event
Predicting evolutionary change at the DNA level in a natural populations
Add to Calendar 2020-09-01T16:00:00 2020-09-01T18:00:00 UTC Predicting evolutionary change at the DNA level in a natural populations

Seminar - 12:00pm - 1:00pm

All Q & A - 1:00pm - 1:30pm

Graduate Student Q & A - 1:30pm - 2:00pm

Start DateTue, Sep 01, 2020
12:00 PM
to
End DateTue, Sep 01, 2020
2:00 PM
Presented By
Dr. John Kelly, University of Kansas

John Kelly, Professor of Ecology and Evolutionary Biology, University of Kansas

Event Series:

Abstract: Evolution by natural selection occurs when the frequencies of genetic variants change because individuals differ in Darwinian fitness components such as survival or reproductive success. Differential fitness has been demonstrated in field studies of many organisms, but our ability to quantitatively predict allele frequency changes from fitness measurements remains unclear. Here, we characterize natural selection on millions of Single Nucleotide Polymorphisms (SNPs) across the genome of the annual plant Mimulus guttatus. We use fitness estimates to calibrate population genetic models that effectively predict observed allele frequency changes into the next generation. Hundreds of SNPs experienced “male selection” in 2013 with one allele at each SNP elevated in frequency among successful male gametes relative to the entire population of adults. In the following generation, allele frequencies at these SNPs consistently shifted in the predicted direction. A second year of study revealed that SNPs had effects on both viability and reproductive success with pervasive trade-offs between fitness components. SNPs favored by male selection were, on average, detrimental to survival. These trade-offs (antagonistic pleiotropy and temporal fluctuations in fitness) may be essential to the long-term maintenance of alleles undergoing substantial changes from generation to generation. Despite the challenges of measuring selection in the wild, the strong correlation between predicted and observed allele frequency changes suggests that population genetic models have a much greater role to play in forward-time prediction of evolutionary change.