Joint genetic analysis of gene expression data with inferred cellular phenotypes

Abstract

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.

Figure 1. Analysing genetic effects in the context of intermediate phenotypes using PHO4 as an example.

(a) Intermediate phenotypes are learnt from expression levels using prior information from Yeastract database on the targets of the factor. The highlighted genes are known targets of PHO4. These activations are learned jointly for all factors. (b) The variation in intermediate phenotypes can be explained by locus genotypes or the growth condition of the segregants. For most loci (greyed out), the genotype is uncorrelated with the factor activation level. For the PHO84 locus at chrIII-46084, not greyed out and indicated by arrow, it is correlated. The plot at right shows the distribution of factor activations stratified by genotype at this locus. (c) Some genotypes show a statistical interaction with the inferred intermediate phenotype affecting gene expression levels, in this case YJL213W. See also Figure 2.

Figure 2. Three broad classes of interaction effects between locus genotype and transcription factor activation affecting gene expression (for details see text).

Each marker shows the gene expression and factor activation for one individual segregant of either BY (blue) and RM (red) background at the locus, and grown in ethanol (triangles) or glucose (circles) as a carbon source. Maximum likelihood fits for expression data for the BY and RM segregants are plotted as solid lines; an interaction effect corresponds to a difference in slope in the two genetic backgrounds. (a) Genotype-environment interaction mediated by the inferred YAP1 transcription factor activation. (b) Interaction between the PHO84 locus and PHO4 transcription factor activation, which is associated both with the PHO84 locus genotype and the PHO4 probe expression level. (c) Epistatic interaction between HAP1 and its target, SCM4, mediated by the HAP1 activation.

Figure 3. Number of genes affected by a genotype-factor interaction for each locus for Yeastract factors (blue), KEGG factors (red), freeform factors (green), and environment (gray).