A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network

TL;DR

This paper introduces graph-guided fused lasso (GFlasso), a novel statistical framework that leverages trait network structures to improve detection of genetic markers influencing correlated traits in complex diseases.

Contribution

The paper presents GFlasso, a new multivariate regression method that explicitly models trait dependencies to enhance genetic association analysis.

Findings

GFlasso outperforms traditional methods in detecting causal SNPs.

Incorporating trait network structure improves sensitivity and specificity.

Method validated on simulated and real asthma datasets.

Abstract

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.