Elliott Tucker-Drob - Associate Professor of Psychology at the University of Texas at Austin and the UT Austin Population Research Center

Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

    Date:  01/29/2019 (Tue)

    Time:  3:00pm- 5:00pm

    Location:  Seminar will be held on-site: Erwin Mill, Room A103

    Organizer:  Laura Satterfield

Meeting Schedule: (Not currently open for scheduling. Please contact the seminar organizer listed above.)

    All meetings will be held in the same location as the seminar unless otherwise noted.

    3:00pm - Seminar Presentation (3:00pm to 5:00pm)

    Additional Comments:  Abstract: Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced 'atlases' of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.