A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits.

ABSTRACT:

Background

On thinking quantitatively of complex diseases, there are at least three statistical strategies for analyzing the gene-gene interaction: SNP by SNP interaction on single trait, gene-gene (each can involve multiple SNPs) interaction on single trait and gene-gene interaction on multiple traits. The third one is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. In this paper, we developed a novel statistic for this strategy through modifying the Partial Least Squares Path Modeling (PLSPM), called mPLSPM statistic.

Results

Simulation studies indicated that mPLSPM statistic was powerful and outperformed the principal component analysis (PCA) based linear regression method. Application to real dat

SUBMITTER: Li F 

PROVIDER: S-EPMC3848962 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature