Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

MOTIVATION: 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. RESULTS: 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. AVAILABILITY: Scripts to calculate SPRE statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Original publication

DOI

10.1093/bioinformatics/btz590

Type

Journal article

Journal

Bioinformatics

Publication Date

27/07/2019