Causality in Coexpression

Abstract: One of the main goals of genetics has been to understand the link between genotype and phenotype. Using yeast (Saccharomyces cerevisiae) as our model organism, we take a closer look at the connection between genetic variation and gene expression to learn more about the mechanisms of gene regulation. We propose an algorithm based on ANOVA to detect causal relationships between coexpressed genes. We first identify expression quantitative trait loci (eQTLs) with strong effects on gene expression. The algorithm then uses these eQTLs with strong effects and the expression of all genes to identify how genes are affecting each other. This is done by analysing coexpressed gene pairs where both genes have an eQTL and finding if the eQTL of one gene affects the expression of the other. Genes that were found to affect the expression of other genes were named “causal genes”. We evaluate our method by comparing its results with known causal genes and conclude that it is a good predictor of known interactions. Using this algorithm, we found 741 genes having causal effects on gene expression, many of which affected the gene expression of many other genes across the genome (2278 total affected genes). Some of the causal genes clustered at six hotspot regions in the genome. Genes in hotspot regions were found to have lower heritability than genes outside these regions. We hypothesize that hotspot regions may be enriched for essential and/or fitness related genes.