Seyoung Kim, Kyung-Ah Sohn, Ross Curtis, Eric P. Xing
School of Computer Science
Carnegie Mellon University
Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. However, most of the conventional approaches for association mapping or eQTL analysis consider a single phenotype at a time instead of taking advantage of the relatedness of traits by analyzing them jointly.
Assuming that a group of tightly correlated traits may share a common genetic basis, we introduce graph-guided fused lasso (GFlasso) for association analysis that searches for genetic variations influencing a group of correlated traits. We explicitly represent the correlation information in multiple quantitative traits as a quantitative trait network, and directly incorporate this network information to scan the genome for association. Our results on asthma data with 53 clinical traits and 34 SNP markers for 543 asthma patients show that our approach has a significant advantage in detecting associations when a genetic marker perturbs synergistically a group of traits.