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Detection and interpretation of shared genetic influences on 42 human traits
- Joseph K Pickrell1,2,
- Tomaz Berisa1,
- Jimmy Z Liu ORCID:orcid.org/0000-0002-8379-54801,
- Laure Ségurel3,
- Joyce Y Tung4 &
- …
- David A Hinds4
Nature Geneticsvolume 48, pages709–717 (2016)Cite this article
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AnErratum to this article was published on 28 September 2016
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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 transporterSLC39A8 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
13 June 2016
In the version of this article initially published online, the label on they 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.
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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 fromhttp://www.magicinvestigators.org/. Data on CAD and myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded fromhttp://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.
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Authors and Affiliations
New York Genome Center, New York, New York, USA
Joseph K Pickrell, Tomaz Berisa & Jimmy Z Liu
Department of Biological Sciences, Columbia University, New York, New York, USA
Joseph K Pickrell
UMR 7206 Eco-Anthropologie et Ethnobiologie, CNRS, MNHN, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
Laure Ségurel
23andMe, Inc., Mountain View, California, USA
Joyce Y Tung & David A Hinds
- Joseph K Pickrell
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- Tomaz Berisa
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- Jimmy Z Liu
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- Laure Ségurel
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- Joyce Y Tung
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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 theABO region. J.Y.T. and D.A.H. performed and analyzed the studies from 23andMe. All authors contributed to the writing of the manuscript.
Corresponding author
Correspondence toJoseph K Pickrell.
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J.Y.T. and D.A.H. are employees of the company 23andMe.
Supplementary information
Supplementary Text and Figures
Supplementary Note, Supplementary Figures 1–18 and Supplementary Table 2. (PDF 2412 kb)
Supplementary Table 1
Genomic regions that contain a variant that influences more than one trait. (TXT 81 kb)
Supplementary Data 1
23andMe analysis summary: age of first menses. (PDF 6120 kb)
Supplementary Data 2
23andMe analysis summary: age of voice deepening. (PDF 1423 kb)
Supplementary Data 3
23andMe analysis summary: any allergy. (PDF 3138 kb)
Supplementary Data 4
23andMe analysis summary: any asthma. (PDF 2591 kb)
Supplementary Data 5
23andMe analysis summary: breast size. (PDF 2190 kb)
Supplementary Data 6
23andMe analysis summary: childhood ear infections. (PDF 2169 kb)
Supplementary Data 7
23andMe analysis summary: hypermobility Beighton score. (PDF 2448 kb)
Supplementary Data 8
23andMe analysis summary: hypothyroidism. (PDF 2333 kb)
Supplementary Data 9
23andMe analysis summary: male pattern baldness. (PDF 3708 kb)
Supplementary Data 10
23andMe analysis summary: migraine. (PDF 2430 kb)
Supplementary Data 11
23andMe analysis summary: nearsightedness. (PDF 4119 kb)
Supplementary Data 12
23andMe analysis summary: Parkinson disease. (PDF 2709 kb)
Supplementary Data 13
23andMe analysis summary: photic sneeze. (PDF 3833 kb)
Supplementary Data 14
23andMe analysis summary: tonsillectomy. (PDF 3043 kb)
Supplementary Data 15
23andMe analysis summary: unibrow. (PDF 3796 kb)
Supplementary Data 16
23andMe analysis summary: morphology, chin dimple. (PDF 3714 kb)
Supplementary Data 17
23andMe analysis summary: nose size. (PDF 2179 kb)
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Pickrell, J., Berisa, T., Liu, J.et al. Detection and interpretation of shared genetic influences on 42 human traits.Nat Genet48, 709–717 (2016). https://doi.org/10.1038/ng.3570
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