You are here

MIA Talks

Primer: Comparing gene expression data across species using evolutionary methods (note: 10am start)

January 19, 2022
Dunn Lab, Yale University

With gene expression data accessible from more species than ever before, we can test the extent to which our understanding of developmental genetics from model organisms helps predict patterns across the tree of life. Central to this is the question: How much evolutionary change in development do we expect to observe? In our study, we provide an answer by comparing RNAseq data across tissues and species of Hawaiian drosophilidae flies using evolutionary methods. We show that there exists cohorts of tissue-specific genes that are stable across evolutionary time, and that largely correspond to described patterns from laboratory model Drosophila species. However, our results also show that, as the evolutionary distance separating species increases, variation between species overwhelms variation between tissues. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression profiles, and use this to identify gains and losses of ovarian expression across species. We then use this distribution to calculate the correlation in expression evolution between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. The objective of the comparative framework of our study is to refine the questions we ask, from "How does gene expression differ across these species?", to "Does gene expression vary more or less than we would expect?"