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Beyond power: Multivariate discovery, replication, and interpretation of pleiotropic loci using summary association statistics

By Zheng Ning, Yakov A. Tsepilov, Sodbo Zh. Sharapov, Alexander K. Grishenko, Xiao Feng, Masoud Shirali, Peter K. Joshi, James F Wilson, Yudi Pawitan, Chris S. Haley, Yurii S Aulchenko, Xia Shen

Posted 08 Jul 2015
bioRxiv DOI: 10.1101/022269

The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods which consider variant association with multiple traits jointly have been developed. However, most effort has been put on improving discovery power: how to replicate and interpret these discovered pleiotropic loci using multivariate methods has yet to be discussed fully. Using only multiple publicly available single-trait GWAS summary statistics, we develop a fast and flexible multi-trait framework that contains modules for (i) multi-trait genetic discovery, (ii) replication of locus pleiotropic profile, and (iii) multi-trait conditional analysis. The procedure is able to handle any level of sample overlap. As an empirical example, we discovered and replicated 23 novel pleiotropic loci for human anthropometry and evaluated their pleiotropic effects on other traits. By applying conditional multivariate analysis on the 23 loci, we discovered and replicated two additional multi-trait associated SNPs. Our results provide empirical evidence that multi-trait analysis allows detection of additional, replicable, highly pleiotropic genetic associations without genotyping additional individuals. The methods are implemented in a free and open source R package MultiABEL.

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