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

Vincent Laville; Amy R. Bentley; Florian Privé; Xiaofeng Zhu; Jim Gauderman; Thomas W. Winkler; Mike Province; D. C. Rao; Hugues Aschard

doi:10.1093/bioinformatics/bty379

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

Vincent Laville, Amy R. Bentley, Florian Privé, Xiaofeng Zhu, Jim Gauderman, Thomas W. Winkler, Mike Province, D. C. Rao, Hugues Aschard

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Summary: 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.

Original language	English
Pages (from-to)	3412-3414
Number of pages	3
Journal	Bioinformatics
Volume	34
Issue number	19
DOIs	https://doi.org/10.1093/bioinformatics/bty379
State	Published - Oct 1 2018

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title = "VarExp: Estimating variance explained by genome-wide GxE summary statistics",

abstract = "Summary: 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.",

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T2 - Estimating variance explained by genome-wide GxE summary statistics

AU - Laville, Vincent

AU - Bentley, Amy R.

AU - Privé, Florian

AU - Zhu, Xiaofeng

AU - Gauderman, Jim

AU - Winkler, Thomas W.

AU - Province, Mike

AU - Rao, D. C.

AU - Aschard, Hugues

PY - 2018/10/1

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N2 - Summary: 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.

AB - Summary: 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.

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

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