Genome-wide mediation analysis: an empirical study to connect phenotype with genotype via intermediate transcriptomic data in maize

Abstract

Mapping genotype to phenotype is an essential topic in genetics and genomics research. As the Omics data become increasingly available, 2-variable methods have been widely applied to associate genotype with the phenotype (genome-wide association study), gene expression with the phenotype (transcriptome-wide association study), and genotype with gene expression. However, signals detected by these 2-variable association methods suffer from low mapping resolution or inexplicit causality between genotype and phenotype, making it challenging to interpret and validate the molecular mechanisms of the underlying genomic variations and the candidate genes. Under the context of genetics research, we hypothesized a causal chain from genotype to phenotype partially mediated by intermediate molecular processes, i.e. gene expression. To test this hypothesis, we applied the high-dimensional mediation analysis, a class of causal inference method with an assumed causal chain from the exposure to the mediator to the outcome, and implemented it with a maize association panel (N = 280 lines). Using 40 publicly available agronomy traits, 66 newly generated metabolite traits, and published RNA-seq data from 7 different tissues, our empirical study detected 736 unique mediating genes. Noticeably, 83/736 (11%) genes were identified in mediating more than 1 trait, suggesting the prevalence of pleiotropic mediating effects. We demonstrated that several identified mediating genes are consistent with their known functions. In addition, our results provided explicit hypotheses for functional validation and suggested that the mediation analysis is a powerful tool to integrate Omics data to connect genotype to phenotype.

Keywords: GWAS; RNA-seq; TWAS; eQTL; maize; mediation analysis; metabolite.