ArticleBMC genetics2013
A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits.
Article in BMC genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Gene-gene interaction detection with deep learning.Communications biology · 2022Article
- Using Composite Phenotypes to Reveal Hidden Physiological Heterogeneity in High-Altitude Acclimatization in a Chinese Han Longitudinal Cohort.Phenomics (Cham, Switzerland) · 2021Article
- Assessing genetic and environmental influences on epicardial and abdominal adipose tissue quantities: a classical twin study.International journal of obesity (2005) · 2018Article
- A gene-based information gain method for detecting gene-gene interactions in case-control studies.European journal of human genetics : EJHG · 2015Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
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6 authors.
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Abstract
backgroundOn 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.
resultsSimulation 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.
conclusionsThe proposed novel mPLSPM statistic is a valid and powerful gene-based method for detecting gene-gene interaction on multiple quantitative phenotypes.
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