Bayesian Analysis of Multi-Target Genetic Markers Using Hierarchical Phenotypic Data

Abstract

Hierarchical data is ubiquitous in healthcare, but taking advantage of hierarchic information is still an open problem. We demonstrate the strengths and weaknesses of Bayesian multilevel analysis (BMLA) in this scenario by characterizing the multi-target relationships between genetic and phenotypic variables. The BMLA method does not scale well with the number of variables, thus we performed filtering using standard pairwise genome-wide association analysis. In this first GWAS phase, we selected only the most significant genotypic variables for further screening. We worked with various thresholds, resulting in 274, 12, and 2 genetic variables, these were treated as interventional variables in the second phase of data analysis using BMLA. The hierarchy of the phenotypic variables was given a priori, thus, we could estimate the posteriors of the relevance, i.e., for direct, non-mediated interaction between these broader categories and genetic markers. Additionally, we could approximate the joint relevance of genetic markers for multiple phenotypic groups, such as for mental health, metabolic and cardiovascular descriptors. The existence of such multi-target (pleiotropic) genetic factors is already indicated by significant genetic correlation between disease groups, i.e., by the overlap of their genetic background. Using this two-stage Bayesian systems-based method, we could robustly induce posteriors for these multivariate and multi-target relevance relations. The systematic investigation of a varying number of genetic and phenotypic factors confirmed that the method is sensitive enough to highlight the weak effects of genetic variables that can be easily overshadowed by the strong phenotypic correlations.