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An Atlas of Genetic Correlations across Human Diseases and Traits

Brendan Bulik-Sullivan, Hilary K Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Consortium, John R. B. Perry, Nick Patterson, Elise Robinson, Mark J. Daly, Alkes L. Price, Benjamin M. Neale
doi: https://doi.org/10.1101/014498
Brendan Bulik-Sullivan
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
3Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
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  • For correspondence: bulik@broadinstitute.org bneale@broadinstitute.org
Hilary K Finucane
4Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Verneri Anttila
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
3Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
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Alexander Gusev
5Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
6Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Felix R. Day
7MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, USA
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ReproGen Consortium
8A list of members and affiliations appears in the Supplementary Note.
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8A list of members and affiliations appears in the Supplementary Note.
8A list of members and affiliations appears in the Supplementary Note.
John R. B. Perry
7MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, USA
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Nick Patterson
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Elise Robinson
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
3Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
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Mark J. Daly
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
3Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
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Alkes L. Price
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
5Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
6Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Benjamin M. Neale
1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
3Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
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  • For correspondence: bulik@broadinstitute.org bneale@broadinstitute.org
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Abstract

Identifying genetic correlations between complex traits and diseases can provide useful etio-logical insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and schizophrenia/ body mass index and associations between educational attainment and several diseases. These results highlight the power of a polygenic modeling framework, since there currently are no genome-wide significant SNPs for anorexia nervosa and only three for educational attainment.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 27, 2015.
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An Atlas of Genetic Correlations across Human Diseases and Traits
Brendan Bulik-Sullivan, Hilary K Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Consortium, John R. B. Perry, Nick Patterson, Elise Robinson, Mark J. Daly, Alkes L. Price, Benjamin M. Neale
bioRxiv 014498; doi: https://doi.org/10.1101/014498
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An Atlas of Genetic Correlations across Human Diseases and Traits
Brendan Bulik-Sullivan, Hilary K Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Consortium, John R. B. Perry, Nick Patterson, Elise Robinson, Mark J. Daly, Alkes L. Price, Benjamin M. Neale
bioRxiv 014498; doi: https://doi.org/10.1101/014498

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