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| Funder | NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES |
|---|---|
| Recipient Organization | Michigan State University |
| Country | United States |
| Start Date | Aug 03, 2021 |
| End Date | May 31, 2026 |
| Duration | 1,762 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10461190 |
PROJECT SUMMARY Understanding how evolutionary forces shape genetic variation for quantitative traits is crucial for determining the prevalence of genetic disease in human populations. Despite numerous examples that genotype-by-environment interactions (GxE) are important for the maintenance of variation for biologically and medically important traits, we lack a clear understanding of the role
that GxE plays in shaping genetic variation and of the evolutionary processes that shape GxE. In particular two broad processes could contribute to GxE: local adaptation for plastic responses and variation in selective constraint across environments. The proposed work evaluates the importance of these two evolutionary processes in shaping
GxE in natural populations. We will leverage large genomic, transcriptomic, and phenotypic datasets in systems where identical genotypes can be phenotyped across multiple biologically-realistic environments. First, we will use an extension of a test previously developed by the PI to look for evidence of local adaptation shaping genetic variation for plastic responses,
contributing to GxE. Next, we will develop a new analysis looking for a covariance between allele frequency and environment-specific effect sizes, consistent with the hypothesis that variation in selective constraint shapes GxE. Finally, we will test the hypothesis that relaxed selective constraint on rarely-expressed plastic responses limits the evolution of adaptive
plasticity by testing for evidence of reduced negative selection on genes that are only expressed in specific environments. Overall, this work will determine the evolutionary processes that shape GxE, filling a crucial gap in our understanding of the maintenance of variation for quantitative traits and providing
methods that will be useful to researchers in a variety of systems.
Michigan State University
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