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Open Access Research article

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

Fangyu Li1, Jinghua Zhao2, Zhongshang Yuan1, Xiaoshuai Zhang1, Jiadong Ji1 and Fuzhong Xue1*

Author Affiliations

1 Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan 250012, China

2 MRC Epidemiology Unit& Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge CB20QQ, UK

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BMC Genetics 2013, 14:89  doi:10.1186/1471-2156-14-89

Published: 23 September 2013

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 data in the EPIC-Norfolk GWAS sub-cohort showed suggestive interaction (γ) between TMEM18 gene and BDNF gene on two composite body shape scores (γ = 0.047 and γ = 0.058, with P = 0.021, P = 0.005), and BMI (γ = 0.043, P = 0.034). This suggested these scores (synthetically latent traits) were more suitable to capture the obesity related genetic interaction effect between genes compared to single trait.

Conclusions

The proposed novel mPLSPM statistic is a valid and powerful gene-based method for detecting gene-gene interaction on multiple quantitative phenotypes.

Keywords:
Thinking quantitatively for complex diseases; Gene-based gene-gene interaction; Quantitative traits; mPLSPM statistic