  Many complex disease syndromes such as asthma consist of a large number of
highly related, rather than independent, clinical phenotypes, raising a new
technical challenge in identifying genetic variations associated simultaneously
with correlated traits. In this study, we propose a new statistical framework
called graph-guided fused lasso (GFlasso) to address this issue in a principled
way. Our approach explicitly represents the dependency structure among the
quantitative traits as a network, and leverages this trait network to encode
structured regularizations in a multivariate regression model over the
genotypes and traits, so that the genetic markers that jointly influence
subgroups of highly correlated traits can be detected with high sensitivity and
specificity. While most of the traditional methods examined each phenotype
independently and combined the results afterwards, our approach analyzes all of
the traits jointly in a single statistical method, and borrow information
across correlated phenotypes to discover the genetic markers that perturbe a
subset of correlated triats jointly rather than a single trait. Using simulated
datasets based on the HapMap consortium data and an asthma dataset, we compare
the performance of our method with the single-marker analysis, and other sparse
regression methods such as the ridge regression and the lasso that do not use
any structural information in the traits. Our results show that there is a
significant advantage in detecting the true causal SNPs when we incorporate the
correlation pattern in traits using our proposed methods.
