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Propagating consequences of molecular mechanisms into complex phenotypes

Banff International Research Station for Mathematical Innovation and Discovery

To understand the biological mechanisms underlying thousands of genetic variants robustly associated with complex traits, scalable methods that integrate GWAS and functional data generated by large-scale efforts are needed. We have proposed a method termed MetaXcan that addresses this need by inferring the downstream consequences of genetically regulated components of molecular traits on complex phenotypes using summary data only. MetaXcan allows multiple causal variants and flexible multivariate models extending the capabilities of existing methods and enabling the testing of complex processes. The application to prediction models of gene expression levels in 44 human tissues and 100+ complex phenotypes revealed many novel genes and re-identified known ones with patterns of regulation in expected as well as unexpected tissues. Prediction models of miRNA showed the potential to identify novel mRNA targets.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: University of Chicago

2017-09-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63126

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/