Multivariate GWAS framework for family trios with parental phenotypes to control dynastic effects.

backgroundEffect size estimates in genome-wide association studies (GWAS) and Mendelian randomization (MR) based on unrelated individuals are often confounded by dynastic effects (DE). Existing family-trio-based methods can mitigate this bias, but they typically discard parental phenotypes and cannot jointly model multiple correlated traits. We propose FT-SEM, a multivariate GWAS framework that uses structural equation modeling for family trio data, with the aim of integrating genotypes and phenotypes from parents and offspring across multiple traits to estimate direct genetic effects on a shared latent factor.

resultsSimulations demonstrated that FT-SEM yields unbiased effect estimates with well-controlled type I error, while improving statistical power over conventional trio-based models by incorporating parental phenotypic information. In a genome-wide scan of 778 UK Biobank family trios, no locus reached genome-wide significance, but the FT-SEM-derived family-corrected summary statistics for a latent obesity factor provided an unbiased outcome base for downstream MR. Two-sample MR analyses revealed that the apparent association between systolic blood pressure and obesity was substantially attenuated after accounting for familial confounding, suggesting that it may be spurious. In contrast, the effect of high-density lipoprotein cholesterol on obesity remained directionally consistent and robust, supporting a genuine causal relationship.

conclusionsFT-SEM provides a rigorous and efficient multivariate framework that eliminates dynastic bias while maximizing the utility of family-trio data. The resulting summary statistics can be seamlessly integrated into two-sample MR analyses, highlighting the necessity of family-trio-informed multivariate modeling for guarding against spurious causal claims in complex trait genetics.