Modeling genetic pleiotropy using machine learning to understand the human genetic architecture

Project Summary

Nowadays in human genetics, one particular concept seems to resurge: pleiotropy. Pleiotropy occurs when one genetic element (e.g. variant, gene) has independent effects on several traits. Although pleiotropy is extremely common and thought to play a central role in the genetic architecture of human complex traits and diseases, it is one of the least understood phenomena. We have shown that several biological mechanisms exist and induce different pleiotropy states at the level of the variants. Specifically, we have conceptualized 5 biological mechanisms 1) linkage disequilibrium; 2) causality between traits; 3) genetic correlation between traits; 4) high polygenicity of traits; 5) horizontal pleiotropy (true independent effects of a variant on two traits). This internship will be dedicated to building a comprehensive framework to disentangle all 5 states of pleiotropy and provide a genome-wide map of pleiotropy using machine learning. Specifically, we propose 1) to improve on a method that we have published in a proof of-concept paper using unsupervised approaches based on penalized methods, random forests or deep learning; 2) to explore semi-supervised learning using a creative strategy to label data that we have developed.

Marie Verbanck