Incipient Balancing Selection through Adaptive Loss of Aquaporins in Natural Populations

A major goal in evolutionary biology is to understand how adaptive evolution has influenced natural variation, but identifying loci subject to positive selection has been a challenge. Here we present the adaptive loss of a pair of paralogous genes in specific Saccharomyces cerevisiae subpopulations. We mapped natural variation in freeze-thaw tolerance to two water transporters, AQY1 and AQY2, previously implicated in freeze-thaw survival. However, whereas freeze-thaw–tolerant strains harbor functional aquaporin genes, the set of sensitive strains lost aquaporin function at least 6 independent times. Several genomic signatures at AQY1 and/or AQY2 reveal low variation surrounding these loci within strains of the same haplotype, but high variation between strain groups. This is consistent with recent adaptive loss of aquaporins in subgroups of strains, leading to incipient balancing selection. We show that, although aquaporins are critical for surviving freeze-thaw stress, loss of both genes provides a major fitness advantage on high-sugar substrates common to many strains' natural niche. Strikingly, strains with non-functional alleles have also lost the ancestral requirement for aquaporins during spore formation. Thus, the antagonistic effect of aquaporin function—providing an advantage in freeze-thaw tolerance but a fitness defect for growth in high-sugar environments—contributes to the maintenance of both functional and nonfunctional alleles in S. cerevisiae. This work also shows that gene loss through multiple missense and nonsense mutations, hallmarks of pseudogenization presumed to emerge after loss of constraint, can arise through positive selection.

Published in the journal: . PLoS Genet 6(4): e32767. doi:10.1371/journal.pgen.1000893
Category: Research Article
doi: 10.1371/journal.pgen.1000893


A major goal in evolutionary biology is to understand how adaptive evolution has influenced natural variation, but identifying loci subject to positive selection has been a challenge. Here we present the adaptive loss of a pair of paralogous genes in specific Saccharomyces cerevisiae subpopulations. We mapped natural variation in freeze-thaw tolerance to two water transporters, AQY1 and AQY2, previously implicated in freeze-thaw survival. However, whereas freeze-thaw–tolerant strains harbor functional aquaporin genes, the set of sensitive strains lost aquaporin function at least 6 independent times. Several genomic signatures at AQY1 and/or AQY2 reveal low variation surrounding these loci within strains of the same haplotype, but high variation between strain groups. This is consistent with recent adaptive loss of aquaporins in subgroups of strains, leading to incipient balancing selection. We show that, although aquaporins are critical for surviving freeze-thaw stress, loss of both genes provides a major fitness advantage on high-sugar substrates common to many strains' natural niche. Strikingly, strains with non-functional alleles have also lost the ancestral requirement for aquaporins during spore formation. Thus, the antagonistic effect of aquaporin function—providing an advantage in freeze-thaw tolerance but a fitness defect for growth in high-sugar environments—contributes to the maintenance of both functional and nonfunctional alleles in S. cerevisiae. This work also shows that gene loss through multiple missense and nonsense mutations, hallmarks of pseudogenization presumed to emerge after loss of constraint, can arise through positive selection.


Biologists have long sought to understand the process of natural selection and the signatures left behind in extant species. Finding evidence of adaptive evolution has been a holy grail for evolutionary biologists, because it can provide insights into how and why organisms evolve. However, examples of adaptive selection from which to glean insights remain relatively scarce [1]. The recent explosion in the number of genomes available for different organisms provides an exciting opportunity to identify loci with unusual patterns of variation indicative of selection (for example [2][7]). However, even for loci with strong signatures of selection, the affected phenotypes are often a complete mystery. In contrast, mapping studies link quantitative trait variation to genomic loci that can then be interrogated for evidence of selection. The challenge in most organisms is identifying responsible SNPs within candidate regions, which are often megabases long and contain hundreds of functional elements, hindering further study [8].

Here, we used the power of yeast genetics and genomics to uncover a unique example of adaptive gene loss, involving multiple paralogous genes and several sequential evolutionary events. We previously surveyed phenotypic variation in Saccharomycetes collected from diverse environments and found that relatively few of those strains (12%) could survive freeze-thaw (FT) stress [9]. Many tolerant strains were isolated from oak soil in the Northeastern United States, whereas sensitive strains were typically isolated from warm environments, often from fruit or fermentations. This suggested that FT tolerance has been selected for in strains from cold climates but lost in other isolates. Several genes have been linked to freeze-thaw tolerance in yeast and other organisms, including water transporters. The paralogous yeast aquaporins (AQYs) AQY1 and AQY2 were implicated in FT stress by the baking industry, which found that AQY over-expression increases yeast viability in frozen bread dough [10]. Rapid export of water through AQYs is thought to increase FT survival by preventing intracellular shearing due to water crystallization [10],[11]. The paralogs may have arisen in the whole-genome duplication (WGD) event in the Saccharomyces lineage [12], since all post-WGD species all have two aquaporins whereas most pre-WGD species have a single ortholog (Dana Wohlbach and A.P.G., unpublished). It has been observed that laboratory and industrial strains as well as several vineyard isolates harbor non-functional alleles of AQY2, while several strains harbor a non-functional version of AQY1 [13][16]. However, without population-level analysis or knowledge of potential ecological driving forces, it is difficult to distinguish selection at these loci from neutral gene loss in the progenitor of the related strains. Here, we provide the first evidence of adaptive loss of AQY paralogs in natural populations of S. cerevisiae, leading to incipient balancing selection via spatial variation in selective pressures.


We mapped FT tolerance using a cross between naturally FT-resistant strain YPS163, collected from Pennsylvania oak trees [17], mated to a FT-sensitive lab strain derived from S288c, by phenotyping 44 recombinant strains together genotyped at 198 markers spaced roughly every 60 kb (∼30 cM) [18]. Two loci were identified: one on the left arm of chromosome 12 and one on the right arm of chromosome 16 (Figure 1, see Methods). Each contained one of two paralogous aquaporin transporters, AQY2 and AQY1, respectively, which were previously linked to freeze-thaw tolerance [10],[11],[19]. Together, these genes explained >90% of the phenotypic variation, with AQY2 alone explaining two-thirds of the effect (Figure 1C). This was confirmed by reciprocal translocation experiments (Figure 1D): deletion of either gene from YPS163 diminished FT tolerance according to the QTL effect plots, while deletion of both genes ablated FT survival. Introducing either gene into the S288c-derived lab strain (which harbors non-functional alleles of both genes [13][16]) donated partial FT tolerance to the otherwise sensitive strain. Thus, AQY2 and, to a lesser extent, AQY1 are major effectors of natural variation in yeast FT tolerance.

Yeast aquaporins underlie freeze-thaw tolerance in yeast.
Fig. 1. Yeast aquaporins underlie freeze-thaw tolerance in yeast.
(A) Genome scan of YPS163 x S288c identified a major QTL on the left arm of Chromosome 12. (B) A second scan in which the first QTL was treated as a fixed term identified a second QTL on the right arm of Chromosome 16. LOD scores at which FDR = 0.01 are indicated by horizontal red lines. Additional peaks in Figure 1B were not significant when both QTL on Chromosomes 12 and 16 were held fixed, suggesting they may be false positives. (C) Effect plot from QTL mapping. (D) Freeze-thaw tolerance was measured in parental strains, YPS163 (‘YPS’) mutants, and S288c-derivative BY4741 (‘S’) harboring empty vector or a plasmid-borne copy of AQY1 or AQY2 from YPS163, as described in Methods. The average and standard deviation of biological quadruplicates is shown.

Sequencing AQY2 and AQY1 from the population revealed a near-perfect correlation between FT tolerance and the presence of functional AQY genes. Tolerant strains contained nearly identical and known functional alleles of both genes, while several strains with an intermediate phenotype contained only one functional gene. However, FT-sensitive strains displayed several different non-functional AQY alleles (Figure 2 and Figure S1). There were three distinct frame-shifting deletions in AQY2, including a known 11-bp deletion in laboratory and vineyard strains [14],[16], deletion of G at position 25 (G25) in Asian isolates and several other strains [14],[16], and a G528 deletion in the Malaysian AQY2 that is unable to contribute FT tolerance in our assay (Table S2). Several coding polymorphisms were shared in the recapitulated proteins encoded by the 11 bp-deletion allele or by the G25 allele (Figure 2). There were also three different non-functional AQY1 alleles in the population, including the A881 deletion that renders AQY1 inactive in our context (see Table S2 and Figure S5C), the V121M polymorphism known to inactivate water transport [13], and a 955-bp deletion that removes the first 106 bp of AQY1 and its upstream region in Malaysian strains. The trees for Aqy2 and Aqy1 are distinct from one another, and significantly different from trees based on neutral or genomic sequence that show clear distinction between Asian strains and vineyard isolates [9],[20],[21]. Such discordance between gene and species trees can be a sign of non-neutral evolution. Furthermore, there were five different combinations of non-functional AQY1 and AQY2 alleles, and a higher-than-expected frequency of strains harboring both functional or both non-functional genes (p = 3.5×10−4, Chi-square test). This cannot be simply explained by shared ancestry, which would have produced similar protein trees and a limited combination of alleles, and instead supports the non-random retention or loss of both AQY genes.

Multiple independent losses of <i>AQY2</i> and <i>AQY1</i> in diverse <i>S. cerevisiae</i> isolates.
Fig. 2. Multiple independent losses of AQY2 and AQY1 in diverse S. cerevisiae isolates.
Bayesian trees of the recapitulated AQY proteins (where gaps were treated as missing data before translation) for Aqy2 (left) and Aqy1 (right). Trees were generated using Mr. Bayes 3.1 and a mixed amino acid replacement model with invgamma rates. All nodes displayed posterior probabilities >0.92. Strains with full-length proteins are nearly identical and do not resolve in the tree. Each star represents the appearance of a different deletion, including the Malaysian G528 (grey), 11-bp (yellow), and Asian G25 (blue) deletions in AQY2, and the amino-terminal Malaysian (green) and A881 (orange) deletions in AQY1. Appearance of the aqy1-V121M polymorphism is highlighted with a pink circle. Strains are color coded according to their niche as shown in the key. Freeze-thaw tolerance scores are listed to the right of strains shown in the Aqy1 tree (with +++ for tolerance and–for sensitivity, see Methods for details).

Signatures of selection at AQY2 and AQY1

We applied several tests to assess if loss of AQY function may have been selected for in some strains. Under the neutral model, the rate of polymorphism within strains should be similar to the rate of divergence across species. Instead, both AQY1 and AQY2 show an excess of replacement polymorphism, assessed by the McDonald-Kreitman test [22] that compares non-synonymous (A) to synonymous (S) codon changes (Table S3). AQY1 showed an A/S ratio of polymorphism (5/4 = 1.25) that was significantly higher than that of divergence (11/49 = 0.22, p = 0.026, Fisher's exact test). AQY2 also showed an excess of polymorphic sites (A/S of 8/20 = 0.4) compared to divergent sites (3/40 = 0.075, p = 0.019), as well as an excess of deletions (3/20 versus 0/40, p = 0.045). AQY2 (but not AQY1) also deviated from neutral evolution at synonymous sites, showing an excess of SNPs compared to 8 intergenic sequences (p = 0.028, multi-locus HKA test [23], Table S4 and Table S5). For the most part, the tests were not significant if subgroups of strains, defined by AQY haplotypes in Figure 2 (see Table S1 for details), were considered separately (Table S3 and Table S5). This result indicates that much of the variation is between strain groups.

Excess polymorphism can result from relaxed constraint in the species, or if local adaptation is driving divergence between populations [24],[25]. To distinguish between these models, we used non-imputed genome sequence data of Liti et al. [21] to characterize sequence variation flanking the AQY genes. We applied several empirical tests, which can handle the missing data in the low-coverage genomic sequences and are less subject to the unusual features of S. cerevisiae populations (including extensive population structure, unknown population dynamics, ambiguous balance between clonal vs. sexual reproduction, and human-associated migration [21], [26][29]) that can confound standard tests [2],[30]. To monitor the variation surrounding AQY2, we subdivided 21 strains with data at AQY2 into strains harboring the Asian G25 deletion, the 11 bp deletion, or the full-length AQY2 (clonal Malaysian strains were not considered, see Methods). We calculated the average pairwise nucleotide differences surrounding AQY loci within and between groups, and then compared this variation to other regions across the genome. We tested for several signatures: a recent selective sweep is predicted to reduce variation flanking the selected allele in the affected population, while balancing selection can increase variation between strain groups [25]. Since much of the genome may be evolving neutrally, loci with extreme values display the strongest evidence for non-neutral evolution.

For strains harboring the Asian G25 allele of AQY2, we saw a high correlation in between-group variation and within-group variation across much of the genome, including the right arm of chromosome 12 (Figure 3A, right side). However, a 50 kb stretch on the left arm of chromosome 12 showed below-average variation within the strains (0.76th percentile compared to other similarly sized regions genome-wide, see Methods) but high variation between groups. There was a sharp break in this pattern at ∼72 kb, which may represent the breakpoint of a selective sweep. To further explore this, we calculated the difference in between-group variation minus within-group variation, then calculated the area under contiguous peaks in the difference curve for comparison (see Figure S2 and Methods). This procedure identified a 5.6 kb region spanning the 870 bp AQY2 ORF that ranked in the top 1.2 percentile of 4,600 regions genome-wide with skewed between-group versus within-group variation (Figure 3A). Strains harboring the 11-bp deletion displayed a 4,300 bp region encompassing AQY2 with a significant skew in the between- versus within-group variation (1.8th percentile of 3,238 regions genome-wide, Figure 3B) and below-average within-group variation (6th percentile genome-wide). Strains harboring the full-length AQY2 showed a smaller peak of 1,800 bp with high between-group variation (6.3rd percentile, Figure 3C), but average within-group variation (>50th percentile). These results show that strains harboring either deletion have low variation within those strain groups, and that the high variation at AQY2 distinguishes the three groups from one another. Indeed, a genome-wide plot of FST [2], which measures the population differentiation based on these three groupings, identified a clear peak of 6.4 kb over AQY2 with above-average FST, ranking among the top 1.5th percentile genome-wide (Figure 3D).

Skewed patterns of variation surrounding the <i>AQY2</i> locus.
Fig. 3. Skewed patterns of variation surrounding the AQY2 locus.
The average number of pairwise nucleotide differences per 1,000 bp sliding window of step size of 100 bp was plotted on Chromosome 12. Variation within (blue curve) and between (red curve) groups of (A) 4 strains harboring the Asian G25 AQY2 deletion, (B) 16 strains with the 11-bp deletion allele, and (C) 6 strains containing the full-length AQY2. Horizontal blue lines represent the genome-wide average of pair-wise variation within each group, and vertical lines represent manually defined breakpoints in trends. Regions identified with skewed between-group minus within-group variation (see Methods) are highlighted in blue. (D) A plot of FST based on the three groupings in (a–c); horizontal grey line represents the genome-wide average. Gene positions are shown above plots as black boxes, with AQY2 highlighted in yellow.

A confounding feature is the extensive population structure within S. cerevisiae [21],[29], which can mimic some signatures of selection. Several controls indicate that the observed patterns are unlikely due to demographics. First, these regions were among the most extreme across the genome, which is not expected if population structure is the underlying cause. However, many S. cerevisiae strains have mosaic genomes, for which large regions have distinct lineages [21],[29]. To control for this, we performed a partitioning sampling: strains were partitioned at each of 1,370 randomly chosen SNPs across the genome. The difference in between-group minus within-group variation was scored surrounding the partitioning SNP and compared to the difference profile when strains were partitioned based on AQY2 allele (see Methods). The regions observed for the Asian G25 or 11-bp deletion classes remained among the most extreme (4.7th and 5.4th percentile, respectively). Thus, the profiles we observe in Figure 3A and 3B are uncommonly found at random SNPs, most of which likely reflect neutral variation. In contrast, the skew in variation found in strains with full-length AQY2 was not significant by this assessment (26th percentile).

We conclude that the observed skew in polymorphism observed in strains with the Asian G25 deletion and the 11-bp deletion in AQY2 resulted from two separate partial selective sweeps that reduced variation within each group. The high variation distinguishing strain groups is a signature of balancing selection, which may be maintaining both functional and non-functional AQY2 alleles in the population. Indeed, we observed a positive Tajima's D at AQY2, assessed on a smaller set of high-quality sequences (D = 0.851, p<0.05, Figure S3), indicating an excess of intermediate-frequency polymorphism that is consistent with balancing selection [24].

The patterns at AQY1 were less clear. Strains harboring the aqy1 V121M allele or the A881 deletion showed reduced variation within each group and high variation between groups at the AQY1 region (Figure S4). Although these were highly significant compared to other loci across the genome (0.77th and 1.23rd percentile, respectively), they were not significant compared to random-SNP partitioning described above (16th and 48th percentile, respectively). Thus, the slight skew in between-group versus within-group variation at AQY1 could be due to demographic factors, incorrect strain groupings, or older or weaker selective sweep(s) that have since recovered variation through recombination or mutation.

Loss of AQY function provides an advantage in high osmolarity

The above results strongly suggest selective pressure to lose AQY function in some strains, perhaps driven by environmental factors. We previously reported an anti-correlation between FT survival and osmo tolerance across a wide range of S. cerevisiae strains (R = −0.35, p = 0.006) [9]. Furthermore, a lab strain with functional AQYs was shown to be sensitive to hypo- and hyper-osmotic cycling, but not to consistently high osmolarity [13],[14]. Instead, we found that loss of both AQY genes provides a major growth advantage in high osmolar conditions found in nature (Figure 4). A YPS163 mutant lacking both AQYs displayed ∼1.7X greater survival in 1.5 M sorbitol, whereas introducing a functional AQY into the S288c-derived lab strain decreased survival 2–3X. Furthermore, sorbitol tolerance was anti-correlated to both freeze-thaw tolerance (R = −0.38) and the number of functional aquaporins (R = −0.31) in these strains (Table S1). The sugar concentration used here is comparable to that found in the fruit substrates of many wild strains [31]. Thus, AQY function presents a substantial fitness defect in conditions relevant in nature, likely due to passive water loss triggered by the high osmolarity of sugary substrates.

Strains lacking AQY genes show a fitness advantage under high osmolarity.
Fig. 4. Strains lacking AQY genes show a fitness advantage under high osmolarity.
Cells were grown on solid agar plates containing 1.5 M sorbitol for 2 days and the number of colony-forming units (CFU) was compared to a no-stress control plate. (A) CFU for YPS163 and YPS163 aqy1Δ aqy2Δ grown on rich medium plus 1.5 M sorbitol; (B) S288c-derivative BY4741 harboring the empty vector or a plasmid expressing the YPS163 allele of AQY1 or AQY2, grown on selective medium with 1.5 M sorbitol. The average and standard deviation of biological triplicates is shown.

In the course of these experiments, we also discovered that YPS163 lacking either aquaporin had a major defect in spore formation during meiosis (Figure 5). Although AQY1 had been previously implicated in a late step in spore maturation [32], our phenotype is distinct in that it affects spore production. Whereas >70% of the parental YPS163 formed full tetrads within 2 days, only 18–24% of the double or single mutants produced full tetrads. After 9 days, the mutant produced more spores but was still defective compared to the parental strain (<60% full tetrads compared to ∼85%, Figure S5). The AQY requirement is ancestral, since an S. paradoxus aqy1Δ mutant displayed an identical defect (Figure 5A). In contrast, strains without functional AQY genes produce full tetrads (albeit with lower efficiency than YPS163 [33]), consistent with a previous report showing AQY1 is not required for sporulation in vineyard strains [34]. More importantly, introducing the functional YPS163 allele of AQY1 into strains with different combinations of non-functional AQY alleles (including strains M22, K1, SK1, and S288c) did not significantly improve spore production (Figure 5B). Thus, strains lacking AQY function have also lost the ancestral need for AQY during spore production.

AQY function is required for sporulation in YPS163.
Fig. 5. AQY function is required for sporulation in YPS163.
(A) Denoted strains were sporulated as described in Methods for 2 days, and the number of events with 4, 3, 2 spores or ‘other’ (representing unsporulated or unscorable cells) was recorded. Each plot represents the average and standard deviation of biological triplicate. Results from S. paradoxus strain NRRL Y-17217 are shown here. (B) Vineyard strain M22, sake strain K1, SK1, and S228c derivative BY4743 (BY) harboring empty vector or pYPS_AQY1 plasmid were sporulated as described for 2–3 d before scoring.


This work provides the first clear evidence for adaptive loss of AQY function in subgroups of wild S. cerevisiae isolates. The excess polymorphism at AQY genes (McDonald-Kreitman and HKA tests), high between-group variation surrounding AQY2 that distinguishes strain groups (group variation and FST plots, Figure 3), and skew in the frequency spectrum toward intermediate-frequency AQY2 alleles (Tajima's D) are all consistent with non-neutral evolution. Furthermore, AQY paralogs have been lost at least 6 independent times, through 2 partial selective sweeps at AQY2 and possibly others at AQY1. The high variation between strain groups, and the non-random retention or loss of both paralogs in diverse strains, is consistent with the establishment of balanced polymorphism. We propose that the antagonistic pleiotropy of aquaporin function, coupled with spatial differences in selective pressures, provide pressure to maintain both functional or both non-functional alleles in distinct subpopulations of S. cerevisiae.

FT tolerance may be crucial for survival in cold climates, and along with sporulation efficiency may impart strong pressure to retain AQY genes in strains from wintry niches. Indeed, the ratio of non-synonymous to synonymous differences in YPS163 compared to S. paradoxus is 2 - 6X lower for AQYs compared to the genomic average (Ka/Ks of 0.018 and 0.059 for AQY2 and AQY1, respectively, versus 0.1 across all genes [35]). This is consistent with purifying selection acting to remove deleterious codon changes. The oak strains likely represent the ancestral state, since close relatives S. paradoxus and S. mikatae are also recovered from tree exudates and soil [17],[36], display high FT tolerance [9], and require aquaporins for sporulation (Figure 5 and data not shown). Interestingly, Northeastern-US oak strains display unique phenotypes suggestive of other evolutionary forces as well. AQY2 is expressed on average 14-fold higher in YPS163 compared to 17 other surveyed strains [9],[37]; those levels are doubled in YPS1009, which underwent a duplication of the entire chromosome 12 [9]. Although further studies will be needed, that over-expression of AQY2 is known to enhance FT tolerance in industrial strains [10] hints that the elevated expression may have been selected for, further underscoring the importance of AQY function in these strains.

In contrast, many other strains exist in warm environments that never experience freezing. Most of these were sampled from fruit substrates and distillations, which typically consist of ∼25% sugars [31], in contrast to oak soil [38],[39] from which many cold-climate strains have been isolated. Thus, the significant advantage in osmo-tolerance due to AQY loss likely played a major role in selection at this locus. It is unclear which came first–loss of aquaporin requirement during sporulation, or loss of aquaporin function that drove subsequent loss of the sporulation role. Loss of sporulation dependency on aquaporins, coupled with migration to warmer climates, would have relaxed constraint on the genes and facilitated their adaptive loss when cells moved to high-sugar substrates. This model could have involved a single loss of sporulation requirement followed by multiple independent losses of aquaporin function. Alternatively, strong selective pressure to lose aquaporins could have forced multiple independent losses of the sporulation requirement, just as it lead to multiple independent losses of aquaporin function.

S. cerevisiae strains are thought to have migrated globally through human association, after two domestication events produced sake/distillation strains and vineyard/wine-making lines ∼10,000 years ago [20],[21],[26],[27],[29],[40]. Human-facilitated migration may have significantly increased exposure of S. cerevisiae to diverse climates, which may have imposed new selective pressures when strains encountered new niches. Increased migration may also have facilitated outcrossing of domesticated strains with natural strains, allowing several of these alleles to spread through natural populations. It is important to note that Malaysian strains, not previously associated with domestication events, show unique non-functional AQY alleles, revealing that loss of aquaporins is not strictly driven by domestication.

The selective sweeps of nonfunctional aquaporin alleles appear to have been recent events, given the strength of the signal at AQY2, and may reflect an ongoing process. A remaining question is the fate of the emerged balance in polymorphism. Given sufficient migration of strains between the two niches and unequal fitness costs of the opposing haplotypes (i.e. two functional or two nonfunctional AQY alleles), one haplotype may eventually win out to fixation, eliminating the balanced alleles. On the other hand, long-term balancing selection could result if equivalent selective constraints are maintained in each respective niche. In the extreme case, strongly opposing selective forces could restrict yeast migration between environments to promote ecological speciation [41]. Little is known about S. cerevisiae migration between tree soil and fruits, although oak-soil strains are genetically well separated from vineyard/fermentation isolates [21],[29],[40],[42]. The antagonistic forces driving aquaporin loss at the cost of freeze-thaw sensitivity may be one factor that has limited gene flow between these niches.


Strains and plasmids

Strains and plasmid constructs are described in Table S6. Two S. cerevisiae strains (DY8 and DY9) were isolated from oak-tree soil from Maribel, Wisconsin using the method of [17], and typed by a mating/sporulation assay with a tester S. cerevisiae strain (Dan Kvitek and APG, unpublished). Gene deletions were created by homologous recombination, replacing AQY1 and/or AQY2 with KanMX3 or NatMX3 drug-resistance cassettes, respectively. Homothalic wild strains capable of mating-type switching (including YPS163, M22, and S. paradoxus) were sporulated and dissected, and drug-marked colonies were selected as homozygous diploids. In all cases, homozygous gene deletions were confirmed by diagnostic PCR. The region corresponding to the 870 bp full-length AQY2 ORF plus 971 bp upstream and 393 bp downstream sequence was cloned from YPS163 or BY4741, by homologous recombination replacing a GFP-ADH1-terminator cassette in plasmid BA1924 (provided by P. Kainth and B. Andrews), which is derived from pRS315-based CEN plasmid BA1926 [43] but with the NatMX3 cassette replacing the LEU2 marker. The region corresponding to the 918 bp full-length AQY1 ORF with the flanking 947 bp upstream and 747 bp downstream was similarly cloned. All clones were verified by sequencing. To assess functionality of the different alleles, AQY1 ORFs representing M22, BY4741, or Y55 alleles (identical to the YPS163 allele but harboring the A881 deletion) or the Malaysian AQY2 allele (identical to YPS163 except for the G528 deletion) were cloned between the native upstream and downstream AQY1 sequence from YPS163. This was done to prevent confounding influences on expression through variation in the flanking regulatory regions. Plasmids were introduced into YPS163 aqy1Δ, BY4741, or other naturally AQY-minus strains, and complementation of spore production in the YPS163 aqy1Δ mutant or of FT tolerance in BY4741 was scored (Table S2).


Yeast strains were grown at 30°C in YPD medium to an optical density at 600 nm (OD600) of 0.3–0.4 in 24-well plates. To measure freeze-thaw tolerance, 200 µl of cells was transferred to 1.5 ml tubes and frozen in a dry ice/ethanol bath (<−50C) for two hours or on ice as control. Viability was measured by scoring serial dilutions spotted onto agar plates as previously described [9], or using Live/Dead stain (Invitrogen, Carlsebad, CA) read on a Guava EasyCyte Plus flow cytometer (Millipore, Billerica, MA). Scores in Figure 2 correspond to high (>80% of YPS163 viability, three pluses), medium (50–80% viability, two pluses), low (<50% viability, one plus), or no detectible (minus sign) FT tolerance. Osmotic tolerance was measured by plating cells onto agar plates containing 1.5 M sorbitol. Percent viability was scored as the number of colony-forming units compared to the no-stress control plate.

Spore analysis

Cells were grown in YPD rich medium to OD600 nm of 1.0, harvested by centrifugation, resuspended in 1% potassium acetate, and incubated at 25°C for 2 or 9 days. Cells were harvested, diluted and the number of spores per tetrad was counted on a hemocytometer.

QTL mapping and sequence analysis

QTL mapping strains and analysis were as previously described [18], using the Haley-Knott algorithm implemented in R-QTL [44]. Two additional peaks in Figure 1B (left arm of Chromosome 2 and right arm of Chromosome 8) were not significant when Chromosome 12 and 16 QTL were held as fixed terms, suggesting the additional peaks may be false positives. Sequencing using Big-Dye (Applied Biosystems, Carlsbad, CA) scored at least 3 reads (including forward and reverse) per basepair from 2 independent genomic preparations (GenBank accessions GQ848552-74 and GQ870433-54). The vast majority of sequence data represented homozygous sites. The few base pairs with evidence of heterozygocity were represented by one of the alleles, randomly chosen. MK-tests, Tajima's D, and Ka/Ks were calculated in DNASP 5.0 [45] and ML-HKA was done as in [23] using sequence data from [9],[20] and here.

Genome-wide sequence analysis was performed using unimputed, aligned data from [21] with quality scores > = 40 (generously provided by Alan Moses), treating all gaps as missing data to avoid alignment errors. Strains were grouped according to AQY2 or AQY1 alleles (see Table S1 for details), and the average number of pairwise SNPs was calculated every 1000 bp with a 100 bp step size, for all pairs of strains within each group and for all strains in a given group compared to each strain outside that group. Within-group variation was scored for all 50 kb regions across the genome with a step size of 20 kb, and for all 5 kb regions with step size of 2 kb. These regions were compared to the 50 kb region highlighted in the text (position 22,000–72,000 in Figure 3) for strains with the G25 AQY2 allele and to a 5 kb region centered on AQY2 for other strain groups. All regions were ranked based on the average pairwise within-group variation to calculate the percentile rank of regions in question.

To monitor the skew variation within and between groups, a difference profile of between-group variation minus within-group variation (calculated as described above) was taken across the genome, and all contiguous regions (“peaks”) where the difference value was >1.5X the chromosome-wide average were identified (see Figure S2 and Table S7). The area under each peak was estimated by the trapezoidal method, and compared to the area under the peaks in Figure 3A and 3B spanning AQY2. For the partitioning sampling, we scanned for SNPs with at least 3 strains harboring the minor allele, every 10,000 bp across each of the 16 yeast chromosomes. Strains were partitioned based on that SNP, then the between-group and within-group variation was measured for 20,000 bp centered on the partitioning SNP, based on the average-pairwise differences every 1,000 bp with a 100 bp step size as above. A profile of the between-group variation minus the within-group variation was taken in every window. For each partitioning SNP, a peak in the difference profile was identify by walking outward until the difference value was <3.54, the cutoff used the genomic scan shown in Figure 3B. The area under the curve was calculated as above and compared to that measured at AQY2 by an identical procedure except that strains were partitioned by Asian G25 allele vs. all others strains or by 11-bp deletion vs. all other strains. Very similar percentile rankings were obtained if we scored 5 kb windows centered on each SNP (data not shown).

Supporting Information

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1. Mitchell-OldsT



2007 Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat Rev Genet 8 845 856

2. AkeyJM





2002 Interrogating a high-density SNP map for signatures of natural selection. Genome Res 12 1805 1814

3. ClarkAG





2003 Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science 302 1960 1963

4. NielsenR





2005 A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol 3 e170 doi:10.1371/journal.pbio.0030170

5. SabetiPC





2002 Detecting recent positive selection in the human genome from haplotype structure. Nature 419 832 837

6. VoightBF




2006 A map of recent positive selection in the human genome. PLoS Biol 4 e72 doi:10.1371/journal.pbio.0050147

7. YamasakiM





2005 A large-scale screen for artificial selection in maize identifies candidate agronomic loci for domestication and crop improvement. Plant Cell 17 2859 2872

8. MackayTF



2009 The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10 565 577

9. KvitekDJ



2008 Variations in stress sensitivity and genomic expression in diverse S. cerevisiae isolates. PLoS Genet 4 e1000223 doi:10.1371/journal.pgen.1000223

10. TangheA

Van DijckP




2002 Aquaporin expression correlates with freeze tolerance in baker's yeast, and overexpression improves freeze tolerance in industrial strains. Appl Environ Microbiol 68 5981 5989

11. TangheA

Van DijckP



2004 Aquaporin-mediated improvement of freeze tolerance of Saccharomyces cerevisiae is restricted to rapid freezing conditions. Appl Environ Microbiol 70 3377 3382

12. WolfeKH


1997 Molecular evidence for an ancient duplication of the entire yeast genome. Nature 387 708 713

13. BonhiversM




1998 Aquaporins in Saccharomyces. Genetic and functional distinctions between laboratory and wild-type strains. J Biol Chem 273 27565 27572

14. CarbreyJM




2001 Aquaporins in Saccharomyces: Characterization of a second functional water channel protein. Proc Natl Acad Sci U S A 98 1000 1005

15. LaizeV





1999 Molecular and functional study of AQY1 from Saccharomyces cerevisiae: role of the C-terminal domain. Biochem Biophys Res Commun 257 139 144

16. LaizeV




2000 Polymorphism of Saccharomyces cerevisiae aquaporins. Yeast 16 897 903

17. SniegowskiPD



2002 Saccharomyces cerevisiae and Saccharomyces paradoxus coexist in a natural woodland site in North America and display different levels of reproductive isolation from European conspecifics. FEMS Yeast Res 1 299 306

18. KimHS


2007 Genetic variation in the cysteine biosynthesis pathway causes sensitivity to pharmacological compounds. Proc Natl Acad Sci U S A 104 19387 19391

19. TangheA

Van DijckP


2006 Why do microorganisms have aquaporins? Trends Microbiol 14 78 85

20. FayJC


2005 Evidence for domesticated and wild populations of Saccharomyces cerevisiae. PLoS Genet 1 e5 doi:10.1371/journal.pgen.0010005

21. LitiG





2009 Population genomics of domestic and wild yeasts. Nature 458 337 341

22. McDonaldJH


1991 Adaptive protein evolution at the Adh locus in Drosophila. Nature 351 652 654

23. WrightSI


2004 The HKA test revisited: a maximum-likelihood-ratio test of the standard neutral model. Genetics 168 1071 1076

24. KreitmanM

2000 Methods to detect selection in populations with applications to the human. Annu Rev Genomics Hum Genet 1 539 559

25. CharlesworthD

2006 Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet 2 e64 doi:10.1371/journal.pgen.0020064

26. LegrasJL




2007 Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history. Mol Ecol 16 2091 2102

27. ReplanskyT




2008 Saccharomyces sensu stricto as a model system for evolution and ecology. Trends Ecol Evol 23 494 501

28. RuderferDM




2006 Population genomic analysis of outcrossing and recombination in yeast. Nat Genet 38 1077 1081

29. SchachererJ




2009 Comprehensive polymorphism survey elucidates population structure of Saccharomyces cerevisiae. Nature 458 342 345

30. TeshimaK



2006 How reliable are empirical genomic scans for selective sweeps? Genome Res 6 702 712

31. CatharinoRR





2006 Characterization of must and wine of six varieties of grapes by direct infusion electrospray ionization mass spectrometry. J Mass Spectrom 41 185 190

32. Sidoux-WalterF



2004 The Saccharomyces cerevisiae aquaporin Aqy1 is involved in sporulation. Proc Natl Acad Sci U S A 101 17422 17427

33. GerkeJP



2006 Natural isolates of Saccharomyces cerevisiae display complex genetic variation in sporulation efficiency. Genetics 174 985 997

34. KarpelJE


2006 Aquaporins in Saccharomyces cerevisiae wine yeast. FEMS Microbiol Lett 257 117 123

35. DonigerSW





2008 A catalog of neutral and deleterious polymorphism in yeast. PLoS Genet 4 e1000183 doi:10.1371/journal.pgen.1000183

36. NaumovGI





2000 Three new species in the Saccharomyces sensu stricto complex: Saccharomyces cariocanus, Saccharomyces kudriavzevii and Saccharomyces mikatae. Int J Syst Evol Microbiol 50 Pt 5 1931 1942

37. FayJC




2004 Population genetic variation in gene expression is associated with phenotypic variation in Saccharomyces cerevisiae. Genome Biol 5 R26

38. SampaioJP


2008 Natural populations of Saccharomyces kudriavzevii in Portugal are associated with oak bark and are sympatric with S. cerevisiae and S. paradoxus. Appl Environ Microbiol 74 2144 2152

39. XuZ




2001 Chemical properties associated with bacterial wetwood in red oaks. Wood Fiber Sci 33 76 83

40. AaE





2006 Population structure and gene evolution in Saccharomyces cerevisiae. FEMS Yeast Res 6 702 715

41. SchluterD

2009 Evidence for ecological speciation and its alternative. Science 323 737 741

42. DiezmannS


2009 Saccharomyces cerevisiae: population divergence and resistance to oxidative stress in clinical, domesticated and wild isolates. PLoS ONE 4 e5317 doi:10.1371/journal.pone.0005317

43. KainthP





2009 Comprehensive genetic analysis of transcription factor pathways using a dual reporter gene system in budding yeast. Methods 48 258 264

44. BromanKW




2003 R/qtl: QTL mapping in experimental crosses. Bioinformatics 19 889 890

45. LibradoP


2009 DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25 1451 1452

Genetika Reprodukční medicína

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PLOS Genetics

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