Adaptation in Outbred Sexual Yeast is Repeatable, Polygenic and Favors Rare Haplotypes

Abstract

We carried out a 200 generation Evolve and Resequence (E&R) experiment initiated from an outbred diploid recombined 18-way synthetic base population. Replicate populations were evolved at large effective population sizes (>105 individuals), exposed to several different chemical challenges over 12 weeks of evolution, and whole-genome resequenced. Weekly forced outcrossing resulted in an average between adjacent-gene per cell division recombination rate of ∼0.0008. Despite attempts to force weekly sex, roughly half of our populations evolved cheaters and appear to be evolving asexually. Focusing on seven chemical stressors and 55 total evolved populations that remained sexual we observed large fitness gains and highly repeatable patterns of genome-wide haplotype change within chemical challenges, with limited levels of repeatability across chemical treatments. Adaptation appears highly polygenic with almost the entire genome showing significant and consistent patterns of haplotype change with little evidence for long-range linkage disequilibrium in a subset of populations for which we sequenced haploid clones. That is, almost the entire genome is under selection or drafting with selected sites. At any given locus adaptation was almost always dominated by one of the 18 founder's alleles, with that allele varying spatially and between treatments, suggesting that selection acts primarily on rare variants private to a founder or haplotype blocks harboring multiple mutations.

Keywords: complex traits; evolve and resequence; experimental evolution; yeast.

Fig. 1.

Schematic of the long-term evolution experiment. Panel (A) depicts the creation of the base population used in this study. In total, 11 Mat a and 11 Mat @ strains were crossed in a full diallel, followed by 12 rounds of forced outcrossing to create a highly diverse and mosaic population of diploids. Panel (B) depicts the long-term evolution experiment. Details of the regimen are in the methods. The first day of the experiment, a different, normally lower dose of the chemical stressor was used to acclimate cells to the chemical.

Fig. 2.

Different classes of evolved populations. In (A), raw haplotype frequency plots are shown of (from top to bottom) a caffeine replicate, a glacial acetic acid replicate, and a cadmium chloride replicate classified as aneuploid haploid, heterozygous clonal, and outbred sexual, respectively. In (B) are shown histograms of the per-site heterozygosity of the corresponding replicates. The average per-site heterozygosity is shown at the top of each panel.

Fig. 3.

Haploid clones were isolated from the base population (A), a week 11 cadmium chloride population (B), a week 11 diamide population (C) as well as a week 11 sodium chloride population (D).

Fig. 4.

Genome-wide Manhattan plots of the LOD score for all chemicals. Red dots represent putative local peaks. The dashed blue horizontal line marks an LOD score of 5.

Fig. 5.

The results of a genome-wide scan for potentially causal genomic regions for cadmium chloride treatment is shown (panel A). Panel (B) depicts a close-up of chromosome XVI, where one of the highest peaks was detected and which emphasizes the polygenic nature of the adaptive response. Panel (C) zooms in under the peak on Chromosome XVI, where there is a candidate causative SNP (red box) just upstream of the candidate gene AFT2 in the most changed haplotype, AB4, that creates a Yap1p binding site (see main text).

Fig. 6.

Average change of the most increased haplotype vs. that of the next most increased haplotype at each of the 21 major peaks detected.

Fig. 7.

Repeatability of evolution amongst replicate populations. Panel (A) depicts the Spearman correlation between the LOD scores of randomly grouped replicates from each chemical treatment. Panel (B) shows the genome-wide z-score of each cadmium chloride group superimposed on one another.

Fig. 8.

The absolute deviation genome-wide of the MIH frequency of a single replicate from the average of the remaining replicates for each chemical. The red line represents a kernel regression run using the ksmooth() function in R with kernel set at “normal” and bandwidth set at 100,000.

Fig. 9.

Detecting pleiotropy. Shown are the Spearman correlations of the genome-wide LOD scores for each pair of chemical treatments.

Fig. 10.

The most striking example of pleiotropy was detected on chromosome X. Panel (A) depicts the superimposed genome-wide z-scores of chlorpromazine, sodium chloride, and YPD, with the shared peak on Chromosome X highlighted in teal. Panel (B) zooms in on z-scores at the shared peak, with panel (C) showing the genes present in this region and panel (D) depicting the mean haplotype change amongst the three different chemicals.