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Detection and interpretation of shared genetic influences on 42 human traits

Nature Genetics volume 48, pages 709717 (2016) | Download Citation

  • An Erratum to this article was published on 28 September 2016

Abstract

We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10−12) and Parkinson disease (log OR = −0.15, P = 1.6 × 10−7), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.

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Change history

  • Corrected online 13 June 2016

    In the version of this article initially published online, the label on the y axis of the bottom-most plots in Figures 1 and 3a incorrectly included a negative symbol. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. 1.

    One hundred years of pleiotropy: a retrospective. Genetics 186, 767–773 (2010).

  2. 2.

    & The many faces of pleiotropy. Trends Genet. 29, 66–73 (2013).

  3. 3.

    , , , & Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

  4. 4.

    et al. Mutations in the cystic fibrosis gene in patients with congenital absence of the vas deferens. N. Engl. J. Med. 332, 1475–1480 (1995).

  5. 5.

    Xanthomata, hypercholesterolemia, angina pectoris. Acta Med. Scand. 95, 75–84 (1938).

  6. 6.

    Atherogenesis in perspective: hypercholesterolemia and inflammation as partners in crime. Nat. Med. 8, 1211–1217 (2002).

  7. 7.

    Causality: Models, Reasoning and Inference vol. 29 (Cambridge University Press, 2000).

  8. 8.

    The cholesterol controversy is over. Why did it take so long? Circulation 80, 1070–1078 (1989).

  9. 9.

    , , & Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

  10. 10.

    et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).

  11. 11.

    et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197–209 (2013).

  12. 12.

    et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 9, e1003455 (2013).

  13. 13.

    et al. Evaluation of the genetic overlap between osteoarthritis with body mass index and height using genome-wide association scan data. Ann. Rheum. Dis. 72, 935–941 (2013).

  14. 14.

    et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607–618 (2011).

  15. 15.

    et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505, 361–366 (2014).

  16. 16.

    et al. Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature 497, 517–520 (2013).

  17. 17.

    et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

  18. 18.

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

  19. 19.

    et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet. 9, e1003087 (2013).

  20. 20.

    et al. Disease risk factors identified through shared genetic architecture and electronic medical records. Sci. Transl. Med. 6, 234ra57 (2014).

  21. 21.

    Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 1, 507–508 (1986).

  22. 22.

    & Mendelian randomization: prospects, potentials, and limitations. Int. J. Epidemiol. 33, 30–42 (2004).

  23. 23.

    & Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23 R1, R89–R98 (2014).

  24. 24.

    et al. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet 380, 572–580 (2012).

  25. 25.

    et al. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 10, e1004494 (2014).

  26. 26.

    et al. The effect of FTO variation on increased osteoarthritis risk is mediated through body mass index: a Mendelian randomisation study. Ann. Rheum. Dis. 73, 2082–2086 (2014).

  27. 27.

    et al. Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am. J. Hum. Genet. 94, 198–208 (2014).

  28. 28.

    et al. Mendelian randomization studies do not support a role for raised circulating triglyceride levels influencing type 2 diabetes, glucose levels, or insulin resistance. Diabetes 60, 1008–1018 (2011).

  29. 29.

    et al. Effects of BMI, fat mass, and lean mass on asthma in childhood: a Mendelian randomization study. PLoS Med. 11, e1001669 (2014).

  30. 30.

    et al. Serum iron levels and the risk of Parkinson disease: a Mendelian randomization study. PLoS Med. 10, e1001462 (2013).

  31. 31.

    , , & Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat. Rev. Genet. 14, 661–673 (2013).

  32. 32.

    et al. Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat. Genet. 47, 839–846 (2015).

  33. 33.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

  34. 34.

    et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).

  35. 35.

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

  36. 36.

    et al. Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics 30, 2906–2914 (2014).

  37. 37.

    Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

  38. 38.

    et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

  39. 39.

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

  40. 40.

    , , & GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  41. 41.

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

  42. 42.

    & Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).

  43. 43.

    et al. Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514, 92–97 (2014).

  44. 44.

    & Emerging patterns of genetic overlap across autoimmune disorders. Genome Med. 4, 6 (2012).

  45. 45.

    et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

  46. 46.

    et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat. Commun. 6, 5897 (2015).

  47. 47.

    & The intriguing relationship between the ABO blood group, cardiovascular disease, and cancer. BMC Med. 13, 7 (2015).

  48. 48.

    , , , & Transcription of human ABO histo-blood group genes is dependent upon binding of transcription factor CBF/NF-Y to minisatellite sequence. J. Biol. Chem. 272, 25890–25898 (1997).

  49. 49.

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

  50. 50.

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

  51. 51.

    , & Review: a meta-analysis of GWAS and age-associated diseases. Aging Cell 11, 727–731 (2012).

  52. 52.

    et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat. Genet. 42, 1077–1085 (2010).

  53. 53.

    et al. Six novel susceptibility loci for early-onset androgenetic alopecia and their unexpected association with common diseases. PLoS Genet. 8, e1002746 (2012).

  54. 54.

    et al. Male-pattern baldness susceptibility locus at 20p11. Nat. Genet. 40, 1282–1284 (2008).

  55. 55.

    Patterned loss of hair in man; types and incidence. Ann. NY Acad. Sci. 53, 708–728 (1951).

  56. 56.

    et al. Association of schizophrenia and autoimmune diseases: linkage of Danish national registers. Am. J. Psychiatry 163, 521–528 (2006).

  57. 57.

    et al. Coeliac disease and schizophrenia: population based case control study with linkage of Danish national registers. Br. Med. J. 328, 438–439 (2004).

  58. 58.

    et al. Autoimmune diseases and severe infections as risk factors for schizophrenia: a 30-year population-based register study. Am. J. Psychiatry 168, 1303–1310 (2011).

  59. 59.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  60. 60.

    Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 344, 1383–1389 (1994).

  61. 61.

    et al. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the Look AHEAD trial. Diabetes Care 30, 1374–1383 (2007).

  62. 62.

    et al. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. N. Engl. J. Med. 359, 229–241 (2008).

  63. 63.

    et al. Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change. PLoS Med. 11, e1001765 (2014).

  64. 64.

    et al. Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes 57, 1419–1426 (2008).

  65. 65.

    , & Long-term growth in juvenile acquired hypothyroidism: the failure to achieve normal adult stature. N. Engl. J. Med. 318, 599–602 (1988).

  66. 66.

    , , , & Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism–derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

  67. 67.

    et al. Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples. PLoS Genet. 10, e1004269 (2014).

  68. 68.

    , , , & Biological and clinical implications of the MTHFR C677T polymorphism. Trends Pharmacol. Sci. 22, 195–201 (2001).

  69. 69.

    et al. Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med. 12, e1001865 (2015).

  70. 70.

    Assortative mating and the genetic correlation. Theor. Appl. Genet. 62, 225–231 (1982).

  71. 71.

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

  72. 72.

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

  73. 73.

    et al. Mining the human phenome using allelic scores that index biological intermediates. PLoS Genet. 9, e1003919 (2013).

  74. 74.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  75. 75.

    et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

  76. 76.

    et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature (2016).

  77. 77.

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

  78. 78.

    et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

  79. 79.

    et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

  80. 80.

    et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

  81. 81.

    et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

  82. 82.

    et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

  83. 83.

    et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492, 369–375 (2012).

  84. 84.

    et al. New gene functions in megakaryopoiesis and platelet formation. Nature 480, 201–208 (2011).

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Acknowledgements

This work was supported in part by the National Human Genome Research Institute of the National Institutes of Health (grant R44HG006981 to 23andMe) and the National Institute of Mental Health (grant R01MH106842 to J.K.P.). We thank the customers of 23andMe for making this work possible, the GWAS consortia that made summary statistics available to us, L. Jostins for providing updated summary statistics from the Crohn's disease and ulcerative colitis GWAS, and G. Coop and M. Stephens for helpful discussions. We thank D. Golan and J. Pritchard for comments on a previous version of this manuscript. We thank D. Cesarini and the Social Science Genetic Association Consortium for access to summary statistics from the association study of educational attainment.

Data on glycemic traits have been contributed by MAGIC investigators and have been downloaded from http://www.magicinvestigators.org/. Data on CAD and myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from http://www.cardiogramplusc4d.org/.

We thank the International Genomics of Alzheimer's Project (IGAP) for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The iSelect chips were funded by the French National Foundation on Alzheimer disease and related disorders. EADI was supported by LABEX (Laboratory of Excellence program investment for the future) DISTALZ grant, INSERM, Institut Pasteur de Lille, Université de Lille 2, and the Lille University Hospital. GERAD was supported by the Medical Research Council (grant 503480), Alzheimer's Research UK (grant 503176), the Wellcome Trust (grant 082604/2/07/Z), and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grants 01GI0102, 01GI0711, and 01GI0420. CHARGE was partly supported by NIH/NIA grant R01 AG033193 and NIA grant AG081220 and AGES contract N01-AG-12100, NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by NIH/NIA grants U01 AG032984, U24 AG021886, and U01 AG016976, and by Alzheimer's Association grant ADGC-10-196728.

Author information

Affiliations

  1. New York Genome Center, New York, New York, USA.

    • Joseph K Pickrell
    • , Tomaz Berisa
    •  & Jimmy Z Liu
  2. Department of Biological Sciences, Columbia University, New York, New York, USA.

    • Joseph K Pickrell
  3. UMR 7206 Eco-Anthropologie et Ethnobiologie, CNRS, MNHN, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.

    • Laure Ségurel
  4. 23andMe, Inc., Mountain View, California, USA.

    • Joyce Y Tung
    •  & David A Hinds

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Contributions

J.K.P. developed and applied the methods for pairwise analysis of association studies. T.B. contributed to the splitting of GWAS hits into independent blocks. J.Z.L. performed the LD score regression analyses. L.S. contributed to the analysis of the ABO region. J.Y.T. and D.A.H. performed and analyzed the studies from 23andMe. All authors contributed to the writing of the manuscript.

Competing interests

J.Y.T. and D.A.H. are employees of the company 23andMe.

Corresponding author

Correspondence to Joseph K Pickrell.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Note, Supplementary Figures 1–18 and Supplementary Table 2.

  2. 2.

    Supplementary Data 1

    23andMe analysis summary: age of first menses.

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    Supplementary Data 2

    23andMe analysis summary: age of voice deepening.

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    Supplementary Data 3

    23andMe analysis summary: any allergy.

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    Supplementary Data 4

    23andMe analysis summary: any asthma.

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    Supplementary Data 5

    23andMe analysis summary: breast size.

  7. 7.

    Supplementary Data 6

    23andMe analysis summary: childhood ear infections.

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    Supplementary Data 7

    23andMe analysis summary: hypermobility Beighton score.

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    Supplementary Data 8

    23andMe analysis summary: hypothyroidism.

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    Supplementary Data 9

    23andMe analysis summary: male pattern baldness.

  11. 11.

    Supplementary Data 10

    23andMe analysis summary: migraine.

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    Supplementary Data 11

    23andMe analysis summary: nearsightedness.

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    Supplementary Data 12

    23andMe analysis summary: Parkinson disease.

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    Supplementary Data 13

    23andMe analysis summary: photic sneeze.

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    Supplementary Data 14

    23andMe analysis summary: tonsillectomy.

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    Supplementary Data 15

    23andMe analysis summary: unibrow.

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    Supplementary Data 16

    23andMe analysis summary: morphology, chin dimple.

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    Supplementary Data 17

    23andMe analysis summary: nose size.

Text files

  1. 1.

    Supplementary Table 1

    Genomic regions that contain a variant that influences more than one trait.

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https://doi.org/10.1038/ng.3570

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