Meta-regression of genome-wide association studies to estimate age-varying genetic effects

Pagoni, Panagiota ORCID: https://orcid.org/0000-0002-8090-512X, Higgins, Julian P.T., Lawlor, Deborah A., Stergiakouli, Evie, Warrington, Nicole M., Morris, Tim T. and Tilling, Kate 2024. Meta-regression of genome-wide association studies to estimate age-varying genetic effects. European Journal of Epidemiology 39 , pp. 257-270. 10.1007/s10654-023-01086-1

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Abstract

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Item Type:	Article
Date Type:	Publication
Status:	Published
Publisher:	Springer
ISSN:	0393-2990
Date of First Compliant Deposit:	21 August 2024
Date of Acceptance:	15 November 2023
Last Modified:	21 Aug 2024 14:48
URI:	https://orca.cardiff.ac.uk/id/eprint/170924

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