Meta-regression of Genome-Wide Association Studies to estimate age-varying genetic effects
Authors: Pagoni Panagiota , Higgins Julian P. T. , Lawlor Deborah A. , Stergiakouli Evie , Warrington Nicole M. , Morris Tim T. , Tilling Kate 
Published: January 2023

ABSTRACTBackgroundFixed-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, therefore meta-analysing GWAS of age-diverse samples could be misleading. Meta-regression allows adjustment for study specific characteristics and models heterogeneity between studies. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes.MethodsWith 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 theFTOSNP rs9939609 on Body Mass Index (BMI).ResultsFixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both the main genetic effects and the 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. This is similar to the association that has been previously reported by a study that used individual participant data.ConclusionsGWAS using summary statistics from age-diverse samples should consider using meta-regression to explore age-varying genetic effects.KEY MESSAGESMeta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect for all studies. However, genetic effects may vary with age, therefore meta-analysing GWAS of age-diverse samples could produce misleading results.Meta-regression could be used to relate observed between-study heterogeneity to study characteristics such as age. Therefore, meta-regression could be used to combine summary level GWAS data to provide evidence for any age-varying genetic effects.This simulation study shows that when genetic effects vary with age, meta-regression provides unbiased estimates of main and age-varying genetic effects. The precision of the estimates depends on the number of studies included, and the diversity in age between them.The applied example using publicly available summary data, supported the simulation study.By applying meta-regression, we observed a previously reported age-varying association between each additional minor allele (A) of rs9939609 and BMI; an inverse at ages 0 to 3 and a positive association at ages 5.5 to 13.Similar association has been previously reported by a study that used individual participant data.