A repeated measures GWAS in relatives: detecting age-specific genetic effects on maternal depression
Authors: Rayner Christopher , McAdams Tom , Ystrom Eivind , Ayorech Ziada 
Published: May 2024

Background: Genome-wide association studies (GWAS) of depression typically rely on cross-sectional designs, using lifetime history (LTH) of a diagnosis as the primary outcome. While this strategy has been essential towards accumulating the samples required to detect the individually small genetic effects on depression, it involves strong assumptions that the variants important for depression risk are identical across long stretches of life. Furthermore, heterogeneity in the presentation and aetiology of depression is rarely accounted for - as single cohorts are rarely large enough to provide sufficient statistical power. Longitudinal cohorts provide opportunities towards understanding how genetic effects on depression vary across time and contexts. Linear mixed models that account for between person (relatedness) and within person (multiple measures of the same individual) correlations can be applied to repeatedly measured outcomes across time to estimate age-specific genetic effects, known as a repeated measures GWAS (RmGWAS). Methods: We used data from Mothers who participated in the Norwegian Mother Father and Child study (MoBa) - Norwegian women who have all experienced the psycho-social experience of childbirth. We restructured the Hopkins Symptoms Checklist Depression scores for 55,287 mothers by age, producing an accelerated longitudinal design with 323,134 depression observations across the lifecourse (age range: 15.9, 60.12 years old). We implemented RmGWAS to estimate genetic effects on depression symptoms at age 30, and the linear change in age (variant by age interaction). We then used RmGWAS estimates to compute age-specific genetic effects (GWASt , for t={20, 25, 30, 35, 40, 45, 50} ). For comparison, we performed a cross-sectional GWAS using LTH depression assessed in the same sample (GWASLTH). Summary statistics for GWASt={20, 25, 30, 35, 40, 45, 50} and GWASLTH were compared using heritability, genetic correlation, and polygenic index (PGI) analyses.Results: Our RmGWAS detected one genetic variant associated with depression symptoms at age 30, but none associated with linear change over time. The SNP-based heritability (h2SNP) of GWASt ranged from (.01 to .05), whereas for GWASLTH estimates were higher (h2SNP≈.15). The fixed effect PGIt on Yt was β=.03 (95% CI: .03, .04; p=8.5e-45), with a marginal incremental R2=.008, and a conditional incremental R2=.014. For PGILTH the fixed effect on YLTH was OR=0.98 (95% CI: .92-1.04; p=4.5e-01) with an incremental of R2=.001. Discussion: Here we illustrate the potential of longitudinal data on genotyped relatives for revealing the genetic underpinnings of symptom variability across the life course. Using a leave one out approach in the sample of MoBa mothers, we detected a statistically significant effect of PGIt, but no effect of PGI(LTH). The gains observed in these analyses are likely due to the boost in statistical power from the repeated measures approach. More complex models, which incorporate the non-linear effects of age and other genetic effect modifiers, are likely to provide greater age- and context-related specificity for genetic effect estimates. Future work will build on this model to further improve the polygenic prediction and mechanistic understanding of mental health across development. In summary, this novel approach reduces some of the common sources of heterogeneity in depression GWAS by exploiting repeated assessments of pregnant women living in the Norwegian welfare state.