in genetic loci linked to body fat distribution, Biosci. Rep., 37, 10.1042/BSR20160184

Loh, 2015, Efficient Bayesian mixed-model analysis increases association power in large cohorts, Nat. Genet., 47, 284, 10.1038/ng.3190

Pulit, 2017, Resetting the bar: statistical significance in whole-genome sequencing-based association studies of global populations, Genet. Epidemiol., 41, 145, 10.1002/gepi.22032

Bulik-Sullivan, 2015, LD Score regression distinguishes confounding from polygenicity in genome-wide association studies, Nat. Genet., 47, 291, 10.1038/ng.3211

Huang, 2009, A comprehensive definition for metabolic syndrome, Dis. Model. Mech., 2, 231, 10.1242/dmm.001180

Cole, 2010, Illustrating bias due to conditioning on a collider, Int. J. Epidemiol., 39, 417, 10.1093/ije/dyp334

Day, 2016, A robust example of collider bias in a genetic association study, Am. J. Hum. Genet., 98, 392, 10.1016/j.ajhg.2015.12.019

Frayling, 2007, A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity, Science, 316, 889, 10.1126/science.1141634

Randall, 2013, Sex-stratified genome-wide association studies including 270 000 individuals show sexual dimorphism in genetic loci for anthropometric traits, PLoS Genet., 9, e1003500, 10.1371/journal.pgen.1003500

Lotta, 2017, Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance, Nat. Genet., 49, 17, 10.1038/ng.3714

Yaghootkar, 2016, Genetic evidence for a link between favorable adiposity and lower risk of type 2 diabetes, hypertension and heart disease, Diabetes, 65, 2448, 10.2337/db15-1671

Scott, 2014, Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity, Diabetes, 63, 4378, 10.2337/db14-0319

Chu, 2017, Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation, Nat. Genet., 49, 125, 10.1038/ng.3738

Pulit, 2010, Multiethnic genetic association studies improve power for locus discovery, PLoS One, 5, e12600, 10.1371/journal.pone.0012600

Petrovski, 2016, Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine, Genome Biol., 17, 157, 10.1186/s13059-016-1016-y

Locke, 2015, Genetic studies of body mass index yield new insights for obesity biology, Nature, 518, 197, 10.1038/nature14177

McCarthy, 2016, A reference panel of 64 976 haplotypes for genotype imputation, Nat. Genet., 48, 1279, 10.1038/ng.3643

Frazer, 2007, A second generation human haplotype map of over 31 million SNPs, Nature, 449, 851, 10.1038/nature06258

The International HapMap Consortium, 2003, The International HapMap Project, Nature, 426, 789, 10.1038/nature02168

Willer, 2010, METAL: fast and efficient meta-analysis of genomewide association scans, Bioinformatics, 26, 2190, 10.1093/bioinformatics/btq340

Chang, 2015, Second-generation PLINK: rising to the challenge of larger and richer datasets, Gigascience, 4, 1, 10.1186/s13742-015-0047-8

Purcell, 2007, PLINK: a tool set for whole-genome association and population-based linkage analyses, Am. J. Hum. Genet., 81, 559, 10.1086/519795

Yang, 2011, GCTA: a tool for genome-wide complex trait analysis, Am. J. Hum. Genet., 88, 76, 10.1016/j.ajhg.2010.11.011

Munafò, 2018, Collider scope: when selection bias can substantially influence observed associations, Int. J. Epidemiol., 47, 226, 10.1093/ije/dyx206

Bulik-Sullivan, 2015, An atlas of genetic correlations across human diseases and traits, Nat. Genet., 47, 1236, 10.1038/ng.3406

Winkler, 2015, EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data, Bioinformatics, 31, 259, 10.1093/bioinformatics/btu621

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Human Molecular Genetics

Tập 28 Số 1

166-174

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