Predicting evolutionary change at the DNA level in a natural Mimulus population

Abstract

AbstractEvolution 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 plantMimulus 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.Author summaryFor the last 100 years, population geneticists have been deriving equations for Δp, the change in allele frequency owing to mutation, selection, migration, and genetic drift. Seldom are these equations used directly, to match a prediction for Δp to an observation of Δp. Here, we apply genomic sequencing technologies to samples from natural populations, obtaining millions of observations of Δp. We estimate natural selection on SNPs in a natural population of yellow monkeyflowers and find extensive evidence for selection through differential male success. We use the SNP-specific fitness estimates to calibrate a population genetic model that predicts observed Δp into the next generation. We find that when male selection favored one nucleotide at a SNP, that nucleotide increased in frequency in the next generation. Since neither observed nor predicted Δp are generally large in magnitude, we developed a novel method called “haplotype matching” to improve prediction accuracy. The method leverages intensive whole genome sequencing of a reference panel (187 individuals) to infer sequence-specific selection in thousands of field individuals sequenced at much lower coverage. This method proved essential to accurately predicting Δp in this experiment and further development may facilitate population genetic prediction more generally.