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Journal Articles Bioinformatics Year : 2018

VarExp: estimating variance explained by genome-wide GxE summary statistics

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Abstract

Many genome-wide association studies and genome-wide screening for gene-environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. This is commonly done using individual-level genotype data but it is challenging when results are derived through meta-analyses. Here, we present R package, 'VarExp', that allows for the estimation of the percentage of phenotypic variance explained using summary statistics only. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly. Its implementation integrates all recent methodological developments and does not need external data to be uploaded by users.

Dates and versions

hal-02272064 , version 1 (27-08-2019)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Vincent Laville, A.R. Bentley, Florian Privé, Xiaofeng Zhu, Jim Gauderman, et al.. VarExp: estimating variance explained by genome-wide GxE summary statistics. Bioinformatics, 2018, 34 (19), pp.3412-3414. ⟨10.1093/bioinformatics/bty379⟩. ⟨hal-02272064⟩
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