TY - JOUR T1 - A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics JF - bioRxiv DO - 10.1101/114561 SP - 114561 AU - Qiongshi Lu AU - Boyang Li AU - Derek Ou AU - Margret Erlendsdottir AU - Ryan L. Powles AU - Tony Jiang AU - Yiming Hu AU - David Chang AU - Chentian Jin AU - Wei Dai AU - Qidu He AU - Zefeng Liu AU - Shubhabrata Mukherjee AU - Paul K. Crane AU - Hongyu Zhao Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/03/07/114561.abstract N2 - Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits’ genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses we demonstrate that our method provides accurate covariance estimates, thus enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal ≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer’s disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in SNPs with high minor allele frequencies and SNPs in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD’s correlation with cognitive traits and hints at an autoimmune component for ALS. ER -