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.
%0 Journal Article
%1 buliksullivan2015atlas
%A Bulik-Sullivan, B
%A Finucane, H K
%A Anttila, V
%A Gusev, A
%A Day, F R
%A Loh, P R
%A ReproGen Consortium,
%A Psychiatric Genomics Consortium,
%A Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3,
%A Duncan, L
%A Perry, J R
%A Patterson, N
%A Robinson, E B
%A Daly, M J
%A Price, A L
%A Neale, B M
%D 2015
%J Nat Genet
%K GWAS LD_score genetic_correlation human_genome methods
%N 11
%P 1236-1241
%R 10.1038/ng.3406
%T An atlas of genetic correlations across human diseases and traits
%U https://www.ncbi.nlm.nih.gov/pubmed/26414676
%V 47
%X 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.
@article{buliksullivan2015atlas,
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. },
added-at = {2018-07-04T19:33:02.000+0200},
author = {Bulik-Sullivan, B and Finucane, H K and Anttila, V and Gusev, A and Day, F R and Loh, P R and {ReproGen Consortium} and {Psychiatric Genomics Consortium} and {Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3} and Duncan, L and Perry, J R and Patterson, N and Robinson, E B and Daly, M J and Price, A L and Neale, B M},
biburl = {https://www.bibsonomy.org/bibtex/2c5cd08b64e5fa76043a7ebb5c2e36a0c/peter.ralph},
doi = {10.1038/ng.3406},
interhash = {302761930a7c431245359f809b447b91},
intrahash = {c5cd08b64e5fa76043a7ebb5c2e36a0c},
journal = {Nat Genet},
keywords = {GWAS LD_score genetic_correlation human_genome methods},
month = nov,
number = 11,
pages = {1236-1241},
pmid = {26414676},
timestamp = {2018-07-04T19:33:02.000+0200},
title = {An atlas of genetic correlations across human diseases and traits},
url = {https://www.ncbi.nlm.nih.gov/pubmed/26414676},
volume = 47,
year = 2015
}