Nat Genet. 2015 Nov;47(11):1236-41. doi: 10.1038/ng.3406. Epub 2015 Sep 28.

An atlas of genetic correlations across human diseases and traits.

Bulik-Sullivan B^1,^2,³, Finucane HK⁴, Anttila V^1,^2,³, Gusev A^5,⁶, Day FR⁷, Loh PR^1,⁵; ReproGen Consortium; Psychiatric Genomics Consortium; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Duncan L^1,^2,³, Perry JR⁷, Patterson N¹, Robinson EB^1,^2,³, Daly MJ^1,^2,³, Price AL^1,^5,⁶, Neale BM^1,^2,³.

Author information

1: Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
2: Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
3: Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
4: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
5: Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
6: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
7: Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK.

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.