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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


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.


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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 Data on CAD and myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from

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


  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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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.

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    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.

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    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.

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    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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