Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results.Author Summary: The first step in transmitting heritable information, expressing RNA molecules, is highly regulated and depends on activations of specific pathways and regulatory factors. The state of the cell is hard to measure, making it difficult to understand what drives the changes in the gene expression. To close this gap, we apply a statistical model to infer the state of the cell, such as activations of transcription factors and molecular pathways, from gene expression data. We demonstrate how the inferred state helps to explain the effects of variation in the DNA and environment on the expression trait via both direct regulatory effects and interactions with the genetic state. Such analysis, exploiting inferred intermediate phenotypes, will aid understanding effects of genetic variability on global traits and will help to interpret the data from existing and forthcoming large scale studies.