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  • Review Article
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Benefits and limitations of genome-wide association studies

Nature Reviews Genetics volume 20, pages 467–484 (2019)Cite this article

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Abstract

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype–phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

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Fig. 1: GWAS study design.
Fig. 2: Number of loci identified as a function of GWAS sample size.
Fig. 3: GWAS performed to date represent the tip of the iceberg.
Fig. 4: Benefits and limitations of GWAS using SNP arrays.

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References

  1. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012). This paper reviews the progress and discoveries made in the first 5 years of GWAS.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017). This paper provides an overview of the lessons learned from the past decade of GWAS.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005). This study may be considered the first GWAS to be published.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017). This paper provides a description of the GWAS Catalog, a continuously updated list of GWAS and their results.

    CAS  PubMed  Google Scholar 

  5. Hirschhorn, J. N. Genomewide association studies — illuminating biologic pathways. N. Engl. J. Med. 360, 1699–1701 (2009).

    CAS  PubMed  Google Scholar 

  6. Klein, R. J., Xu, X., Mukherjee, S., Willis, J. & Hayes, J. Successes of genome-wide association studies. Cell 142, 350–351 (2010).

    CAS  PubMed  Google Scholar 

  7. Speakman, J., Loos, R., O’Rahilly, S., Hirschhorn, J. & Allison, D. GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity. Int. J. Obes. 42, 1524–1531 (2018).

    CAS  Google Scholar 

  8. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010). The authors of this paper suggest that most GWAS findings may be due to cryptic population stratification.

    CAS  PubMed  Google Scholar 

  10. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017). This reference provides a theoretical groundwork for the omnigenic model of inheritance.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Goldstein, D. B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009). In this paper, the author is among the first to suggest that GWAS may eventually implicate most of the genome.

    CAS  PubMed  Google Scholar 

  12. Meyre, D. Give GWAS a chance. Diabetes 66, 2741–2742 (2017).

    CAS  PubMed  Google Scholar 

  13. Duncan, L. et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am. J. Psychiatry 174, 850–858 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Milne, R. L. et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat. Genet. 49, 1767–1778 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Sud, A., Kinnersley, B. & Houlston, R. S. Genome-wide association studies of cancer: current insights and future perspectives. Nat. Rev. Cancer 17, 692–704 (2017).

    CAS  PubMed  Google Scholar 

  17. Zhao, W. et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49, 1450–1457 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017).

    CAS  PubMed  Google Scholar 

  20. de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

    PubMed  PubMed Central  Google Scholar 

  21. Jansen, P. R. et al. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat. Genet. 51, 394–403 (2019). This is the largest GWAS published to date.

    CAS  PubMed  Google Scholar 

  22. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S. & Hirschhorn, J. N. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet. 33, 177–182 (2003).

    CAS  PubMed  Google Scholar 

  25. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010). This study helped to largely resolve the ‘missing’ heritability problem, by demonstrating that a large portion of the heritability can be explained by common SNPs.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Loh, P.-R. et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet. 47, 1385–1392 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Yang, J. et al. Ubiquitous polygenicity of human complex traits: genome-wide analysis of 49 traits in Koreans. PLOS Genet. 9, e1003355 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Ahlqvist, E., van Zuydam, N. R., Groop, L. C. & McCarthy, M. I. The genetics of diabetic complications. Nat. Rev. Nephrol. 11, 277–287 (2015).

    CAS  PubMed  Google Scholar 

  30. Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. & Visscher, P. M. Common disease is more complex than implied by the core gene omnigenic model. Cell 173, 1573–1580 (2018).

    CAS  PubMed  Google Scholar 

  31. Hampe, J. et al. A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1. Nat. Genet. 39, 207–211 (2007).

    CAS  PubMed  Google Scholar 

  32. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

    Google Scholar 

  33. Murthy, A. et al. A Crohn’s disease variant in Atg16l1 enhances its degradation by caspase 3. Nature 506, 456–462 (2014).

    CAS  PubMed  Google Scholar 

  34. Brest, P. et al. A synonymous variant in IRGM alters a binding site for miR-196 and causes deregulation of IRGM-dependent xenophagy in Crohn’s disease. Nat. Genet. 43, 242–245 (2011).

    CAS  PubMed  Google Scholar 

  35. Williams, A. L. et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506, 97–101 (2014).

    CAS  PubMed  Google Scholar 

  36. Rusu, V. et al. Type 2 diabetes variants disrupt function of SLC16A11 through two distinct mechanisms. Cell 170, 199–212 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460, 744–747 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Altshuler, D., Daly, M. J. & Lander, E. S. Genetic mapping in human disease. Science 322, 881–888 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Morris, Z. S., Wooding, S. & Grant, J. The answer is 17 years, what is the question: understanding time lags in translational research. J. R. Soc. Med. 104, 510–520 (2011).

    PubMed  PubMed Central  Google Scholar 

  41. Edwards, A. O. et al. Complement factor H polymorphism and age-related macular degeneration. Science 308, 421–424 (2005).

    CAS  PubMed  Google Scholar 

  42. Chen, W. et al. Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration. Proc. Natl Acad. Sci. USA 107, 7401–7406 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Thorleifsson, G. et al. Common sequence variants in the LOXL1 gene confer susceptibility to exfoliation glaucoma. Science 317, 1397–1400 (2007).

    CAS  PubMed  Google Scholar 

  44. Shields, B. M. et al. Maturity-onset diabetes of the young (MODY): how many cases are we missing? Diabetologia 53, 2504–2508 (2010).

    CAS  PubMed  Google Scholar 

  45. Yamagata, K. et al. Mutations in the hepatocyte nuclear factor-1α gene in maturity-onset diabetes of the young (MODY3). Nature 384, 455–458 (1996).

    CAS  PubMed  Google Scholar 

  46. Ridker, P. M. et al. Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study. Am. J. Hum. Genet. 82, 1185–1192 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Reiner, A. P. et al. Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1α are associated with C-reactive protein. Am. J. Hum. Genet. 82, 1193–1201 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Owen, K. R. et al. Assessment of high-sensitivity C-reactive protein levels as diagnostic discriminator of maturity-onset diabetes of the young due to HNF1A mutations. Diabetes Care 33, 1919–1924 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Thanabalasingham, G. et al. A large multi-centre European study validates high-sensitivity C-reactive protein (hsCRP) as a clinical biomarker for the diagnosis of diabetes subtypes. Diabetologia 54, 2801–2810 (2011).

    CAS  PubMed  Google Scholar 

  50. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    CAS  PubMed  Google Scholar 

  51. Giacomini, K. M. et al. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine. Nat. Rev. Drug Discov. 16, 1 (2017).

    CAS  PubMed  Google Scholar 

  52. Ge, D. et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 461, 399–401 (2009).

    CAS  PubMed  Google Scholar 

  53. Suppiah, V. et al. IL28B is associated with response to chronic hepatitis C interferon-α and ribavirin therapy. Nat. Genet. 41, 1100–1104 (2009).

    CAS  PubMed  Google Scholar 

  54. Tanaka, Y. et al. Genome-wide association of IL28B with response to pegylated interferon-α and ribavirin therapy for chronic hepatitis C. Nat. Genet. 41, 1105–1109 (2009).

    CAS  PubMed  Google Scholar 

  55. Link, E. et al. SLCO1B1 variants and statin-induced myopathy — a genomewide study. N. Engl. J. Med. 359, 789–799 (2008).

    CAS  PubMed  Google Scholar 

  56. Muir, A. J. et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for IFNL3 (IL28B) genotype and PEG interferon-α-based regimens. Clin. Pharmacol. Ther. 95, 141–146 (2014).

    CAS  PubMed  Google Scholar 

  57. Ramsey, L. B. et al. The clinical pharmacogenetics implementation consortium guideline for SLCO1B1 and simvastatin-induced myopathy: 2014 update. Clin. Pharmacol. Ther. 96, 423–428 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007). This study is the first true GWAS for a complex disease that used SNP arrays with exhaustive coverage of the genome.

    CAS  PubMed  Google Scholar 

  60. Yasuda, K. et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat. Genet. 40, 1092–1097 (2008).

    CAS  PubMed  Google Scholar 

  61. Unoki, H. et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat. Genet. 40, 1098–1102 (2008).

    CAS  PubMed  Google Scholar 

  62. Moltke, I. et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014).

    CAS  PubMed  Google Scholar 

  63. Bosse, Y. & Amos, C. I. A. Decade of GWAS results in lung cancer. Cancer Epidemiol. Biomarkers Prev. 27, 363–379 (2018).

    PubMed  Google Scholar 

  64. Minster, R. L. et al. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat. Genet. 48, 1049–1054 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Nead, K. T. et al. Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals. Hum. Mol. Genet. 24, 3582–3594 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Choquet, H., Kasberger, J., Hamidovic, A. & Jorgenson, E. Contribution of common PCSK1 genetic variants to obesity in 8,359 subjects from multi-ethnic American population. PLOS ONE 8, e57857 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Kurokawa, N. et al. The ADRB3 Trp64Arg variant and BMI: a meta-analysis of 44 833 individuals. Int. J. Obes. 32, 1240–1249 (2008).

    CAS  Google Scholar 

  68. Benzinou, M. et al. Common nonsynonymous variants in PCSK1 confer risk of obesity. Nat. Genet. 40, 943–945 (2008).

    CAS  PubMed  Google Scholar 

  69. Wen, W. et al. Meta-analysis identifies common variants associated with body mass index in East Asians. Nat. Genet. 44, 307–311 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Turcot, V. et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat. Genet. 50, 26–41 (2018).

    CAS  PubMed  Google Scholar 

  71. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). This study describes one of the largest reference panels to date, which comprises 64,976 haplotypes and provides accurate genotype imputation at MAFs as low as 0.1%.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Grove, M. L. et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLOS ONE 8, e68095 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Peloso, G. M. et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94, 223–232 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Auer, P. L. et al. Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits. Nat. Genet. 46, 629–634 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. CHARGE Consortium Hematology Working Group. Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits. Nat. Genet. 48, 867–876 (2016).

    Google Scholar 

  76. Eicher, J. D. et al. Platelet-related variants identified by exomechip meta-analysis in 157,293 individuals. Am. J. Hum. Genet. 99, 40–55 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Surendran, P. et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151–1161 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016). This is a large GWAS for T2DM that finds little evidence for low-frequency and rare variants despite being sufficiently powered to detect such associations.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).

    CAS  PubMed  Google Scholar 

  81. Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68 (2015).

    Google Scholar 

  82. Deelen, P. et al. Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands’. Eur. J. Hum. Genet. 22, 1321–1326 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

    CAS  PubMed  Google Scholar 

  84. Huang, J. et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6, 8111 (2015).

    CAS  PubMed  Google Scholar 

  85. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/563866v1 (2019).

  86. Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLOS Biol. 8, e1000294 (2010). The authors of this paper lay out the theoretical basis for synthetic associations in GWAS.

    PubMed  PubMed Central  Google Scholar 

  87. Anderson, C. A., Soranzo, N., Zeggini, E. & Barrett, J. C. Synthetic associations are unlikely to account for many common disease genome-wide association signals. PLOS Biol. 9, e1000580 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Doche, M. E. et al. Human SH2B1 mutations are associated with maladaptive behaviors and obesity. J. Clin. Invest. 122, 4732–4736 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Liu, R. et al. Rare loss-of-function variants in NPC1 predispose to human obesity. Diabetes 66, 935–947 (2017).

    CAS  PubMed  Google Scholar 

  90. Saeed, S. et al. Loss-of-function mutations in ADCY3 cause monogenic severe obesity. Nat. Genet. 50, 175–179 (2018).

    CAS  PubMed  Google Scholar 

  91. Grarup, N. et al. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Nat. Genet. 50, 172–174 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J. A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Bonnefond, A. et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat. Genet. 44, 297–301 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl Acad. Sci. USA 111, 13127–13132 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Cardinale, C. J. et al. Targeted resequencing identifies defective variants of decoy receptor 3 in pediatric-onset inflammatory bowel disease. Genes Immun. 14, 447–452 (2013).

    CAS  PubMed  Google Scholar 

  97. Ellinghaus, D. et al. Association between variants of PRDM1 and NDP52 and Crohn’s disease, based on exome sequencing and functional studies. Gastroenterology 145, 339–347 (2013).

    CAS  PubMed  Google Scholar 

  98. Beaudoin, M. et al. Deep resequencing of GWAS loci identifies rare variants in CARD9, IL23R and RNF186 that are associated with ulcerative colitis. PLOS Genet. 9, e1003723 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Philippe, J. et al. A nonsense loss-of-function mutation in PCSK1 contributes to dominantly inherited human obesity. Int. J. Obes. 39, 295–302 (2015).

    CAS  Google Scholar 

  100. Lessard, S. et al. Testing the role of predicted gene knockouts in human anthropometric trait variation. Hum. Mol. Genet. 25, 2082–2092 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Walters, R. G. et al. A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature 463, 671–675 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Bochukova, E. G. et al. Large, rare chromosomal deletions associated with severe early-onset obesity. Nature 463, 666–670 (2010).

    CAS  PubMed  Google Scholar 

  103. Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34 (2009).

    CAS  PubMed  Google Scholar 

  104. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Wheeler, E. et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat. Genet. 45, 513–517 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Malhotra, D. et al. High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72, 951–963 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Jacobs, K. B. et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat. Genet. 44, 651–658 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Laurie, C. C. et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat. Genet. 44, 642–650 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Forsberg, L. A. et al. Mosaic loss of chromosome Y in peripheral blood is associated with shorter survival and higher risk of cancer. Nat. Genet. 46, 624–628 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Bonnefond, A. et al. Association between large detectable clonal mosaicism and type 2 diabetes with vascular complications. Nat. Genet. 45, 1040–1043 (2013).

    CAS  PubMed  Google Scholar 

  112. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

    PubMed  PubMed Central  Google Scholar 

  113. Tregouet, D. A. et al. Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat. Genet. 41, 283–285 (2009).

    CAS  PubMed  Google Scholar 

  114. El-Sayed Moustafa, J. S. et al. Novel association approach for variable number tandem repeats (VNTRs) identifies DOCK5 as a susceptibility gene for severe obesity. Hum. Mol. Genet. 21, 3727–3738 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Stacey, S. N. et al. Insertion of an SVA-E retrotransposon into the CASP8 gene is associated with protection against prostate cancer. Hum. Mol. Genet. 25, 1008–1018 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. de Vries, P. S. et al. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration. Hum. Mol. Genet. 25, 358–370 (2016).

    PubMed  Google Scholar 

  117. Ma, J., Xiong, M., You, M., Lozano, G. & Amos, C. I. Genome-wide association tests of inversions with application to psoriasis. Hum. Genet. 133, 967–974 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Reich, D. et al. Reconstructing Native American population history. Nature 488, 370–374 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Reich, D., Thangaraj, K., Patterson, N., Price, A. L. & Singh, L. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Fu, Q. et al. The genetic history of Ice Age Europe. Nature 534, 200–205 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Lazaridis, I. et al. Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513, 409–413 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Price, A. L. et al. Discerning the ancestry of European Americans in genetic association studies. PLOS Genet. 4, e236 (2008).

    PubMed  PubMed Central  Google Scholar 

  123. Jakkula, E. et al. The genome-wide patterns of variation expose significant substructure in a founder population. Am. J. Hum. Genet. 83, 787–794 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Hoggart, C. J. et al. Fine-scale estimation of location of birth from genome-wide single-nucleotide polymorphism data. Genetics 190, 669–677 (2012).

    PubMed  PubMed Central  Google Scholar 

  125. Goode, E. L. & Jarvik, G. P. Assessment and implications of linkage disequilibrium in genome-wide single-nucleotide polymorphism and microsatellite panels. Genet. Epidemiol. 29, S72–S76 (2005).

    PubMed  Google Scholar 

  126. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Ross, S. et al. Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes in the risk of coronary artery disease. Eur. Heart J. 36, 1454–1462 (2015).

    PubMed  Google Scholar 

  128. Liu, H. Y. et al. Fine-mapping of 98 obesity loci in Mexican children. Int. J. Obes. 43, 23–32 (2018).

    Google Scholar 

  129. Homer, N. et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLOS Genet. 4, e1000167 (2008).

    PubMed  PubMed Central  Google Scholar 

  130. Kling, D. et al. DNA microarray as a tool in establishing genetic relatedness — current status and future prospects. Forens. Sci. Int. Genet. 6, 322–329 (2012).

    CAS  Google Scholar 

  131. Ramstetter, M. D. et al. Benchmarking relatedness inference methods with genome-wide data from thousands of relatives. Genetics 207, 75–82 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Kerr, S. M. et al. Pedigree and genotyping quality analyses of over 10,000 DNA samples from the Generation Scotland: Scottish Family Health Study. BMC Med. Genet. 14, 38 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Katsanis, S. H. & Katsanis, N. Molecular genetic testing and the future of clinical genomics. Nat. Rev. Genet. 14, 415–426 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Srebniak, M. I. et al. Prenatal SNP array testing in 1000 fetuses with ultrasound anomalies: causative, unexpected and susceptibility CNVs. Eur. J. Hum. Genet. 24, 645–651 (2016).

    PubMed  Google Scholar 

  135. Treff, N. R. et al. Single nucleotide polymorphism microarray-based concurrent screening of 24-chromosome aneuploidy and unbalanced translocations in preimplantation human embryos. Fertil. Steril. 95, 1606–1612 (2011).

    CAS  PubMed  Google Scholar 

  136. Treff, N. R. et al. A novel single-cell DNA fingerprinting method successfully distinguishes sibling human embryos. Fertil. Steril. 94, 477–484 (2010).

    CAS  PubMed  Google Scholar 

  137. Rosenfeld, J. A. et al. Diagnostic utility of microarray testing in pregnancy loss. Ultrasound Obstet. Gynecol. 46, 478–486 (2015).

    CAS  PubMed  Google Scholar 

  138. Monzon, F. A. et al. Whole genome SNP arrays as a potential diagnostic tool for the detection of characteristic chromosomal aberrations in renal epithelial tumors. Mod. Pathol. 21, 599–608 (2008).

    CAS  PubMed  Google Scholar 

  139. Deng, W. Q. & Pare, G. A fast algorithm to optimize SNP prioritization for gene–gene and gene–environment interactions. Genet. Epidemiol. 35, 729–738 (2011).

    PubMed  Google Scholar 

  140. Bonnefond, A. et al. Molecular diagnosis of neonatal diabetes mellitus using next-generation sequencing of the whole exome. PLOS ONE 5, e13630 (2010).

    PubMed  PubMed Central  Google Scholar 

  141. Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Zhang, Y., Qi, G., Park, J. H. & Chatterjee, N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326 (2018).

    CAS  PubMed  Google Scholar 

  143. Park, J. H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570–575 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Xiao, Y., Segal, M. R., Yang, Y. H. & Yeh, R. F. A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays. Bioinformatics 23, 1459–1467 (2007).

    CAS  PubMed  Google Scholar 

  145. Korn, J. M. et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat. Genet. 40, 1253–1260 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Li, G., Gelernter, J., Kranzler, H. R. & Zhao, H. M(3): an improved SNP calling algorithm for Illumina BeadArray data. Bioinformatics 28, 358–365 (2012).

    PubMed  Google Scholar 

  147. Goldstein, J. I. et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Shah, T. S. et al. optiCall: a robust genotype-calling algorithm for rare, low-frequency and common variants. Bioinformatics 28, 1598–1603 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Liu, R., Dai, Z., Yeager, M., Irizarry, R. A. & Ritchie, M. E. KRLMM: an adaptive genotype calling method for common and low frequency variants. BMC Bioinformatics 15, 158 (2014).

    PubMed  PubMed Central  Google Scholar 

  150. Winchester, L., Yau, C. & Ragoussis, J. Comparing CNV detection methods for SNP arrays. Brief. Funct. Genomic Proteomic 8, 353–366 (2009).

    CAS  PubMed  Google Scholar 

  151. Coin, L. J. et al. cnvHap: an integrative population and haplotype-based multiplatform model of SNPs and CNVs. Nat. Methods 7, 541–546 (2010).

    CAS  PubMed  Google Scholar 

  152. Hauser, E., Cremer, N., Hein, R. & Deshmukh, H. Haplotype-based analysis: a summary of GAW16 Group 4 analysis. Genet. Epidemiol. 33, S24–S28 (2009).

    PubMed  PubMed Central  Google Scholar 

  153. Nielsen, R., Paul, J. S., Albrechtsen, A. & Song, Y. S. Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 12, 443–451 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Das, S., Abecasis, G. R. & Browning, B. L. Genotype imputation from large reference panels. Annu. Rev. Genomics Hum. Genet. 19, 73–96 (2018).

    CAS  PubMed  Google Scholar 

  155. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  157. Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

  159. Turner, S. D. qqman: an R package for visualizing GWAS results using QQ and manhattan plots. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/005165v1 (2014).

  160. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Pe’er, I., Yelensky, R., Altshuler, D. & Daly, M. J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    PubMed  Google Scholar 

  163. Pulit, S. L., de With, S. A. & de Bakker, P. I. Resetting the bar: statistical significance in whole-genome sequencing-based association studies of global populations. Genet. Epidemiol. 41, 145–151 (2017).

    PubMed  Google Scholar 

  164. Pasaniuc, B. & Price, A. L. Dissecting the genetics of complex traits using summary association statistics. Nat. Rev. Genet. 18, 117–127 (2017).

    CAS  PubMed  Google Scholar 

  165. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018).

    PubMed  PubMed Central  Google Scholar 

  167. Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).

    PubMed Central  Google Scholar 

  168. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Vilhjalmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Kichaev, G. & Pasaniuc, B. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet. 97, 260–271 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  173. Jensen, P. B., Jensen, L. J. & Brunak, S. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395–405 (2012).

    CAS  PubMed  Google Scholar 

  174. Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).

    CAS  PubMed  Google Scholar 

  175. Sudlow, C. et al. 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 (2015).

    PubMed  PubMed Central  Google Scholar 

  176. Sanchez-Roige, S. et al. Genome-wide association study of delay discounting in 23,217 adult research participants of European ancestry. Nat. Neurosci. 21, 16–18 (2018).

    CAS  PubMed  Google Scholar 

  177. Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    CAS  PubMed  Google Scholar 

  178. Flannick, J. & Florez, J. C. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat. Rev. Genet. 17, 535–549 (2016).

    CAS  PubMed  Google Scholar 

  179. McAllister, E. J. et al. Ten putative contributors to the obesity epidemic. Crit. Rev. Food Sci. Nutr. 49, 868–913 (2009).

    PubMed  PubMed Central  Google Scholar 

  180. Pigeyre, M., Yazdi, F. T., Kaur, Y. & Meyre, D. Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin. Sci. 130, 943–986 (2016).

    CAS  Google Scholar 

  181. Reddon, H., Gueant, J. L. & Meyre, D. The importance of gene–environment interactions in human obesity. Clin. Sci. 130, 1571–1597 (2016).

    CAS  Google Scholar 

  182. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  183. Müller, M. J. et al. The case of GWAS of obesity: does body weight control play by the rules? Int. J. Obes. 42, 1395–1405 (2018).

    Google Scholar 

  184. Felix, J. F. et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum. Mol. Genet. 25, 389–403 (2016).

    CAS  PubMed  Google Scholar 

  185. Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLOS Genet. 11, e1005378 (2015).

    PubMed  PubMed Central  Google Scholar 

  186. Warrington, N. M. et al. A genome-wide association study of body mass index across early life and childhood. Int. J. Epidemiol. 44, 700–712 (2015).

    PubMed  PubMed Central  Google Scholar 

  187. Yu, H. et al. Genome-wide association study suggested the PTPRD polymorphisms were associated with weight gain effects of atypical antipsychotic medications. Schizophr. Bull. 42, 814–823 (2016).

    PubMed  Google Scholar 

  188. Taylor, A. E. et al. Stratification by smoking status reveals an association of CHRNA5-A3-B4 genotype with body mass index in never smokers. PLOS Genet. 10, e1004799 (2014).

    PubMed  PubMed Central  Google Scholar 

  189. Hatoum, I. J. et al. Weight loss after gastric bypass is associated with a variant at 15q26.1. Am. J. Hum. Genet. 92, 827–834 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  190. McCaffery, J. M. et al. Human cardiovascular disease IBC chip-wide association with weight loss and weight regain in the look AHEAD trial. Hum. Hered. 75, 160–174 (2013).

    PubMed  Google Scholar 

  191. Yang, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Meyre, D. et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat. Genet. 41, 157–159 (2009).

    CAS  PubMed  Google Scholar 

  193. Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  194. Blakemore, A. I. et al. A rare variant in the visfatin gene (NAMPT/PBEF1) is associated with protection from obesity. Obesity 17, 1549–1553 (2009).

    CAS  PubMed  Google Scholar 

  195. Liu, Y. J. et al. Genome-wide association scans identified CTNNBL1 as a novel gene for obesity. Hum. Mol. Genet. 17, 1803–1813 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  196. Lu, Y. et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat. Commun. 7, 10495 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  197. Tanaka, T. et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Am. J. Clin. Nutr. 97, 1395–1402 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  198. De Moor, M. H. et al. Genome-wide association study of exercise behavior in Dutch and American adults. Med. Sci. Sports Exerc. 41, 1887–1895 (2009).

    PubMed  PubMed Central  Google Scholar 

  199. Lane, J. M. et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat. Genet. 49, 274–281 (2017).

    CAS  PubMed  Google Scholar 

  200. Kilpelainen, T. O. et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat. Commun. 7, 10494 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  201. Sun, Q. et al. Genome-wide association study identifies polymorphisms in LEPR as determinants of plasma soluble leptin receptor levels. Hum. Mol. Genet. 19, 1846–1855 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  202. Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLOS Genet. 8, e1002607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. Ried, J. S. et al. A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape. Nat. Commun. 7, 13357 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  204. Freimer, N. B. & Mohr, D. C. Integrating behavioural health tracking in human genetics research. Nat. Rev. Genet. 20, 129–130 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  205. Tam, V., Turcotte, M. & Meyre, D. Established and emerging strategies to crack the genetic code of obesity. Obes. Rev. 20, 212–240 (2019).

    CAS  PubMed  Google Scholar 

  206. Yengo, L. et al. Detection and quantification of inbreeding depression for complex traits from SNP data. Proc. Natl Acad. Sci. USA 114, 8602–8607 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  207. Medina-Gomez, C. et al. Challenges in conducting genome-wide association studies in highly admixed multi-ethnic populations: the Generation R Study. Eur. J. Epidemiol. 30, 317–330 (2015).

    PubMed  PubMed Central  Google Scholar 

  208. Li, Y. R. & Keating, B. J. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med. 6, 91 (2014).

    PubMed  PubMed Central  Google Scholar 

  209. Morris, A. P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).

    PubMed  PubMed Central  Google Scholar 

  210. Wood, A. R. et al. Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively. Diabetologia 59, 1214–1221 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  211. Wermter, A. K. et al. Preferential reciprocal transfer of paternal/maternal DLK1 alleles to obese children: first evidence of polar overdominance in humans. Eur. J. Hum. Genet. 16, 1126–1134 (2008).

    CAS  PubMed  Google Scholar 

  212. Joo, J., Kwak, M., Ahn, K. & Zheng, G. A robust genome-wide scan statistic of the Wellcome Trust Case-Control Consortium. Biometrics 65, 1115–1122 (2009).

    PubMed  Google Scholar 

  213. Hoggart, C. J. et al. Novel approach identifies SNPs in SLC2A10 and KCNK9 with evidence for parent-of-origin effect on body mass index. PLOS Genet. 10, e1004508 (2014).

    PubMed  PubMed Central  Google Scholar 

  214. Tukiainen, T. et al. Chromosome X-wide association study identifies loci for fasting insulin and height and evidence for incomplete dosage compensation. PLOS Genet. 10, e1004127 (2014).

    PubMed  PubMed Central  Google Scholar 

  215. Bush, W. S. & Moore, J. H. Chapter 11: genome-wide association studies. PLOS Comput. Biol. 8, e1002822 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  216. Manning, A. K. et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet. Epidemiol. 35, 11–18 (2011).

    PubMed  PubMed Central  Google Scholar 

  217. Aschard, H., Hancock, D. B., London, S. J. & Kraft, P. Genome-wide meta-analysis of joint tests for genetic and gene–environment interaction effects. Hum. Hered. 70, 292–300 (2011).

    PubMed Central  Google Scholar 

  218. Liu, J. Z. et al. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 87, 139–145 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  219. Mishra, A. & Macgregor, S. VEGAS2: software for more flexible gene-based testing. Twin Res. Hum. Genet. 18, 86–91 (2015).

    PubMed  Google Scholar 

  220. Stephens, M. & Balding, D. J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009).

    CAS  PubMed  Google Scholar 

  221. Szymczak, S. et al. Machine learning in genome-wide association studies. Genet. Epidemiol. 33, S51–S57 (2009).

    PubMed  Google Scholar 

  222. Li, A. & Meyre, D. Challenges in reproducibility of genetic association studies: lessons learned from the obesity field. Int. J. Obes. 37, 559–567 (2013).

    CAS  Google Scholar 

  223. Bhattacharjee, S. et al. A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am. J. Hum. Genet. 90, 821–835 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  224. Anderson, C. A. et al. Data quality control in genetic case–control association studies. Nat. Protoc. 5, 1564–1573 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  225. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    PubMed  PubMed Central  Google Scholar 

  226. Muir, P. et al. The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol. 17, 53 (2016).

    PubMed  PubMed Central  Google Scholar 

  227. Richter, B. G. & Sexton, D. P. Managing and analyzing next-generation sequence data. PLOS Comput. Biol. 5, e1000369 (2009).

    PubMed  PubMed Central  Google Scholar 

  228. Mardis, E. R. The $1,000 genome, the $100,000 analysis? Genome Med. 2, 84 (2010).

    PubMed  PubMed Central  Google Scholar 

  229. Dudbridge, F. & Gusnanto, A. Estimation of significance thresholds for genomewide association scans. Genet. Epidemiol. 32, 227–234 (2008).

    PubMed  PubMed Central  Google Scholar 

  230. Hatzikotoulas, K., Gilly, A. & Zeggini, E. Using population isolates in genetic association studies. Brief. Funct. Genomics 13, 371–377 (2014).

    PubMed  PubMed Central  Google Scholar 

  231. Hagg, S. et al. Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity. Hum. Mol. Genet. 24, 6849–6860 (2015).

    PubMed  PubMed Central  Google Scholar 

  232. Liu, Y. J. et al. Biological pathway-based genome-wide association analysis identified the vasoactive intestinal peptide (VIP) pathway important for obesity. Obesity 18, 2339–2346 (2010).

    CAS  PubMed  Google Scholar 

  233. Johansson, A. et al. Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene. Obesity 18, 803–808 (2010).

    CAS  PubMed  Google Scholar 

  234. Du, H. et al. Genetic polymorphisms in the hypothalamic pathway in relation to subsequent weight change — the DiOGenes study. PLOS ONE 6, e17436 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  235. Greenawalt, D. M. et al. A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort. Genome Res. 21, 1008–1016 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  236. Grossman, S. R. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883–886 (2010).

    CAS  PubMed  Google Scholar 

  237. Ioannidis, J. P. Why most published research findings are false. PLOS Med. 2, e124 (2005).

    PubMed  PubMed Central  Google Scholar 

  238. Manolio, T. A. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14, 549–558 (2013).

    CAS  PubMed  Google Scholar 

  239. Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  240. Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).

    CAS  PubMed  Google Scholar 

  241. Pare, G., Asma, S. & Deng, W. Q. Contribution of large region joint associations to complex traits genetics. PLOS Genet. 11, e1005103 (2015). This paper demonstrates the contribution of joint association of multiple weakly associated variants over large chromosomal regions to complex traits.

    PubMed  PubMed Central  Google Scholar 

  242. Frazer, K. A., Murray, S. S., Schork, N. J. & Topol, E. J. Human genetic variation and its contribution to complex traits. Nat. Rev. Genet. 10, 241–251 (2009).

    CAS  PubMed  Google Scholar 

  243. Aschard, H. et al. Inclusion of gene–gene and gene–environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am. J. Hum. Genet. 90, 962–972 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  244. Choquet, H. & Meyre, D. Genetics of obesity: what have we learned? Curr. Genomics 12, 169–179 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  245. Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  246. Zaitlen, N. et al. Leveraging population admixture to characterize the heritability of complex traits. Nat. Genet. 46, 1356–1362 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  247. Mayhew, A. J. & Meyre, D. Assessing the heritability of complex traits in humans: methodological challenges and opportunities. Curr. Genomics 18, 332–340 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  248. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  249. Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  250. McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356–369 (2008).

    CAS  PubMed  Google Scholar 

  251. Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  252. Stryjecki, C., Alyass, A. & Meyre, D. Ethnic and population differences in the genetic predisposition to human obesity. Obes. Rev. 19, 62–80 (2018).

    CAS  PubMed  Google Scholar 

  253. Magi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet. 26, 3639–3650 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  254. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  255. Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at type 2 diabetes susceptibility loci. eLife 7, e31977 (2018).

    PubMed  PubMed Central  Google Scholar 

  256. Ng, M. C. Y. et al. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLOS Genet. 13, e1006719 (2017).

    PubMed  PubMed Central  Google Scholar 

  257. [No authors listed.] Freely associating. Nat. Genet. 22, 1–2 (1999).

  258. Wang, K. et al. Interpretation of association signals and identification of causal variants from genome-wide association studies. Am. J. Hum. Genet. 86, 730–742 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  259. Holm, H. et al. A rare variant in MYH6 is associated with high risk of sick sinus syndrome. Nat. Genet. 43, 316–320 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  260. Thun, G. A. et al. Causal and synthetic associations of variants in the SERPINA gene cluster with α1-antitrypsin serum levels. PLOS Genet. 9, e1003585 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  261. Wray, N. R., Purcell, S. M. & Visscher, P. M. Synthetic associations created by rare variants do not explain most GWAS results. PLOS Biol. 9, e1000579 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  262. Scherag, A. et al. Investigation of a genome wide association signal for obesity: synthetic association and haplotype analyses at the melanocortin 4 receptor gene locus. PLOS ONE 5, e13967 (2010).

    PubMed  PubMed Central  Google Scholar 

  263. Creemers, J. W. et al. Heterozygous mutations causing partial prohormone convertase 1 deficiency contribute to human obesity. Diabetes 61, 383–390 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  264. Voight, B. F. et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLOS Genet. 8, e1002793 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  265. Cortes, A. & Brown, M. A. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101 (2011).

    PubMed  PubMed Central  Google Scholar 

  266. Gong, J. et al. Fine mapping and identification of BMI loci in African Americans. Am. J. Hum. Genet. 93, 661–671 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  267. Bahcall, O. G. iCOGS collection provides a collaborative model. Foreword. Nat. Genet. 45, 343 (2013).

    CAS  PubMed  Google Scholar 

  268. Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  269. Ghoussaini, M. et al. Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation. Nat. Commun. 4, 4999 (2014).

    PubMed  Google Scholar 

  270. Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  271. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Google Scholar 

  272. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  273. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  274. GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Google Scholar 

  275. Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  276. Ward, L. D. & Kellis, M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).

    CAS  PubMed  Google Scholar 

  277. Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  PubMed  Google Scholar 

  278. Gallagher, M. D. & Chen-Plotkin, A. S. The post-GWAS era: from association to function. Am. J. Hum. Genet. 102, 717–730 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  279. Denker, A. & de Laat, W. The second decade of 3C technologies: detailed insights into nuclear organization. Genes Dev. 30, 1357–1382 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  280. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  281. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  PubMed  Google Scholar 

  282. Church, C. et al. Overexpression of Fto leads to increased food intake and results in obesity. Nat. Genet. 42, 1086–1092 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  283. Fischer, J. et al. Inactivation of the Fto gene protects from obesity. Nature 458, 894–898 (2009).

    CAS  PubMed  Google Scholar 

  284. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  285. Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  286. Visscher, P. M., Hill, W. G. & Wray, N. R. Heritability in the genomics era — concepts and misconceptions. Nat. Rev. Genet. 9, 255–266 (2008).

    CAS  PubMed  Google Scholar 

  287. Witte, J. S., Visscher, P. M. & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  288. Buchner, D. A. & Nadeau, J. H. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res. 25, 775–791 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  289. Mackay, T. F. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat. Rev. Genet. 15, 22–33 (2014).

    CAS  PubMed  Google Scholar 

  290. Wei, W. H., Hemani, G. & Haley, C. S. Detecting epistasis in human complex traits. Nat. Rev. Genet. 15, 722–733 (2014).

    CAS  PubMed  Google Scholar 

  291. Okada, Y. et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in East Asian populations. Nat. Genet. 44, 302–306 (2012).

    CAS  PubMed  Google Scholar 

  292. Cortes, A. et al. Major histocompatibility complex associations of ankylosing spondylitis are complex and involve further epistasis with ERAP1. Nat. Commun. 6, 7146 (2015).

    PubMed  Google Scholar 

  293. Wang, K., Bucan, M., Grant, S. F., Schellenberg, G. & Hakonarson, H. Strategies for genetic studies of complex diseases. Cell 142, 351–353 (2010).

    CAS  PubMed  Google Scholar 

  294. Liu, L., Zhang, D., Liu, H. & Arendt, C. Robust methods for population stratification in genome wide association studies. BMC Bioinformatics 14, 132 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  295. Hinney, A. et al. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLOS ONE 2, e1361 (2007).

    PubMed  PubMed Central  Google Scholar 

  296. Wang, K. et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459, 528–533 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  297. Ma, D. et al. A genome-wide association study of autism reveals a common novel risk locus at 5p14.1. Ann. Hum. Genet. 73, 263–273 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  298. Loos, R. J. F. & Janssens, A. Predicting polygenic obesity using genetic information. Cell Metab. 25, 535–543 (2017).

    CAS  PubMed  Google Scholar 

  299. Janssens, A. C. & van Duijn, C. M. Genome-based prediction of common diseases: advances and prospects. Hum. Mol. Genet. 17, R166–R173 (2008).

    CAS  PubMed  Google Scholar 

  300. Janssens, A. C. et al. The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet. Med. 9, 528–535 (2007).

    PubMed  Google Scholar 

  301. Stutzmann, F. et al. Non-synonymous polymorphisms in melanocortin-4 receptor protect against obesity: the two facets of a Janus obesity gene. Hum. Mol. Genet. 16, 1837–1844 (2007).

    CAS  PubMed  Google Scholar 

  302. Ichimura, A. et al. Dysfunction of lipid sensor GPR120 leads to obesity in both mouse and human. Nature 483, 350–354 (2012).

    CAS  PubMed  Google Scholar 

  303. Challis, B. G. et al. A missense mutation disrupting a dibasic prohormone processing site in pro-opiomelanocortin (POMC) increases susceptibility to early-onset obesity through a novel molecular mechanism. Hum. Mol. Genet. 11, 1997–2004 (2002).

    CAS  PubMed  Google Scholar 

  304. Bonnefond, A. et al. Eating behavior, low-frequency functional mutations in the melanocortin-4 receptor (MC4R) gene, and outcomes of bariatric operations: a 6-year prospective study. Diabetes Care 39, 1384–1392 (2016).

    CAS  PubMed  Google Scholar 

  305. Kuhnen, P. et al. Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. N. Engl. J. Med. 375, 240–246 (2016).

    PubMed  Google Scholar 

  306. Farooqi, I. S. et al. Effects of recombinant leptin therapy in a child with congenital leptin deficiency. N. Engl. J. Med. 341, 879–884 (1999).

    CAS  PubMed  Google Scholar 

  307. Collet, T. H. et al. Evaluation of a melanocortin-4 receptor (MC4R) agonist (setmelanotide) in MC4R deficiency. Mol. Metab. 6, 1321–1329 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  308. Clement, K. et al. MC4R agonism promotes durable weight loss in patients with leptin receptor deficiency. Nat. Med. 24, 551–555 (2018).

    CAS  PubMed  Google Scholar 

  309. Manning, A. et al. A low-frequency inactivating AKT2 variant enriched in the Finnish population is associated with fasting insulin levels and type 2 diabetes risk. Diabetes 66, 2019–2032 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  310. Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018). This is a recent in-depth review on the emerging personal and clinical utility of PRSs.

    CAS  PubMed  Google Scholar 

  311. Gronberg, H. et al. Prostate cancer screening in men aged 50–69 years (STHLM3): a prospective population-based diagnostic study. Lancet Oncol. 16, 1667–1676 (2015).

    PubMed  Google Scholar 

  312. Maas, P. et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2, 1295–1302 (2016).

    PubMed  PubMed Central  Google Scholar 

  313. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018). This paper demonstrates the utility of using PRSs to identify individuals with a level of risk equivalent to that of rare monogenic mutations.

    CAS  PubMed  PubMed Central  Google Scholar 

  314. Theriault, S. et al. Polygenic contribution in individuals with early-onset coronary artery disease. Circ. Genom. Precis. Med. 11, e001849 (2018).

    PubMed  Google Scholar 

  315. Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

    PubMed  PubMed Central  Google Scholar 

  316. Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  317. Meisel, S. F., Beeken, R. J., van Jaarsveld, C. H. & Wardle, J. Genetic susceptibility testing and readiness to control weight: results from a randomized controlled trial. Obesity 23, 305–312 (2015).

    PubMed  Google Scholar 

  318. Meisel, S. F., Walker, C. & Wardle, J. Psychological responses to genetic testing for weight gain: a vignette study. Obesity 20, 540–546 (2012).

    CAS  PubMed  Google Scholar 

  319. Xing, C. et al. Evaluation of power of the Illumina HumanOmni5M-4v1 BeadChip to detect risk variants for human complex diseases. Eur. J. Hum. Genet. 24, 1029–1034 (2016).

    CAS  PubMed  Google Scholar 

  320. Abecasis, G. R. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

    PubMed  Google Scholar 

  321. Rosenberg, N. A. et al. Genome-wide association studies in diverse populations. Nat. Rev. Genet. 11, 356–366 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  322. Hoffmann, T. J. et al. Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. Genomics 98, 422–430 (2011).

    CAS  PubMed  Google Scholar 

  323. Southam, L. et al. Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits. Nat. Commun. 8, 15606 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  324. Mitt, M. et al. Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel. Eur. J. Hum. Genet. 25, 869–876 (2017).

    PubMed  PubMed Central  Google Scholar 

  325. Tachmazidou, I. et al. Whole-genome sequencing coupled to imputation discovers genetic signals for anthropometric traits. Am. J. Hum. Genet. 100, 865–884 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  326. Pigeyre, M. & Meyre, D. in Pediatric Obesity: Etiology, Pathogenesis and Treatment 2nd edn (ed. Freemark, M.) 135–152 (Humana Press, 2018).

  327. Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).

    CAS  PubMed  Google Scholar 

  328. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  329. Hendricks, A. E. et al. Rare variant analysis of human and rodent obesity genes in individuals with severe childhood obesity. Sci. Rep. 7, 4394 (2017).

    PubMed  PubMed Central  Google Scholar 

  330. Steinthorsdottir, V. et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294–298 (2014).

    CAS  PubMed  Google Scholar 

  331. Chatterjee, N., Shi, J. & Garcia-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  332. Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  333. Loos, R. J. et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat. Genet. 40, 768–775 (2008).

  334. Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

  335. Weedon, M. N. et al. A common variant of HMGA2 is associated with adult and childhood height in the general population. Nat. Genet. 39, 1245–1250 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  336. Gudbjartsson, D. F. et al. Many sequence variants affecting diversity of adult human height. Nat. Genet. 40, 609–615 (2008).

    CAS  PubMed  Google Scholar 

  337. Weedon, M. N. et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat. Genet. 40, 575–583 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  338. Lettre, G. et al. Identification of ten loci associated with height highlights new biological pathways in human growth. Nat. Genet. 40, 584–591 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  339. Soranzo, N. et al. Meta-analysis of genome-wide scans for human adult stature identifies novel loci and associations with measures of skeletal frame size. PLoS Genet. 5, e1000445 (2009).

    PubMed  PubMed Central  Google Scholar 

  340. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

  341. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

  342. Heid, I. M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).

  343. Shungin, D. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

  344. Lotta, L. A. et al. Association of genetic variants related to gluteofemoral vs abdominal fat distribution with type 2 diabetes, coronary disease, and cardiovascular risk factors. JAMA 320, 2553–2563 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  345. Jónsson, H. et al. Whole genome characterization of sequence diversity of 15,220 Icelanders. Sci. Data 4, 170115 (2017).

    PubMed  PubMed Central  Google Scholar 

  346. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    Google Scholar 

  347. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

    Google Scholar 

  348. UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015). This is one of the first large-scale attempts to use WGS to identify low-frequency and rare variants associated with common diseases and traits in the general population.

    Google Scholar 

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Acknowledgements

D.M. holds a Canada Research Chair in Genetics of Obesity. Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases. G.P. holds the Canada Research Chair in Genetic and Molecular Epidemiology.

Reviewer information

Nature Reviews Genetics thanks S. Chanock, J. Florez and M. Nelson for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

    Vivian Tam, Nikunj Patel, Michelle Turcotte, Guillaume Paré & David Meyre

  2. Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Québec City, Québec, Canada

    Yohan Bossé

  3. Department of Molecular Medicine, Laval University, Québec City, Quebec, Canada

    Yohan Bossé

  4. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada

    Guillaume Paré & David Meyre

  5. Inserm UMRS 954 N-GERE (Nutrition–Genetics–Environmental Risks), University of Lorraine, Faculty of Medicine, Nancy, France

    David Meyre

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Contributions

V.T., M.T. and D.M. researched the literature. V.T., M.T., Y.B., G.P. and D.M. provided substantial contributions to discussion of the content. V.T., N.P., Y.B. and D.M. wrote the article. M.T., Y.B., G.P. and D.M. reviewed and/or edited the manuscript before submission.

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Glossary

Single-nucleotide variants

(SNVs). Single nucleotides in the genome that vary between individuals in a population. Single-nucleotide polymorphism refers to a SNV that occurs at an appreciable frequency in a population (for example, >1%).

Heritability

The proportion of phenotypic variation between individuals in a population that is due to genetic factors.

Rare variants

Variations in the genome for which the less prevalent form (minor allele) occurs at a frequency of 1% or less in the population.

Imputation

Statistical inference of unobserved genotypes from a reference panel of known haplotypes in a population.

Effect sizes

The magnitudes of the effect of alleles on phenotypic values.

Linkage disequilibrium

The nonrandom association of alleles at two or more loci due to limited recombination.

Haplotype

A set of genetic markers that are present on a single chromosome and in linkage disequilibrium.

Common variants

Variation in the genome for which the less prevalent form (minor allele) occurs at a frequency of 5% or greater in the population.

Minor allele frequencies

(MAFs). The frequencies of the less common allele of a genetic variant in a population.

Copy number variants

(CNVs). A class of DNA sequence variants (including deletions and duplications) that lead to a departure from the expected diploid representation of DNA sequence.

Clonal mosaicism

The presence of clones of cells with different karyotypes within an individual derived from a single zygote.

Fine-mapping

The process of localizing association signals to causal variants using statistical, bioinformatic or functional methods.

Epistasis

Statistical interaction between loci in their effect on a trait such that the effect of a genotype at one locus is dependent on the genotypes at the other locus (or loci).

Population stratification

Differences in allele frequencies between cases and controls resulting from systematic differences in ancestry rather than association of genes with disease.

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Tam, V., Patel, N., Turcotte, M. et al. Benefits and limitations of genome-wide association studies. Nat Rev Genet 20, 467–484 (2019). https://doi.org/10.1038/s41576-019-0127-1

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