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Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease.

Abstract

Scripts to calculate SPRE statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/.

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 SPRE (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.

Supplementary data are available at Bioinformatics Online.

Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect meta-analysis methods. However, the power to detect genetic associations under fixed-effect models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. Han and Eskin developed a modified random-effects meta-analysis approach (RE2) that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, updating the method (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 fixed-effect meta-analysis.

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