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Abstract Genetic associations shared by multiple traits provide evidence about the biological role of disease associated variants. We propose Genetic Factor Analysis (GFA), a multi-phenotype analysis method that identifies common patterns of cross-trait associations, the signatures of shared biological processes. GFA overcomes many limitations of alternative methods by automatically selecting the number of factors, accounting for sample overlap, and allowing factors to be non-orthogonal. We apply GFA to analysis of 22 common risk factors for coronary artery disease (CAD), and type 2 diabetes (T2D), allowing us to partition the heritability of CAD, T2D, and risk factors into 13 pleiotropic components. This analysis reveals, among other findings, that about 8% of the heritability of BMI is mediated by factors that do not contribute do CAD or T2D risk. In a second application, we use GFA to obtain a biologically meaningful decomposition of a large set of blood cell composition phenotypes and show that accounting for overlapping samples is critical to obtaining this result.
Morrison et al. (2024) studied this question.