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            <title xml:lang="en">VarExp: estimating variance explained by genome-wide GxE summary statistics</title>
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              <persName>
                <forename type="first">Vincent</forename>
                <surname>Laville</surname>
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                <forename type="first">A.R.</forename>
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                <forename type="first">Xiaofeng</forename>
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                <forename>Catherine</forename>
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            <funder>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).</funder>
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                <title level="j">Bioinformatics</title>
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                  <publisher>Oxford University Press (OUP)</publisher>
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            <abstract xml:lang="en">
              <p>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.</p>
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              <org type="consortium">We gratefully acknowledge all contributors to the CHARGE Gene-Lifestyle Interactions Working Group</org>
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