Design and model choices shape inference of age-varying genetic effects on complex traits

Understanding how genetic influences on complex traits change with age is a fundamental question in genetic epidemiology. Both cross-sectional (between-subject) and longitudinal (within-subject) approaches can contribute to answering this question, but come with distinct strengths and limitations. Using data from 31 health-related phenotypes in the UK Biobank, we applied a two-stage genome-wide approach to identify genetic variants exhibiting age-dependent effects. To assess the robustness of these findings, we tested for variant-specific results across multiple analytical models, and linked these to differences in key methodological assumptions. Within this framework, we systematically compared the results from cross-sectional gene-by-age interaction models (up to 406,226 individuals) with those from genetic association tests on longitudinal change (up to 83,579 individuals with repeat measurements), and investigated potential sources of bias underlying any observed discrepancies. We found high concordance in the direction of age-varying genetic effects across the two designs (85.96% of the 57 identified variants), but only moderate agreement in magnitude of effect sizes (Pearson r = 0.74). Gene-by-birth year effects, which bias cross-sectional estimates, accounted for the largest proportion of variance in effect size differences across SNPs with age-varying effects between designs (53.1%). Participation bias accounted for an additional 13.3%, while unmodeled non-linear age trajectories contributed minimally to these differences (2.1%). Overall, our results demonstrate that both cross-sectional and longitudinal designs can yield different estimates of age-varying genetic effects, principally due to cohort confounding and participation bias. As neither approach is immune to bias, we recommend integrating both designs for robust inference, to help minimize bias and more accurately characterize how genetic effects on complex traits change over the lifespan.