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A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics

By Qiongshi Lu, Boyang Li, Derek Ou, Margret Erlendsdottir, Ryan L Powles, Tony Jiang, Yiming Hu, David Chang, Chentian Jin, Wei Dai, Qidu He, Zefeng Liu, Shubhabrata Mukherjee, Paul K Crane, Hongyu Zhao

Posted 07 Mar 2017
bioRxiv DOI: 10.1101/114561 (published DOI: 10.1016/j.ajhg.2017.11.001)

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 (N~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.

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