ArticleHuman heredity2014
Analysis of gene-gene interactions using gene-trait similarity regression.
Article in Human heredity, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models.Scientific reports · 2017Article
- An Exploration of Gene-Gene Interactions and Their Effects on Hypertension.International journal of genomics · 2017Article
- A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.Genetic epidemiology · 2015Article
- Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Genetic epidemiology · 2015Article
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Authors and funding
3 authors.
Funding
Abstract
objectiveGene-gene interactions (G×G) are important to study because of their extensiveness in biological systems and their potential in explaining missing heritability of complex traits. In this work, we propose a new similarity-based test to assess G×G at the gene level, which permits the study of epistasis at biologically functional units with amplified interaction signals.
methodsUnder the framework of gene-trait similarity regression (SimReg), we propose a gene-based test for detecting G×G. SimReg uses a regression model to correlate trait similarity with genotypic similarity across a gene. Unlike existing gene-level methods based on leading principal components (PCs), SimReg summarizes all information on genotypic variation within a gene and can be used to assess the joint/interactive effects of two genes as well as the effect of one gene conditional on another.
resultsUsing simulations and a real data application to the Warfarin study, we show that the SimReg G×G tests have satisfactory power and robustness under different genetic architecture when compared to existing gene-based interaction tests such as PC analysis or partial least squares. A genome-wide association study with approx. 20,000 genes may be completed on a parallel computing system in 2 weeks.
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