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

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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. © 2013 Li et al.; licensee BioMed Central Ltd.

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Li, F., Zhao, J., Yuan, Z., Zhang, X., Ji, J., & Xue, F. (2013). A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits. BMC Genetics, 14. https://doi.org/10.1186/1471-2156-14-89

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