Thursday, November 21, 2024
11/21/2024
PROJECT SUMMARY Understanding the genetic variation that underlies differences in survival and reproduction is essential to the study of biology and human disease. While most studies of adaptation have focused on traits driven by loci of large effect, the majority of variation in survival and reproduction is due to variation at complex traits, which involve many loci throughout the genome and are influenced by the environment. The majority of these human trait associations are found in non-coding regions, suggesting that genetic variants that affect gene regulation play an important role in trait variation and fitness. However, few studies have been able to connect genetic variants affecting gene expression with complex traits in natural populations, limiting our understanding of the genomics of adaptive evolution. The overarching goal of this work is to identify specific loci involved in complex trait variation affecting fitness in natural populations. Previous work has often fallen short of this goal due to (1) a lack of power for detecting variants of small or modest effect involved in trait variation, or (2) the difficulty of linking variants under selection to traits in natural populations. The proposed work will overcome this limitation by integration studies of tissue-specific assays of gene regulation, surveys in natural populations, and the use of new sequencing and computational approaches. First, we will identify gene regulatory variation associated with ecologically and biomedically significant traits (e.g., body mass and composition, blood chemistry measures, renal physiology, and behavior/activity level) by combining tissue-specific measures of allele-specific expression and chromatin accessibility with population genomic scans for selection. Then, we will use ecologically relevant treatments to examine the effect of environmental variation on gene expression levels and identify gene-by-environment interactions, as well as test the importance of these interactions to adaptive evolution. Finally, we will use scRNA-seq data and gene regulatory network reconstruction to examine how gene regulatory networks evolve in the process of adaptation. Altogether, this work will directly address the challenge of connecting genotype to phenotype for complex traits with implications for the genetics of human health and disease.
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