Inference of causal relationships between biomarkers and outcomes in high dimensions

This paper describes a unified computational framework for studying associations between multiple genotypes, biomarkers, and phenotypic traits in the presence of noise and unobserved confounders. The framework builds on sparse Bayesian modeling methods developed for regression and modified here for inferring structures of richer networks with latent variables. The method exploits the use of genotypes as "instrumental variables" to infer causal associations between phenotypic biomarkers and outcomes, without requiring the assumption that genotypic effects are mediated only through the observed biomarkers. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs) detected in genetic linkage studies. To demonstrate our method, we examined effects of gene transcript levels in the liver on plasma HDL cholesterol levels in a sample of 260 mice from a heterogeneous stock.