Standing genetic variation drives repeatable experimental evolution in outcrossing populations of Saccharomyces cerevisiae

Abstract

In "evolve-and-resequence" (E&R) experiments, whole-genome sequence data from laboratory-evolved populations can potentially uncover mechanisms of adaptive change. E&R experiments with initially isogenic, asexually reproducing microbes have repeatedly shown that beneficial de novo mutations drive adaptation, and these mutations are not shared among independently evolving replicate populations. Recent E&R experiments with higher eukaryotes that maintain genetic variation via sexual reproduction implicate largely different mechanisms; adaptation may act primarily on pre-existing genetic variation and occur in parallel among independent populations. But this is currently a debated topic, and generalizing these conclusions is problematic because E&R experiments with sexual species are difficult to implement and important elements of experimental design suffer for practical reasons. We circumvent potentially confounding limitations with a yeast model capable of shuffling genotypes via sexual recombination. Our starting population consisted of a highly intercrossed diploid Saccharomyces cerevisiae initiated from four wild haplotypes. We imposed a laboratory domestication treatment on 12 independent replicate populations for 18 weeks, where each week included 2 days as diploids in liquid culture and a forced recombination/mating event. We then sequenced pooled population samples at weeks 0, 6, 12, and 18. We show that adaptation is highly parallel among replicate populations, and can be localized to a modest number of genomic regions. We also demonstrate that despite hundreds of generations of evolution and large effective population sizes, de novo beneficial mutations do not play a large role in this adaptation. Further, we have high power to detect the signal of change in these populations but show how this power is dramatically reduced when fewer timepoints are sampled, or fewer replicate populations are analyzed. As ours is the most highly replicated and sampled E&R study in a sexual species to date, this evokes important considerations for past and future experiments.

Keywords: experimental evolution; population genomics; quantitative trait loci.

Fig. 1.

Schematic illustrating the experimental strategy. A four-way cross of diploid strains from different geographic origins (DBVPG6765, wine/European or WE in green; Y12, sake/Asian or SA in blue; DBVPG6044, W. African or WA in red; YPS128, N. American or NA in gold) generated a “synthetic population” that we used as the ancestral source for experimental populations (the SGRP-4X; Cubillos et al. 2013). Twelve replicate populations evolved in parallel and were sampled for sequencing at four timepoints: initially, and after 6, 12, and 18 rounds of forced outcrossing. Numbers in parentheses indicate the approximate number of mitotic divisions that have elapsed by each week.

Fig. 2.

Evidence of allele frequency change across the genome. (a) Sites that have changed the most over time in all replicate populations. Results of a genome scan for SNPs with significant variation in allele frequency between sampled timepoints (via an ANOVA on square root arcsin transformed allele frequencies treating “generation” as a continuous variable). y-Axis values indicate transformed p-values from this ANOVA, such that higher values indicate higher levels of significance; the blue and red horizontal lines represent our empirically determined genome-wide alpha of 0.5 and 0.05, respectively. We find five peak regions that exceed our lower threshold, and label these A–E. (b) Haplotype change across the genome, represented as the average difference between founder haplotype frequency at week 18 from the ancestral founder haplotype frequency. We find three additional peaks and label these F–H. Note that absolute frequency differences are plotted for easier visualization; relative frequency differences are plotted in supplementary fig. S6 for comparison.

Fig. 3.

Allele frequency trajectories at the most significant SNPs in the data set. Allele frequencies at each timepoint, averaged over all replicate populations for peaks A-H, The allele being modeled is the most significant SNP in each peak region.

Fig. 4.

Effects of replication and sampling on signal. ANOVA analysis used to generate fig. 2 was carried out on (a) the entire data set consisting of all 12 replicate populations sampled at four timepoints (0, 180, 360, and 540 generations); (b) a data set consisting of only five replicate populations sampled at the same four timepoints; (c) a data set consisting of all twelve replicate populations sampled at two timepoints (zero and 360 generations) and (d) a data setconsisting of five replicate populations sampled at these two timepoints. Increasing both replication and sampling results in stronger, more localized signals of change.