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<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>

Original publication

DOI

10.1093/bioinformatics/btz590

Type

Journal article

Journal

Bioinformatics

Publisher

Oxford University Press (OUP)

Publication Date

27/07/2019