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

8 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.",

author = "Vincent Laville and Bentley, {Amy R.} and Florian Priv{\'e} and Xiaofeng Zhu and Jim Gauderman and Winkler, {Thomas W.} and Mike Province and Rao, {D. C.} and Hugues Aschard",

note = "Funding Information: This work was supported by the R01HL118305 grant from the NHLBI. H.A. was also supported by R21HG007687 from NHGRI. A.R.B. was supported by the Intramural Research Program of the National Human Genome Research Institute in the Center for Research in Genomics and Global Health (CRGGH, Z01HG200362). Publisher Copyright: {\textcopyright} The Author(s) 2018.",

year = "2018",

month = oct,

day = "1",

doi = "10.1093/bioinformatics/bty379",

language = "English",

volume = "34",

pages = "3412--3414",

journal = "Bioinformatics",

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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

N1 - Funding Information: This work was supported by the R01HL118305 grant from the NHLBI. H.A. was also supported by R21HG007687 from NHGRI. A.R.B. was supported by the Intramural Research Program of the National Human Genome Research Institute in the Center for Research in Genomics and Global Health (CRGGH, Z01HG200362). Publisher Copyright: © The Author(s) 2018.

PY - 2018/10/1

Y1 - 2018/10/1

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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DO - 10.1093/bioinformatics/bty379

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SN - 1367-4803

VL - 34

SP - 3412

EP - 3414

JO - Bioinformatics

JF - Bioinformatics

IS - 19

ER -

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

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