An atlas of genetic correlations across human diseases and traits

Brendan Bulik-Sullivan; Hilary K Finucane; Verneri Anttila; Alexander Gusev; Felix R Day; Po-Ru Loh; Laramie Duncan; John R B Perry; Nick Patterson; Elise B Robinson; Mark J Daly; Alkes L Price; Benjamin M Neale; ReproGen Consortium

doi:10.1038/ng.3406

An atlas of genetic correlations across human diseases and traits

Brendan Bulik-Sullivan, Hilary K Finucane, Verneri Anttila, Alexander Gusev, Felix R Day, Po-Ru Loh, Laramie Duncan, John R B Perry, Nick Patterson, Elise B Robinson, Mark J Daly, Alkes L Price, Benjamin M Neale, ReproGen Consortium

3093 Citations (Scopus)

Abstract

Identifying genetic correlations between complex traits and diseases can provide useful etiological 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-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

Original language	English
Journal	Nature Genetics
Volume	47
Issue number	11
Pages (from-to)	1236-41
Number of pages	6
ISSN	1061-4036
DOIs	https://doi.org/10.1038/ng.3406
Publication status	Published - Nov 2015

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title = "An atlas of genetic correlations across human diseases and traits",

abstract = "Identifying genetic correlations between complex traits and diseases can provide useful etiological 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-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.",

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year = "2015",

month = nov,

doi = "10.1038/ng.3406",

language = "English",

volume = "47",

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journal = "Nature Genetics",

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T1 - An atlas of genetic correlations across human diseases and traits

AU - Bulik-Sullivan, Brendan

AU - Finucane, Hilary K

AU - Anttila, Verneri

AU - Gusev, Alexander

AU - Day, Felix R

AU - Loh, Po-Ru

AU - Duncan, Laramie

AU - Perry, John R B

AU - Patterson, Nick

AU - Robinson, Elise B

AU - Daly, Mark J

AU - Price, Alkes L

AU - Neale, Benjamin M

AU - ReproGen Consortium

AU - Hansen, Thomas Folkmann

AU - Werge, Thomas Mears

AU - Olsen, Line

PY - 2015/11

Y1 - 2015/11

N2 - Identifying genetic correlations between complex traits and diseases can provide useful etiological 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-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

AB - Identifying genetic correlations between complex traits and diseases can provide useful etiological 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-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

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DO - 10.1038/ng.3406

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JO - Nature Genetics

JF - Nature Genetics

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An atlas of genetic correlations across human diseases and traits

Abstract

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