Author:
Huang Wen,Carbone Mary Anna,Lyman Richard F.,Anholt Robert H. H.,Mackay Trudy F. C.
Abstract
AbstractThe genetics of phenotypic responses to changing environments remains elusive. Using whole genome quantitative gene expression as a model, we studied how the genetic architecture of regulatory variation in gene expression changed in a population of fully sequenced inbred Drosophila melanogaster strains when flies developed at different environments (25 °C and 18 °C). We found a substantial fraction of the transcriptome exhibited genotype by environment interaction, implicating environmentally plastic genetic architecture of gene expression. Genetic variance in expression increased at 18 °C relative to 25 °C for most genes that had a change in genetic variance. Although the majority of expression quantitative trait loci (eQTLs) for the gene expression traits in the two environments were shared and had similar effects, analysis of the environment-specific eQTLs revealed enrichment of binding sites for two transcription factors. Finally, although genotype by environment interaction in gene expression could potentially disrupt genetic networks, the co-expression networks were highly conserved across environments. Genes with higher network connectivity were under stronger stabilizing selection, suggesting that stabilizing selection on expression plays an important role in promoting network robustness.
Publisher
Cold Spring Harbor Laboratory
Reference43 articles.
1. Alpert, M.H. , Frank, D.D. , Kaspi, E. , Flourakis, M. , Zaharieva, E.E. , Allada, R. , Para, A. , and Gallio, M. (2020). A Circuit Encoding Absolute Cold Temperature in Drosophila. Curr. Biol.
2. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
3. Genetic redundancy fuels polygenic adaptation in Drosophila
4. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection;Proc. Natl. Acad. Sci,2011
5. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias