Date of Original Version

6-15-2012

Type

Article

PubMed ID

22689753

Rights Management

© The Author(s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract or Description

MOTIVATION: As many complex disease and expression phenotypes are the outcome of intricate perturbation of molecular networks underlying gene regulation resulted from interdependent genome variations, association mapping of causal QTLs or expression quantitative trait loci must consider both additive and epistatic effects of multiple candidate genotypes. This problem poses a significant challenge to contemporary genome-wide-association (GWA) mapping technologies because of its computational complexity. Fortunately, a plethora of recent developments in biological network community, especially the availability of genetic interaction networks, make it possible to construct informative priors of complex interactions between genotypes, which can substantially reduce the complexity and increase the statistical power of GWA inference.

RESULTS: In this article, we consider the problem of learning a multitask regression model while taking advantage of the prior information on structures on both the inputs (genetic variations) and outputs (expression levels). We propose a novel regularization scheme over multitask regression called jointly structured input-output lasso based on an ℓ(1)/ℓ(2) norm, which allows shared sparsity patterns for related inputs and outputs to be optimally estimated. Such patterns capture multiple related single nucleotide polymorphisms (SNPs) that jointly influence multiple-related expression traits. In addition, we generalize this new multitask regression to structurally regularized polynomial regression to detect epistatic interactions with manageable complexity by exploiting the prior knowledge on candidate SNPs for epistatic effects from biological experiments. We demonstrate our method on simulated and yeast eQTL datasets.

AVAILABILITY: Software is available at http://www.sailing.cs.cmu.edu/.

DOI

10.1093/bioinformatics/bts227

Creative Commons


This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License

Share

COinS
 

Published In

Bioinformatics, 28, 12, 137-146.