Deciphering the Genic Basis of Yeast Fitness Variation bySimultaneous Forward and Reverse Genetics

Calum J. Maclean,†,1 Brian P.H. Metzger,†,‡,1 Jian-Rong Yang,†,§,1 Wei-Chin Ho,1 Bryan Moyers,¶,2 andJianzhi Zhang*,1

1Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI‡Present address: Department of Ecology and Evolution, University of Chicago, Chicago, IL§Present address: Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China¶Present address: HudsonAlpha Institute for Biotechnology, Huntsville, AL

†These authors contributed equally to this work.*Corresponding author: E-mail: jianzhi@umich.edu.Associate editor: Ryan Hernandez

Abstract

The budding yeast Saccharomyces cerevisiae is the best studied eukaryote in molecular and cell biology, but its utility forunderstanding the genetic basis of phenotypic variation in natural populations is limited by inefficient association mappingdue to strong and complex population structure. To overcome this challenge, we generated genome sequences for 85 strainsand performed a comprehensive population genomic survey of a total of 190 diverse strains. We identified considerablevariation in population structure among chromosomes and identified 181 genes that are absent from the reference genome.Many of these nonreference genes are expressed and we functionally confirmed that two of these genes confer increasedresistance to antifungals. Next, we simultaneously measured the growth rates of over 4,500 laboratory strains, each of whichlacks a nonessential gene, and 81 natural strains across multiple environments using unique DNA barcode present in eachstrain. By combining the genome-wide reverse genetic information gained from the gene deletion strains with a genome-wide association analysis from the natural strains, we identified genomic regions associated with fitness variation in naturalpopulations. To experimentally validate a subset of these associations, we used reciprocal hemizygosity tests, finding thatwhile the combined forward and reverse genetic approaches can identify a single causal gene, the phenotypic consequencesof natural genetic variation often follow a complicated pattern. The resources and approach provided outline an efficientand reliable route to association mapping in yeast and significantly enhance its value as a model for understanding thegenetic mechanisms underlying phenotypic variation and evolution in natural populations.

Key words: Saccharomyces cerevisiae, GWAS, growth rate, genome sequencing, population structure, drug resistance.

Introduction

Understanding the genetic basis of phenotypic variation is amajor goal of modern biology. Model organisms play a prom-inent role in this endeavor because of the wealth of accumu-lated biological information and tools available formanipulating and examining these organisms. The buddingyeast Saccharomyces cerevisiae has long been a favored eukary-otic model organism to molecular and cell biologists and wasthe first eukaryote to have its genome fully sequenced(Goffeau et al. 1996). Large-scale phenotyping of gene deletion(Giaever et al. 2002; Ohya et al. 2005; Hillenmeyer et al. 2008;Giaever and Nislow 2014) and overexpression (Sopko et al.2006; Douglas et al. 2012) strains has provided extensive dataon gene function, while the availability of genome sequencesfrom closely related species (Kellis et al. 2003; Dujon et al. 2004;Scannell et al. 2011; Hittinger 2013; Liti et al. 2013) offers evo-lutionary insights into genotype–phenotype mapping.

Prior to 2009, the identification of genotype–phenotyperelationships in yeast relied on only a few laboratory strains,

which are now known to be phenotypic outliers (Liti et al.2009; Warringer et al. 2011). Recent years have seen intensi-fied research on the natural diversity and ecology of S. cere-visiae. For instance, S. cerevisiae has been isolated globallyfrom diverse natural and man-made environments (Litiet al. 2009; Wang et al. 2012) and shown to harbor greaterphenotypic diversity but much lower genetic diversity than itssister species S. paradoxus (Liti et al. 2009; Warringer et al.2011). As such, S. cerevisiae has great potential for linkingnatural variation in phenotype to genetic variation in individ-ual genes, a process that requires knowledge of both yeastgenomic diversity and population structure. Such knowledgehas accumulated primarily through low-coverage Sanger se-quencing (Liti et al. 2009) and tiling array hybridization(Schacherer et al. 2009). These studies, as well as restriction-site associated DNA sequencing of a large strain set (Cromieet al. 2013), have revealed a complex population structure ofS. cerevisiae. Higher-quality genomes produced by next-generation sequencing have further revealed the presenceof both copy-number variants and genomic rearrangements

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(Bergstrom et al. 2014; Hose et al. 2015; Strope et al. 2015), aswell as the origins of domestic S. cerevisiae strains (Galloneet al. 2016; Goncalves et al. 2016). Unfortunately, while strainsrepresenting pure lineages are often phenotypically distinct,many S. cerevisiae strains are mosaics with complex ancestryfrom multiple lineages due to human activity (Warringer et al.2011). This strong and complex population structure has madegenome-wide association study (GWAS), an important forwardgenetic method for detecting influential genetic variants inmany species, difficult in yeast (Connelly and Akey 2012; Diaoand Chen 2012). Consequently, the use of this otherwise pow-erful model species for systematic analysis of the genetic basis ofnatural phenotypic variation has been hindered.

To overcome this hurdle, we developed a resource forefficient GWAS in S. cerevisiae that simultaneously combinesforward and reverse genetic analyses. While these approachesare individually commonly used in the yeast community(Smith et al. 2011; Liti and Louis 2012; Swinnen et al. 2012;Fay 2013; Giaever and Nislow 2014; Long et al. 2015), ourapproach allows both forward and reverse genetic informa-tion to be gained from a single experiment. To do so, we firstgenerated genome sequences of 85 diverse S. cerevisiae strainsthat are genetically and phenotypically variable. Combiningthis data with available genome sequences from the literature,we assembled a dataset of 190 S. cerevisiae genomes andconducted a comprehensive population genomic analysis,identifying single nucleotide polymorphisms (SNPs) at�3.5% of sites. From this information, we elucidated detailedphylogenetic relationships among strains and the broad pop-ulation structure of the species. We detected genes from thenewly sequenced genomes that are absent from the referencegenome and demonstrated their expression and functions.We then barcoded the newly sequenced strains and simulta-neously phenotyped them with over 4,500 single genedeletion strains by a high-throughput barcode-sequencing(bar-seq) method (Smith et al. 2009). With the assistance ofthis reverse genetic information, our GWAS identified poten-tial causal genes responsible for growth rate variations in fiveof six environments examined. We experimentally verified asubset of these associations for high-temperature growth by areciprocal hemizygosity test (Steinmetz et al. 2002), establish-ing the combination of simultaneous association mappingand reverse genetics as a powerful approach for unbiasedidentification of the genic basis underlying fitness variationamong natural S. cerevisiae strains.

Results

Genomic analysis of S. cerevisiae Reveals a ComplexPopulation StructureTo identify genetic variation underlying phenotypic variationamong S. cerevisiae strains, we generated genome sequencesof 85 strains collected in six continents and from a variety ofhuman-associated and wild environments (fig. 1a; supple-mentary data S1, Supplementary Material online). We ob-tained an average of 3.75 million 2� 100-nucleotide paired-end reads per strain, approximately 97% of which were suc-cessfully mapped to the S288c reference genome.

This resulted in an average coverage of 60� per genome(range 38–99�) (supplementary data S2, SupplementaryMaterial online). On average, 6% of the reference genomewas not covered by a read in each sequenced strain due tostochastic sampling of reads and/or strain differences in genecontent as well as repeat elements. In total, we identified311,287 single nucleotide polymorphisms (SNPs) and 15,884insertions/deletions (indels).

To acquire a more comprehensive view of S. cerevisiaestrain relations, we identified 105 additional strains thathave publically available genome sequences (Hose et al.2015; Strope et al. 2015) at the time of our analysis (August2016), and applied our analysis pipeline to the Illumina se-quencing reads of these strains. A neighbor-joining tree of all190 strains was then constructed on the basis of a combinedset of 421,773 SNPs (fig. 1b; supplementary fig. S1 and dataS1–S2, Supplementary Material online). The tree was rootedusing outgroup sequences from recently identified Chineseisolates (Wang et al. 2012). We recovered phylogenetic clus-tering based roughly on geographical and environmental or-igins of the strains, consistent with early observations madefrom fewer strains and SNPs (Liti et al. 2009; Schacherer et al.2009). Clustering can be seen of strains into the West African,Malaysian/North American, Sake, Laboratory, and European/Wine groups previously identified. Additionally, we identifieda “Bakery” clade that was previously suggested to exist bymicrosatellite-based analysis (Legras et al. 2007) and a newNatural clade of three wild strains (one from soil in Illinois andtwo on the gums of wild cherry trees from unknown loca-tions) (fig. 1b). The remaining strains, originating from a widevariety of environments, form a group named “Mosaics”(fig. 1b; see below). While this work was under preparationfor submission, two genomic studies focusing on closely re-lated wine and beer strains were published (Gallone et al.2016; Goncalves et al. 2016). Because these domestic strainslargely represent a single clade in our analysis, these two stud-ies do not offer a species-wide view of yeast’s evolutionaryhistory and the exclusion of these strains does not affect theoverall phylogenetic patterns observed in our analysis.

To more closely examine the population structure ofS. cerevisiae, we employed a model-based clustering algorithmimplemented in fastSTRUCTURE (Raj et al. 2014) and iden-tified seven distinct subpopulations (top row in fig. 1c) thatare in agreement with the strain isolation sources and cor-roborate the clustering pattern seen in the phylogeny. In ad-dition, many strains that do not fall within a specific clade aremosaics with ancestry from several lineages (top row infig. 1c), supporting previous observations based on smallerdata (Liti et al. 2009). We then conducted thefastSTRUCTURE analysis for each of the 16 chromosomesand observed prominent among-chromosome variations inpopulation structure (fig. 1c). For example, the West Africansubpopulation is genetically distinct from other subpopula-tions for 10 of its 16 chromosomes, but is indistinct from thesake subpopulation for three chromosomes and indistinctfrom the Malaysian/North American subpopulation for an-other three chromosomes. This variation in chromosomalpopulation structure is indicative of differences in the

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FIG. 1. Geographical, environmental, and phylogenetic relationships of the 190 S. cerevisiae strains analyzed. (a) World map indicating thegeographic locations where the analyzed strains were sampled. Colors represent the environment of isolation if known. (b) Maximum compositelikelihood neighbor-joining tree of the 190 strains based on genome-wide SNP data. The environment type from which each strain was isolated isindicated as a colored circle. Branches are colored to denote clade. The scale bar represents 0.1% genome sequence divergence. Strain names inblack are those sequenced in this work, while those in gray were sequenced previously. The same tree with bootstrap values is shown insupplementary figure S1, Supplementary Material online. Trees based on individual chromosomes are provided in supplementary figure S2,Supplementary Material online. (c) Population structures of the 190 strains. Strains are arrayed based on clade membership in panel b. Different

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evolutionary histories of different chromosomes due to per-vasive gene flow. Because S. cerevisiae reproduces largely asex-ually (Tsai et al. 2008), rare crosses between lineages canestablish unique populations where distinct chromosomecombinations persist in the absence of outbreeding. The ob-served differences in chromosomal population structure arenot due to stochasticity in the population structure assess-ment because multiple runs on the same chromosome dis-played only minor variations. Furthermore, phylogeniesreconstructed using SNPs from individual chromosomes cor-roborate the fastSTRUCTURE results (supplementary fig. S2,Supplementary Material online).

The extent of linkage disequilibrium (LD) between SNPs isan important characteristic determining the highest possibleresolution of association analysis; the lower the LD, the higherthe resolution can be. We found that the mean LD measuredby r2 equals 0.0164 for SNPs within 100 nucleotides and ithalves as the physical distance increases to 1,200 nucleotides(fig. 1d). This fast breakdown of linkage disequilibrium is sim-ilar to previous reports (Liti et al. 2009). Given that the averagedistance between the beginning of one gene and that of thenext gene on the chromosome is �2 kb in yeast, this resultindicates that fine-scale mapping to the gene level should betheoretically possible by GWAS in this species. Nevertheless,due to the variation in recombination rate across the yeastgenome, mapping resolution is expected to vary among ge-nomic regions.

To further characterize the genetic variation present in S.cerevisiae, we conducted a comprehensive population geno-mic analysis of all 190 strains. The basic population geneticparameters are summarized in table 1. Intronic and intergenicpolymorphisms (hW) and nucleotide diversities (p) are signif-icantly lower than those at synonymous sites, suggesting per-vasive purifying selection acting on noncoding regions. This isconsistent with the fact that the compact nature of the yeastgenome results in intergenic regions dense with promoterand other important regulatory elements and that yeast in-trons can regulate gene expression (Juneau et al. 2006;Parenteau et al. 2008). Consistent with previous analyses con-ducted using a smaller data set (Liti et al. 2009), we found noclear sign of positive selection using the majority of popula-tion genetic tests we applied, and the estimated fraction ofadaptive amino acid substitutions was 0. However, removingthe Wine/European strains from analysis resulted in the de-tection of positive section in one test, suggesting that sam-pling biases and population structure may confound currentapproaches for detecting positive selection (supplementaryfigs. S3–S5 and tables S1–S2, Supplementary Material online;see “Materials and Methods” for details).

Finally, for the 85 newly sequenced genomes, we also an-alyzed and observed intron presence/absence polymorphisms(supplementary fig. S6, Supplementary Material online; see

“Materials and Methods”) and identified aneuploidies andlarge segmental duplications (supplementary fig. S7,Supplementary Material online; see “Materials andMethods”). Similar to the results of a previous in-depth in-vestigation into Saccharomyces mitochondrial DNA from alargely different set of strains (Wolters et al. 2015), we foundseveral differences in intron content between related isolates.We also detected many small- and large-scale duplication,many of which show no clear pattern of phylogenetic distri-bution (Hose et al. 2015).

Some Non-Reference Genes Confer Drug ResistanceOur knowledge of S. cerevisiae genome content and functionis derived largely from a few laboratory strains, which are nowknown to be phenotypically atypical (Warringer et al. 2011).Furthermore, S288c, the strain from which the reference set ofgenes are defined, was constructed in the 1980s largely from astrain that was isolated in the late 1930s (Mortimer andJohnston 1986). Because the reference strain has been main-tained in relatively benign and unvarying laboratory environ-ments and has undergone repeated population bottlenecks,often to single individuals, the possibility arises that genesimportant for survival outside of the laboratory environmenthave been lost. As a consequence, identifying coding regionsnot present in the reference strain has the potential to explainphenotypic variation among strains. Indeed, recent reportshave confirmed the presence of several nonreference genes inthe genomes of natural and industrial S. cerevisiae strains(Novo et al. 2009; Borneman and Pretorius 2015; McIlwainet al. 2016). To further our understanding of the distributionand importance of nonreference genes, we additionally per-formed a de novo genome assembly of the Illumina sequenc-ing reads obtained from our 85 strains (see supplementarydata S3, Supplementary Material online for assembly statis-tics). We identified 181 distinct nonreference genes distrib-uted across the phylogeny (fig. 2a; supplementary fig. S8 anddata S4, Supplementary Material online). The majority werefound to have a BLAST hit in a previously sequencednonS288c S. cerevisiae strain, while others had best hits inother fungi or more distantly related organisms (fig. 2a).The phylogenetic distribution of nonreference genes didnot follow a straightforward pattern; distantly related strains

FIG. 1 Continuedcolors show different inferred populations, which are indicated at the top of the panel. The Y-axis shows the fraction of SNPs coming from eachinferred population. (d) Linkage disequilibrium (LD) decays as the physical distance between two linked sites becomes larger. LD is measured by r2

between two linked sites minus the mean r2 between two sites located on different chromosomes.

Table 1. Polymorphism (hW) and Nucleotide Diversity (p) Per kb inDifferent Regions of the Yeast Genome.

hW PMean SD Mean SD

Intergenic 5.63 0.018 2.67 0.015Intronic 7.52 0.135 3.68 0.122Synonymous 14.60 0.035 9.08 0.037Nonsynonymous 3.63 0.010 1.37 0.007Total 5.97 0.009 2.99 0.009

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often share nonreference genes, suggesting independent gainsvia introgression or horizontal gene transfer, independentlosses, or segregating polymorphism (fig. 2a; supplementaryfig. S8, Supplementary Material online).

To begin characterization of these nonreference genes, wefirst estimated their expression levels in 23 S. cerevisiae strainswith available transcriptome data generated by mRNA

sequencing (RNA-seq) (Skelly et al. 2013). We found thaton average, 54.9% of the nonreference genes examined hada higher expression level than each of the 5% most lowlyexpressed reference genes (fig. 2d; supplementary data S5,Supplementary Material online). Because the RNA-seq datawere collected in a single benign environment, it is likely thatmore of the nonreference genes identified are expressed at

A

C

D

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FIG. 2. Origins, expressions, and functions of nonreference genes identified from the 85 newly sequenced S. cerevisiae genomes. (a) Evolutionaryorigins of nonreference genes. Color indicates the number of nonreference genes identified from each of the 85 genomes (strain name shown at thebottom of the panel) that received the best hit in a particular species listed at the right-hand side of the panel. The relationships of the 85 strains, asin figure 1b, are indicated by the phylogeny. (b) Expression levels of nonreference genes estimated using existing mRNA sequencing data for 23 ofthe 85 sequenced strains. Each circle represents a nonreference gene in the strain indicated at the bottom of the panel. The red horizontal barrepresents the lower fifth percentile of gene expression levels of all reference genes in that strain. FPKM, Fragments Per Kilobase of transcript perMillion mapped reads. (c) Fitness consequence of deleting Non-Ref-129 from UWOPS87-2421 in the presence of the antifungal fluconazole. Fitnessis quantified by efficiency (maximum OD). Wild-type and deletion strain data are shown by black solid line and dashed gray line, respectively.P-values from t-tests of the null hypothesis of no fitness effect from the gene deletion are indicated as follows: *�0.05; **�0.01; ***�0.001. Errorbars represent the standard error of the mean from three replicates. Maximum growth rate and phylogenetic position of strains containing Non-Ref-129 are shown in supplementary figure S9a, Supplementary Material online. (d) Fitness consequence of deleting Non-Ref-67 from CLIB272 inthe presence of the antifungal cantharidin. All notations are the same as in panel c. Maximum growth rate and phylogenetic position of strainscontaining Non-Ref-67 variants are shown in supplementary figure S9b, Supplementary Material online.

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appreciable levels in the appropriate environments due toconditional expression.

To examine the functional importance of nonreferencegenes, we focused on two of them for which our initialBLAST search revealed their closest hit to be within thewell annotated Lachancea thermotolerans genome.Following Liti et al. (2009) and Warringer et al. (2011), weused growth curves to determine the phenotypic conse-quences of deleting these nonreference genes on two aspectsof strain growth, maximum growth rate and efficiency (see“Materials and Methods”). Non-Ref-129, identified from theMalaysian strain UWOPS87-2421, resembles the L. thermoto-lerans coding region KLTH0E00528g, which is annotated as ahomolog of S. cerevisiae FLR1, a multi-drug transporter re-sponsible for the efflux of drugs such as the widely used an-tifungal fluconazole (Gbelska et al. 2006). Thus, Non-Ref-129,expressed even in a benign environment (5.25 RPKM; supple-mentary data S5, Supplementary Material online), may conferresistance to this important drug. We deleted Non-Ref-129from haploid UWOPS87-2421 cells and exposed both thewild-type and deletion strains to various fluconazole concen-trations to investigate the impact of gene deletion on straingrowth (fig. 2c). Deleting Non-Ref-129 had a small but signif-icant effect on the maximum growth rate (supplementary fig.S9a, Supplementary Material online) and a large effect ongrowth efficiency, especially when fluconazole concentrationis comparable with a typical high-dose fluconazole treatmentin clinical settings (>25mg/ml) (Menichetti et al. 1996; Martin1999) (fig. 2c). If Non-Ref-129 is indeed a drug transportersimilar to FLR1, it may also be involved in resistance to dia-zaborine, benomyl, methotrexate, and other drugs (Brocoet al. 1999; Jungwirth et al. 2000).

To better understand the history of Non-Ref-129, we per-formed additional BLAST searches in other published S. cer-evisiae genomes that were built de novo (Strope et al. 2015).We found in YJM653 an intact Non-Ref-129 and in YJM1250an apparently pseudogenized Non-Ref-129 that is disruptedby an insertion; these two strains respectively reside at theedge of and within the Wine/European clade, both beinghighly diverged from UWOPS87-2421 (supplementary fig.S9a, Supplementary Material online). UWOPS87-2421 andYJM653 differ at only one nonsynonymous site and no syn-onymous sites in this 1644-nucleotide gene. The highlysimilar chromosomal locations of all three Non-Ref-129genes in an unstable telomeric region (UWOPS87-2421,ChX:33185-34826; YJM653, ChX:30819-32462; YJM1250,ChX:22819-24457) suggest a single origin of Non-Ref-129in S. cerevisiae.

The second nonreference gene experimentally studied,Non-Ref-67, was initially identified in strains CLIB272 and Y6and found to be similar to the L. thermotolerans geneKLTH0H09460g, which is homologous to CRG1 in S. cerevisiae,a methyltransferase gene involved in lipid homeostasis andproviding resistance to the phosphatase inhibitor cantharidin(Lissina et al. 2011). Unlike Non-Ref-129, Non-Ref-67 is notexpressed in benign conditions (supplementary data S5,Supplementary Material online). However, because CRG1 ex-pression increases by 40- to 50-fold upon exposure to

cantharidin and other stresses (Lissina et al. 2011), it is possi-ble that Non-Ref-67 expression is condition-specific. We de-leted Non-Ref-67 from haploid CLIB272 cells and exposedwild-type and deletion strains to varying concentrations ofcantharidin (fig. 2d; see “Materials and Methods”). The dele-tion of Non-Ref-67 does not alter the maximum growth rateconsistently (supplementary fig. S9b, Supplementary Materialonline), but has a significant effect on growth efficiency atintermediate drug levels, with the deletion strain reachingonly�50% of the maximum OD of the wild-type strain ata cantharidin concentration of 12 mM (fig. 2d). AdditionalBLAST searches detected Non-Ref-67 in five S. cerevisiaegenomes previously published (Strope et al. 2015). Weidentified two distinct versions of Non-Ref-67 distributedacross the phylogeny (inset of supplementary fig. S9b,Supplementary Material online) that differ at 10 sites,including four nonsynonymous sites. However, the similargenomic location of these two variants, in a telomericregion of Ch. XV, suggests a single origin.

Two possible scenarios can explain the origin and phylo-genetic distribution of each of the nonreference genes studiedin depth here (Non-Ref-129 and Non-Ref-67). The first is that agene arose via horizontal gene transfer after the separation ofS. cerevisiae from S. paradoxus. The acquired gene may nothave been fixed in S. cerevisiae if the transfer was recent.Alternatively, the gene may have been fixed, followed by mul-tiple losses within S. cerevisiae. The two nonreference geneshave relatively low levels of sequence identity to the L. ther-motolerans genes mentioned, suggesting that the donor spe-cies have yet to be identified. The second scenario is thatthese nonreference genes arose from gene duplication. Therelatively large sequence dissimilarities of these genes withtheir closest paralogs in S. cerevisiae suggest that the duplica-tion events were ancient, implying multiple independentlosses of these genes in several yeast species as well as withinS. cerevisiae, which is possible given their subtelomeric loca-tions. While the first scenario appears more parsimonious,both scenarios remain possible at this stage.

Bar-Seq Allows High-Throughput SimultaneousPhenotyping of Thousands of StrainsAccurate phenotyping is central to uncovering the geneticbasis of phenotypic variation. Phenotyping different naturalyeast strains has primarily relied on the production and anal-ysis of growth curves (Warringer et al. 2011) or digitalphotography-based colony sizes (Bloom et al. 2013), whichhave limited throughput and resolution and can be time-consuming. We decided to adopt bar-seq (Smith et al.2009) for phenotyping, which allows for the simultaneousmeasurement of the growth rates of all strains of interest inthe same test tube through Illumina sequencing of strain-specific DNA barcodes. Bar-seq was originally designed toquantify the relative growth rates of S288c-derived gene de-letion strains each carrying two unique pieces of 20-nucleo-tide DNA (barcodes) inserted at the time of strainconstruction (Winzeler et al. 1999; Giaever et al. 2002). Wesimilarly constructed a panel of barcoded strains from a sub-set of the 85 strains sequenced in this work. Each carries two

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unique barcodes which are not present in any deletion strain.This not only allows the bar-seq experiment of the naturalstrains but also that of natural strains and deletion strains allin one test tube (fig. 3a). Although our methodology couldeasily be used to expand the strain set in the future, at thetime of our phenotypic analysis the additional 105 sequencedstrains included in our phylogenetic analysis were unavailable.

We successfully inserted the unique barcodes, flanking aG418 sulfate resistance marker (KanMX4), at the HO(YDL227C) locus of 81 of the 85 diploid strains sequencedhere (supplementary fig. S10 and data S1, SupplementaryMaterial online; see “Materials and Methods”). Of these 81heterozygous HO/hoD::KanMX4 strains, we obtained stable aand a haploids for 76 of them. We then created MATahoD::HygMX4 (Hygromycin B resistance) and MATa

hoD::NatMX4 (nourseothricin resistance) haploids throughmarker switching (supplementary data S1, SupplementaryMaterial online; see “Materials and Methods”).

To test the utility of these barcoded strains for studying thegenetic basis of phenotypic variation, we combined the 81barcoded diploid strains with the 4,653 diploid strains fromthe S. cerevisiae homozygous nonessential gene deletion col-lection to create a common starter pool to use across ourexperiments (fig. 3a). We grew this pool to a benign environ-ment (YPD at 30 �C) as well as six stressful environments: hightemperature (YPD at 40 �C), high salt/osmotic stress (YPD at30 �Cþ 1.25 M NaCl), high ethanol (YPD at 30 �Cþ 7% eth-anol), superoxide anions (YPD at 30 �Cþ 4 mM paraquat),oxidizing agents (YPD at 30 �Cþ 3 mM hydrogen peroxide),and a hypoxia mimetic (YPD at 30 �Cþ 1 mM cobalt

BA

C

FIG. 3. Simultaneous high-throughput phenotyping of 81 natural isolates and 4,521 gene deletion strains. (a) Flow chart showing the procedure ofbarcoding the natural isolates and simultaneously phenotyping natural isolates and gene deletion strains. (b) Heat map showing the fitness of 81natural isolates relative to the reference strain (S288c-derived HO deletion strain) in each stressful environment, relative to that in the benignenvironment of YPD at 30 �C. The colored arrows show the color scheme for relative fitness, with the most extreme colors depicting the mostextreme fitness values in each environment. Colored circles indicate the environmental origins of the natural isolates as in figure 1a. (c) The fitnessof 81 natural isolates and 4,521 S288c derived gene deletion strains relative to the reference strain in the high temperature environment, relative tothat in the benign environment. The natural isolates are shown by colored circles, based on the color scheme in figure 1a. The gene deletion strainsare shown by black or gray circles depending on their locations on odd-numbered or even-numbered chromosomes, respectively, and are arrangedby chromosomal position. Dashed vertical lines indicate the regions identified as significant by GWAS with those shown in red denoting thosefurther investigated by hemizygosity tests in figure 4.

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chloride). We extracted genomic DNAs from the commonstarting pool and following each competition, produced bar-seq libraries, and quantified the sequencing read number ofeach barcode, a proxy for strain frequency, at each time point,by Illumina sequencing (fig. 3a). We estimated the fitness ofeach strain in a particular environment relative to the benign30 �C YPD environment, relative to the BY4743-derived HOdeletion strain (Giaever et al. 2002) to identify strains withparticularly high or low relative fitness in the environment ofinterest (fig. 3b). While there is a clear phylogenetic compo-nent to the phenotypic similarity among some strains, beingparticularly apparent within the North American andMalaysian clades, other strains appear phenotypically divergedfrom the clades they are most closely related to. The bar-seqdata also allowed us to determine the effect each gene deletionhas across the tested environments, revealing that relative fit-ness varies greatly among the gene deletion strains, with 13.8%(623/4521) of gene deletion strains having significantly higherrelative fitness and 18.3% (829/4521) having significantly lowerrelative fitness than the reference strain in high temperature(fig. 3c; supplementary data S6, Supplementary Material on-line). Similar patterns are observed in the other five environ-ments examined (supplementary fig. S11 and data S6,Supplementary Material online). Broadly, the fitnesses ofboth the gene deletion strains and the natural strains are pos-itively correlated across the environments tested (supplemen-tary fig. S12, Supplementary Material online).

Combining Forward and Reverse Genetics RevealsSNPs Underlying Phenotypic DiversityUsing a multi-stage GWAS approach (supplementary fig. S13,Supplementary Material online; see “Materials andMethods”), we attempted to identify SNPs associated withrelative fitness variation among the 81 natural strains. Wediscovered between 3 and 19 associated SNPs per environ-ment after controlling for population structure for five of thesix environments examined (supplementary data S7,Supplementary Material online). For example, we detected13 SNPs associated with relative fitness at 40 �C. Five of the 13SNPs map to a �16 kb region on Ch. XI that contains theribosomal protein gene RPS21a, deletion of which is known toslow growth at high temperature (Sinha et al. 2008). Similarly,a second SNP on Ch. XI, located 51.5 kb from this cluster, iswithin 7 kb upstream and downstream of the genes DBP7,RPC37 and GCN3, which again are known to reduce heattolerance upon deletion, as well as SET3 and YKR023C, knownto reduce stress tolerance when deleted.

While association studies rarely validate that the identifiedSNPs or linked regions are responsible for the observed phe-notypic differences, validation can be performed in yeast.Such validations are an increasingly important step in under-standing the signals identified by GWAS because many con-founding factors—such as the strong population structureand linkage (Connelly and Akey 2012)—can lead to false sig-nals that are hard to untangle without direct genetic manip-ulations. To this end, we used reciprocal hemizygosity tests toidentify difference in fitness due to deletion of alternative al-leles in hybrids of high- and low-fitness strains at 40 �C relative

to 30 �C for genes surrounding each of several associated SNPs(fig. 4a; see “Materials and Methods”). In each competitionexperiment, one strain expressed yellow fluorescent protein(YFP), facilitating the quantification of relative fitness by flowcytometry (He et al. 2010). Reciprocal experiments with theYFP marker in opposing hybrid background were per-formed to remove any fitness effect of YFP expression.We chose two strains with high fitness (YPS128 andYJM320) and two with low fitness (W303, UWOPS05-227.2) at 40 �C relative to 30 �C and selected three sig-nificant SNPs for investigation, reciprocally deleting fourto six genes surrounding each SNP (supplementary dataS7, Supplementary Material online).

The first SNP of interest, the most significant identified, islocated at site 490,822 of chromosome XI within the bidirec-tional promoter of GCN3 and BCH2, both annotated as hav-ing temperature related deletion phenotypes. In addition,two neighboring genes (DBP7 and RPC37) are similarly anno-tated. Our bar-seq data showed that deleting DBP7 fromS288c drastically reduced fitness at 40 �C compared to thatat 30 �C. To investigate if variations in these genes cause thefitness variation in natural populations, we individually de-leted the alternative alleles in W303/YPS128 hybrids for eachof the above four genes and compared their growth rates at40 �C relative to that at 30 �C. As expected, the strain retain-ing the predicted high-fitness allele of DBP7 from YPS128grew significantly faster than the strain retaining the pre-dicted low-fitness allele from W303 at 40 �C relative to30 �C (fig. 4b). No such significant difference was observedfor the other three genes tested. Thus, variation in eitherfunction or expression of DBP7, which encodes a putativeATP-dependent RNA helicase of the DEAD-box family, likelycontributes to fitness variation at 40 �C among these strains.Interestingly, the causal gene is two genes (3.5 kb) away fromthe significantly associated SNP identified in GWAS.

The second SNP of interest is located within the codingregion of CNA1 (Ch XII, 1004315), a gene whose deletion isannotated as increasing stress susceptibility. We individuallydeleted the alternative alleles in W303/YPS128 hybrids forCNA1 and four neighboring genes and compared theirgrowth rates. Unexpectedly, retention of the high-fitness alleleresulted in a higher fitness than retention of the low-fitnessallele at 40 �C relative to 30 �C for four of the five genesexamined (fig. 4c). This suggests a complex genetic architec-ture and highlights that the associated SNPs are unlikely to becausal themselves, but instead simply markers for genomicregions harboring naturally occurring variation affecting thetrait of interest. Interestingly, unlike the results for DBP7, de-letion from S288c for two of these four genes did not have anappreciable impact on relative fitness, suggesting that genesunderlying phenotypic variation in natural populations candiffer from the genes affecting growth in the laboratory strain.As such, these results indicate the necessity of considering thegenetic background when determining gene–phenotype re-lationships. Furthermore, the type of mutations may alsomatter, because only null mutations are present in the dele-tion collection, while some gain-of-function mutations areexpected in natural strains.

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The third region investigated contains a cluster of five sig-nificantly associated SNPs on Ch. XI. Three of these are lo-cated in the coding region of DYN1, and one in each of thecoding regions of the upstream genes RHO4 and TRM2. Weindividually deleted alternative alleles of these three genes aswell as three additional genes, including the thermally anno-tated gene RPS21A, from the hybrid of UWOPS05-227.2 andYJM320. The results confirmed that the RHO4 and RPS21Aalleles have different effects on relative fitness at 40 �C(fig. 4d). However, deleting RHO4 from S288c did not signif-icantly alter the relative fitness at 40 �C, again suggestingdifferences in genetic background and/or type of mutationunderlying phenotypic variation in natural strains relative to alaboratory strain. Finally, for GLG1, the strain carrying the pre-dicted high-fitness allele was outcompeted by the strain car-rying the predicted low-fitness allele, suggesting not only thatan associated SNP may have multiple causal genetic variantsbut also that these variants may have opposite fitness effects.

Discussion

We have presented here a detailed overview of the genomicdiversity within S. cerevisiae by combining newly sequencedgenomes with those previously published. The genome se-quences, in conjunction with the genetically tractable hap-loids and diploids created, provide valuable resources to thecommunity for understanding the genetic basis of pheno-typic variation in yeast. This will not only be informativedue to the wealth of biological information we have aboutyeast but will also be useful to society due to the wide use ofdiverse yeast in many industries.

We found strong population structure in S. cerevisiae andsignificant variation in population structure and evolutionaryhistory among different parts of the yeast genome. The incon-gruences in phylogeny and population structure among differ-ent genomic regions are likely due, in a large part, to matingbetween divergent strains. Meiotic products of such hybrids

A

C

B

D

FIG. 4. Reciprocal hemizygosity test for causal effects of candidate genes surrounding SNPs identified by GWAS to be associated with relative fitnessat 40 �C. (a) Reciprocal hemizygosity test. Blue- and red-outlined cells depict strains carrying the predicted high- and low-fitness alleles, respec-tively. Black crosses indicate gene deletion. Yellow colored cells indicate YFP expression. The frequency of YFP and nonYFP expressing cells weredetermined at two time points. Competitions above and below the dashed line have alternative genotypes marked with YFP, allowing removal ofpotential fitness effects of YFP expression. (b) Fitness of the hemizygous strain deficient for the low-fitness allele of a candidate gene relative to thehemizygous strain deficient for the high-fitness allele in the high-temperature environment, relative to a benign environment. The low- and high-fitness alleles are from W303 and YPS128, respectively. Significant deviation of relative fitness from 1 is determined by a t-test using biologicalreplicates and is indicated as follows: *P� 0.05; **P� 0.01; ***P� 0.001. Error bars denote the 95% confidence intervals determined by Fieller’stheorem. The genes examined are shown at the bottom of the panel, with black arrows pointing to the significantly associated SNPs detected fromGWAS. The number below each gene is the fitness of the gene deletion strain relative to that of the reference strain at 40 �C relative to 30 �C, asshown in figure 3c. Genes with significant positive effects on relative fitness (when deleted) are indicated in green, while significant negative effectsare indicated in red. Gray and white coloring of genes denote no significant effect upon deletion and no data available, respectively. (c) Same aspanel b except for a different genomic region. (d) Same as panel b except for a different genomic region and the strains used. The low- and high-fitness alleles are from UWOPS05-227.2 and YJM320, respectively.

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and their subsequent asexual competition can quickly lead tosuch patterns. Even without meiosis, an apparently rare eventin yeast (Tsai et al. 2008), the production of beneficial aneu-ploidies during clonal growth can occur, removing some of thechromosomes derived from the hybrid-forming strains.

We identified SNPs associated with variation in growthrate under several environmental stresses relative to a benigncondition and validated a subset of these associations exper-imentally. These observations add much complexity to asso-ciation mapping in yeast, especially if the goal is to identifycausal genetic variants. For instance, only a minority of theassociated SNPs were located within the causal genes vali-dated by the hemizygosity test, suggesting that it is uncom-mon for a SNP identified by our association analysis to causethe observed fitness variation among the natural strains.Instead, the association analysis identified regions likely toharbor allelic variation affecting growth rate, and the reversegenetic data then more precisely located the causal genes. Inaddition to this problem, we found several cases where de-leting a gene from a laboratory strain had no appreciablephenotypic effect, yet alternative alleles segregating in naturalpopulations had different effects on growth rate. Becausethese genes are annotated as functional in the laboratorystrain, our finding suggests that gain-of-function mutationsrelative to the laboratory strain may be involved in naturalphenotypic variation and that forward genetics may some-times identify a genetic basis that is invisible by reverse geneticsof laboratory strains using gene deletion. Alternatively, theseresults may indicate that the effects of segregating variationdepend on genetic background, i.e. epistasis. For high temper-ature growth, this view is consistent with previous work iden-tifying variability in the gene-phenotype relationship acrossgenetic backgrounds (Sinha et al. 2006; Cubillos et al. 2013).Finally, we also observed instances where retention of the as-sumed fitter allele resulted in lower fitness than retention of theassumed less fit allele. Because the genetic basis of high tem-perature growth is one of the most extensively studied traits inS. cerevisiae (Steinmetz et al. 2002; Sinha et al. 2006; Donigeret al. 2008; Parts et al. 2011; Edwards and Gifford 2012; Bloomet al. 2013; Cubillos et al. 2013), it remains to be determined theextent to which this variability in the genic basis of complextraits is observed for other phenotypes. That we observed all ofthe above phenomena in mapping only a single trait suggeststhat this key model system for understanding eukaryotic cellbiology still has much to teach us about the genetic basis ofphenotypic diversity. The combination of forward and reversegenetic approaches in this model system offers one way inwhich this complexity can begin to be unraveled.

Materials and Methods

Strains and Strain ConstructionThe strains sequenced in this work were obtained from theauthors of two previous studies (Liti et al. 2009; Schachereret al. 2009) and are listed in supplementary data S1,Supplementary Material online. The information of geo-graphic location and environment of each strain is also pro-vided when available. Most strains used are originally diploid

and homothallic, and contain no tractable genetic marker,making tracking strains difficult and the maintenance of sta-ble haploid strains, necessary for many studies, impossible. Toproduce a set of strains useful to the community, we adoptedthe approach used in the construction of the S. cerevisiaegene deletion collection to introduce drug resistance markersflanked by two unique, strain-identifying, 20-nucleotide DNAbarcodes at the HO (YDL227C) locus of each strain (outlinedin supplementary fig. S10, Supplementary Material online).This process simultaneously removed the strains’ ability tomating-type switch and introduced a reliable means for straintracking. Diploid strains were transformed using the lithiumacetate method (Cubillos et al. 2009) with minor alterations.The �1 mg of transforming HO-targeting DNA contained aG418 sulfate resistance marker flanked by strain-specific barc-odes and was produced by two successive polymerase chainreaction (PCR) amplifications. We first amplified the KanMX4cassette from plasmid pFA6a-KanMX4 (Wach et al. 1994)using two 74-nucleotide primers each containing a unique20-nucleotide barcode, the sequences necessary for its ampli-fication (U1þU2 or D1þD2), and priming sites for the sec-ond PCR (supplementary data S8, Supplementary Materialonline). The second PCR used a dilution of the product ofthe first PCR as the template and added sequences homolo-gous to regions upstream and downstream of HO for target-ing and replacement of the locus (supplementary fig. S10,Supplementary Material online). The primers used in thisPCR differed by strain to maintain lineage-specific SNPs inthe region. A full list of the primers used can be found insupplementary data S8, Supplementary Material online. Toensure that the barcodes assigned to each strain are novel andmaintain their compatibility with those in gene deletionstrains (Giaever et al. 2002), molecular barcoded yeast(MoBY) ORF library (Ho et al. 2009), and existing technologiesused to estimate barcode frequency, we employed unusedbarcode sequences already present on the widely used Tag4array (Pierce et al. 2006). We confirmed successful insertion ofthe KanMX4 cassette by PCR and confirmed their sequenceusing Sanger sequencing. Although Cubillos et al. (2009) havepreviously produced a set of genetically tractable strains for asubset of genotypes studied, they were unsuitable for severalreasons. First, the shorter barcode (6 bp vs. 20 bp) reduces thenumber of strains that can be confidently mixed due to po-tential misidentification of barcodes and therefore counts inthe presence of sequencing errors. Second, the barcodes usedare incompatible with the bar-seq methodology used for thegene deletion lines due to differences in flanking sequences.This precludes the simultaneous single tube determination ofboth wild and deletion line fitness values. Third, all strains wereconverted to uracil auxotrophs by deletion of URA3, possiblyaffecting strain growth. Together, these considerations led usto construct this new resource for the community.

From these heterozygous HO marked diploids (MATa/MATa HO/hoD::Uptag-KanMX4-Downtag), stable haploidstrains were obtained by sporulation on potassium acetatemedia followed by ascus digestion and tetrad dissection. G418resistant colonies were identified by replication to YPD mediacontaining 300mg/ml G418 sulfate (Gold Biotechnology, US).

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Colony PCR was used to determine the mating type of indi-vidual colonies, and single MATa and MATa colonies werestreaked to obtain a pure strain of each mating type. Sampleswere grown overnight and frozen at �80 �C in 20% glycerolfor long-term storage. To allow for the easy formation of dip-loids between any two strains, we switched the drug resistancecassette carried by MATa and MATa strains to hygromycin Band nourseothricin, respectively (Gold Biotechnology). Thiswas achieved by the standard LiAc method using a PCR prod-uct produced by the use of primers specific to the TEF pro-moter and terminator common to all three drug resistancecassettes (Wach et al. 1994; Goldstein and McCusker 1999).

Two sets of strains were treated slightly differently due totheir genotypes. First, RM11 was previously made into a stablehaploid strain by insertion of a KanMX4 cassette at the HOlocus, resulting in the deletion of the targeting region we usedin all other strains (Brem et al. 2002). To insert the appropriatebarcodes into this background, unique homologous primerswere used to target and replace the existing KanMX4 cassettewith a HphMX4 marker amplified from plasmid pAG32(Goldstein and McCusker 1999). Unique barcodes werethen added and the cassette was switched back toKanMX4. Second, three strains (S288c, W303, and RM11)were already heterothallic haploids. After insertion of thebarcoded cassette at the HO locus, these strains were trans-formed with plasmid pCM66, which contains a galactose in-ducible copy of HO and a nourseothricin drug resistancemarker, to obtain strains of both mating types. After trans-formation, nourseothricin resistant cells were grown with ga-lactose as the sole carbon source at 30 �C without shaking for8 h to induce expression of HO. This allowed for mating-typeswitching and subsequent mother–daughter cell mating toproduce diploids. Cells were then streaked for single colonieson YPD (1% yeast extract, 2% peptone, 2% glucose, and 2%agar) plates, and the ploidy of single colonies was checked bycolony PCR using mating-type-specific primers. Diploid colo-nies were streaked for single colonies on fresh, nonselective,YPD plates and assayed for nourseothricin resistance. A singlecolony unable to grow in the presence of the drug, and there-fore having lost the plasmid, was selected for each strain.

We attempted to produce genetically tractable strains foreach of the 85 strains whose genomes we sequenced, butfound some to be unamenable to our approach, either dueto natural resistance to the drugs used or an inability to suc-cessfully sporulate and produce viable offspring of both mat-ing types. The full details of all tractable strains created andthe reason for missing strains are outlined in supplementarydata S1, Supplementary Material online.

Genome SequencingEach of the 85 strains was streaked from frozen stocks ontoYPD plates. Following 2 days of growth, a single colony waspicked into 5 ml of liquid YPD media and grown to saturation(36 h at 30 �C with shaking). Cultures were centrifuged tocollect cells, and DNA was extracted using standard methods.Dried DNA pellets were resuspended in 70 ml of Tris-EDTA(pH8.0), the DNA was quantified, and the purity was assessed,before DNA storage at �80 �C. Illumina libraries were

constructed using a protocol modified from a previous study(Rohland and Reich 2012). Briefly, 5 mg of genomic DNA wassheared using a Covaris S220 (duty cycle 10%, intensity 4,cycles/burst 200, time 55 s), of which 2 mg was used in libraryconstruction. To select DNA fragments of the desired sizerange (�400 nucleotides), we used DNA binding Magna beadsto perform dual size selection. The fragments were blunt-endrepaired, adapter ligated, and nick filled to repair the adapteroverhangs. Finally, sequences necessary for multiplexing andcluster formation on an Illumina HiSeq2000 were added byPCR. Equal amounts of each library were combined and runacross two paired-end 100-nucleotide lanes (43 strains in onelane and 42 in a second) of an Illumina HiSeq2000 at theUniversity of Michigan DNA sequencing core.

Read Mapping and SNP/Indel CallingReads were first trimmed using Cutadapt (Martin 2011) to re-move adapter sequences. Bowtie2 v2.1.0 (Langmead andSalzberg 2012) was used to map reads to the S288c reference(R64-1-1) genome under the sensitive local alignment mode,allowing up to 3 mismatches/indels per read. Pertinent statisticsobtained during the mapping process are listed in supplemen-tary data S2, Supplementary Material online. Paired reads wereconsidered nonconcordant and discarded from further analysisif apparent mapping locations were more than 1,200 nucleo-tides apart or if the paired reads appeared to completely overlapone another. Paired reads were also removed from further anal-ysis if either read was found to map ambiguously. Finally, weremoved PCR duplicates by discarding all but one copy of anyread pair found to map to exactly the same genomic position.

SAMtools v0.1.18 (Li et al. 2009) and VarScan v2.3.6(Koboldt et al. 2012) were used to identify SNPs and indelswithin each genome. Only variants identified by both pro-grams were used in downstream analysis. To further reducefalse calls due to misalignment of reads to the reference ge-nome, we removed variants that showed a significant strandbias (binomial P< 0.001) or invariant distance to the end ofsupporting reads (VDB< 0.0015) (Daneck et al. 2012). Onlythe most likely variant is listed for the indel and homozygousSNP lists. For the heterozygous SNP list, maximum likelihoodgenotype inferred by SAMtools is reported. To reduce errorsin estimating allele frequencies, we used only segregating siteswith reads covering the variant in each of the 85 strainsexcept when identifying pseudogenizing variants.

Phylogenetic ReconstructionWe reconstructed a maximum composite likelihoodneighbor-joining tree using MEGA 5.2 with all homozygousSNPs and all substitution types (Tamura et al. 2011). Weallowed heterogeneous rates amongst lineages and heteroge-neous rates amongst sites. Clades were identified in line withprevious studies (Liti et al. 2009; Schacherer et al. 2009). Toassess the strength of support for the phylogeny, we per-formed 1,000 bootstraps. Phylogenies of individual chromo-somes were reconstructed using the same method.

Population StructureTo assess the population structure of the 190 strains, we useda model-based Markov Chain Monte Carlo (MCMC)

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algorithm implemented in fastSTRUCTURE (Raj et al. 2014).For genome-wide population structure analysis, we randomlyselected 10% of nonsingleton homozygous SNPs. One hun-dred runs of fastSTRUCTURE for each of K¼ 2–9 were per-formed, with other parameters set as the default. K¼ 7 wasfound to be the best. The population structure that exhibitedthe maximum mean likelihood was plotted using R. Finally,the population structure of each chromosome was deter-mined using all homozygous SNPs on the specific chromo-some at K¼ 7.

Linkage DisequilibriumLD measured by r2 between every pair of SNPs was calculatedusing custom code. We then computed the average r2 for allSNP pairs with a distance in the range between x� 99 and xnucleotides, where x¼ 100, 200, 300, . . ., and 200,000. Wecomputed the expected LD between unlinked SNPs by cal-culating the mean r2 of 10,000 random pairs of SNPs locatedon different chromosomes. Following Schacherer et al. (2009),for each distance range, we presented the difference betweenan observed r2 and the expected r2 of unlinked SNPs infigure 1d.

Population Genomic Analysis of Natural SelectionWe used only SNP sites that are dimorphic and for which theancestral state could be unambiguously assigned in popula-tion genomic analysis. To infer SNP ancestral states, we tookadvantage of the published orthology information and multi-species genome sequences (Scannell et al. 2011). We used T-Coffee (Notredame et al. 2000) and the default settings inBioPerl to align the coding sequences of S. paradoxus, S.mikatae, and S. bayanus with the orthologous coding se-quences of the S. cerevisiae reference sequence R64-1-1.Using these multiple-sequence alignments, we consideredthe states of S. paradoxus, S. mikatae, and S. bayanus foreach SNP site and unambiguously assigned its ancestral stateif at least two of these outgroup species were in agreement.

We found polymorphisms that result in nonsense muta-tions to have much lower derived allele frequencies (DAFs)than nonsynonymous polymorphisms, which have lowerDAFs than synonymous polymorphisms (supplementary fig.S3, Supplementary Material online). This pattern suggeststhat purifying selection against nonsense mutations is gener-ally stronger than that against nonsynonymous mutations,which is in turn stronger than that against synonymous mu-tations. To examine whether purifying selection acts on syn-onymous mutations, especially in genes with strong codonusage bias (CUB), we measured CUB by codon-adaptationindex (CAI) of yeast genes previously published (Qian et al.2012b). We divided genes into two bins: those with CAI> 0.6and those with CAI� 0.6. We found that synonymous poly-morphisms in high-CAI genes tend to have lower DAFs thanthose in low-CAI genes (supplementary fig. S3,Supplementary Material online), supporting the hypothesisof purifying selection against synonymous mutations in geneswith strong CUB. For each SNP category, we also calculatedthe population genetic statistics Tajima’s D (1989), Fu and Li’sF (1993), and Fay and Wu’s H (2000). Compared with

synonymous polymorphisms, the more negative values of Dand F for nonsynonymous and nonsense polymorphisms areconsistent with the excess of rare alleles, and the less negativevalues of H are consistent with the deficiency of commonalleles (supplementary table S1, Supplementary Materialonline).

To assess potential positive selection at the protein level,we counted the number of synonymous polymorphisms (PS),nonsynonymous polymorphisms (PN), synonymous substitu-tions between S. cerevisiae and S. paradoxus (DS) and non-synonymous substitutions between S. cerevisiae andS. paradoxus (DN) in each gene. The McDonald–Kreitman(1991) test was performed using a two-tailed Fisher’s exacttest within R followed by a Bonferroni multiple-test correc-tion. To calculate the proportion of amino acid substitutionsdriven by positive selection (a) for each gene, we usedS. paradoxus as an outgroup. We first determined if DN/PN>DS/PS. When DN/PN>DS/PS, we calculated a by1-DSPN/(DNPS); otherwise, we calculated a’ by 1 � DNPS/(DSPN), which represents the fraction of nonsynonymous mu-tations under purifying selection. We found that the distribu-tion of a is largely consistent with widespread purifyingselection and relatively few instances of positive selection(supplementary fig. S4, Supplementary Material online). Inaddition, McDonald–Kreitman tests of individual genes failedto detect a significant signal of positive selection for any geneafter Bonferroni correction. This result is consistent with aprevious analysis of a smaller yeast dataset (Liti et al. 2009).McDonald–Kreitman tests suggested that 6.9% of genes areunder significant purifying selection (Bonferroni correctedP-value< 0.05).

Because slightly deleterious alleles can bias the estimationof a, we further estimated a using the approach proposed byEyre-Walker and Keightley (2009). Briefly, their approach usespolymorphism data to estimate the distribution of fitnesseffects of new deleterious mutations (DFE) and then usesDFE to predict the number of neutral and adaptive substitu-tions. When using this approach with a two-epoch model, wefound the estimated a to be �0.11 (supplementary table S2,Supplementary Material online), which is again consistentwith the lack of signal of positive selection.

Recently, Messer and Petrov (2013) proposed a heuristicmethod to estimate a by considering a values for polymor-phic sites with different levels of DAF. Following Messer andPetrov (2013), we applied the extended version of theMcDonald–Kreitman test to all genes. Starting with allSNPs, we sequentially raised the threshold for DAF and recal-culated a using only SNPs that pass the threshold. UsingMATLAB, we applied a nonlinear least-square method to fitthe data to the function of ax¼ aþ be�cx, where x is the DAFthreshold and ax is the corresponding a. We restricted b< 0and c> 0. While the theoretical support of Messer andPetrov’s approach is lacking, this approach, surprisingly, con-verges on an estimated value of 0.55 for a, suggesting that�55% of between-species sequence divergence at nonsynon-ymous sites is due to positive selection (supplementary fig.S5a, Supplementary Material online). Interestingly, the plot ofa vs. DAF showed a clear valley around intermediate DAFs

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that has not been observed previously. Suspecting the reasonmight be heterogeneous selection across strains, we parti-tioned strains based on phylogenetic clustering. We foundthat if we separated strains from the Wine/European clusterfrom all other strains and performed the same analysis on thetwo groups separately, the plots of a vs. DAF were dramati-cally different from each other. The extrapolated a valueswere �0.36 for the Wine/European cluster and 0.56 for allother strains. This difference in the estimate of a was notcaused by sampling in general, but the specific partitionsused (supplementary fig. S5b, c, Supplementary Material on-line). In addition, the a vs. DAF plot for strains not in theWine/European cluster showed a better fit to an exponentialcurve than the combined analysis (adjusted R2¼ 0.85 with-out Wine/Europen strains vs. adjusted R2¼ 0.69 for allstrains). Overall, these results suggest that the historical actionof natural selection within these two groups has been differ-ent and that positive selection in yeast may be more commonthan initially expected, especially outside of domesticatedwine strains. These results, however, are inconsistent withthe results obtained using the method of Eyre-Walker andKeightley (2009), where no positive values are apparent whenusing either only strains in the Wine/European cluster or onlystrains outside the Wine/European cluster (supplementarytable S2, Supplementary Material online).

De Novo Assembly and Identification ofNon-Reference GenesDe novo genome assembly was performed usingSOAPdenovo2 v2.04 (Luo et al. 2012) with K¼ 51 for alladapter-trimmed reads from each of the 85 strains we se-quenced. Basic statistics of the genome assemblies are listedin supplementary data S3, Supplementary Material online. Toexamine the quality of the assemblies, we used BLASTn tosearch for the KanMX4 vector sequence present at the HOlocus of 81 genomes. We then used it as an anchor to extractthe strain-specific barcodes (UPTAG and DSTAG). We suc-cessfully recovered the strain-specific barcodes for all 81 ge-nomes. To rule out false positive de novo gene calls, Exoneratev2.2.0 (Slater and Birney 2005) was used to align known genesin the reference genome of S288c to the assembled contigs.Known genes were localized onto contigs by prioritizing theExonerate hits by (i) best hit with syntenic neighbor genes ateither side, (ii) best hit with 100% query sequence coverage,and (iii) hits longer than 200 nucleotides that do not overlapwith better hits by more than 30 nucleotides—in case a geneis split among multiple contigs. Having identified the loca-tions of known genes on de novo contigs, we used GeneMarkSv4.17 (Besemer et al. 2001) to perform gene predictions andcompared these to the locations of known genes.

Predicted genes that showed no overlap with known genelocations were considered candidate nonreference genes. Toavoid false positives caused by un-localized known genes, weused BLASTn to align the predicted genes with cDNA se-quences of known genes. All predicted genes with hits cov-ering 80% of the query, or with a>200-nucleotide region thatis >90% identical with any reference gene, were removed. Inorder to classify the origin of remaining nonreference genes,

we retrieved the best hit in the NCBI “nr” database reportedby BLASTn and tBLASTx for each nonreference gene. If thebest hit was in the reference genome, it was also removedunless there was a premature stop codon for the hit region inthe reference genome. Finally, we removed nonreferencegenes with best hits to sequences derived from vectors, syn-thetic constructs, phages, bacterial genomes, or tandem ele-ments (supplementary data S4, Supplementary Materialonline).

To access the expression levels of the nonreference genes,RNA-seq data of 23 S. cerevisiae strains generated by SOLiDwere downloaded (Skelly et al. 2013). The color space RNA-seq reads from each strain were mapped to known and pre-dicted nonreference genes using bowtie (Langmead et al.2009), allowing up to two mismatches in color space. Thebest hits for each read were then used to calculate the ReadsPer Kilobase of transcript per Million mapped reads (RPKM)for each gene.

Phenotypic Consequences of DeletingNon-Reference GenesTwo newly identified nonreference genes were deleted fromthe genetic backgrounds in which they were discovered usingthe G418 sulfate resistance cassette KanMX4 that was PCR-amplified from plasmid pFA6a–KanMX4 (Wach et al. 1994),followed by PCR confirmation (see supplementary data S8,Supplementary Material online for primer sequences). Non-Ref-129 was deleted from strain UWOPS87-2421, while Non-Ref-67 was deleted from strain CLIB272. Both backgroundswere MATa hoD::NatMX4 genotypes detailed below.

To test the phenotypic consequences of the deletion ofthese two genes, growth curves were obtained using aBioscreen C (Growth Curves, USA). Non-Ref-129 deletionstrain and wild-type strain were grown in CompleteSupplemented Media (CSM) containing varying concentra-tions (0, 3, 6, 12.5, 25, 37.5, 50, 75, and 100 mg/ml) of flucon-azole (Sigma Aldrich). Non-Ref-67 deletion strain andwild-type strain were also grown in CSM media but usingvarying concentrations (0, 1.5, 3, 6, 12, 25, 50, and 100 mM)of cantharidin. To initiate the growth, we grew 5 ml CSM cul-tures from frozen stocks for 24 h. Each strain was diluted 200times into 350 ml of the appropriate drug-containing media intriplicate. Growth curves were collected at 30 �C for 60 h usingthe Wide-Band 420–580 nm filter every 20 min. Maximumgrowth rate (OD/h) was collected from growth curve dataas in a previous study (Warringer et al. 2011) and efficiency(Max OD) was calculated by taking the average of the 3rd–7thhighest OD values recorded. Two-tailed t-tests were used toassess the significance of differences between genotypes. Threereplications were performed for each growth curve.

Identification of Intron LossesBased on S288c reference genome annotation, we built a se-quence database containing all known exon–exon junctionsup to 101 nucleotides from each side of the junction. To searchfor reads supporting an intron loss event, all reads weremapped to this database by Bowtie2. We required at least95% coverage of the query read and that the read mapped

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to at least 20 nucleotides in each of two adjacent exons. Wefurther filtered ambiguous mappings on the reference genomeby BLASTn with an E-value cutoff at 0.01. To search for readssupporting the presence of introns, the same procedure wasconducted for all sequences annotated as exon–intron bor-ders in the S288c reference genome. Finally, intron loss wasdeclared in strains in which at least two reads span the exon–exon junction but no read spans the two correspondingexon–intron borders.

Identifying Potential Copy Number Variants (CNVs)and AneuploidiesTo assess gene duplication/deletion events, read pairs thatwere concordantly mapped to the reference genome follow-ing filtering of potential PCR duplicates were analyzed usingCufflinks v2.1.1 (Trapnell et al. 2010) to generate FragmentsPer Kilobase of transcript per Million mapped reads (FPKM)values for each coding sequence (CDS). Potential CNVs ofindividual genes, as well as aneuploidies and large scale du-plications, were then identified by dividing each FPKM by thatobtained from the same CDS in the reference strain S288c.

Simultaneous Phenotyping of Barcoded Yeast StrainsTo phenotype all strains with unique barcodes, we used bar-seq (Smith et al. 2009) to estimate their relative growth ratesin each of seven environments. The barcoded strains weremixed approximately equally and combined with the diploidhomozygous gene deletion collection (Invitrogen95401.H1Pool). Each nondeletion strain was present at ap-proximately twice the initial population size of each genedeletion strain. The initial pool of strains was grown for ap-proximately two generations in 25 ml of YPD media at 30 �Cbefore the resulting culture, termed generation 0, was used toinitiate competitions in each of the experiments. To reducethe effect of genetic drift, large populations were maintainedthroughout competitions with regular transfers to fresh me-dia every 4–5 generations to maintain populations in expo-nential growth. Populations competed for approximately 30generations (6 transfers) and samples were stored at�80 �Cfollowing each transfer. Following preliminary investigations,we chose to carry out in-depth analysis of the populationsfollowing the second transfer (�10 generations) in YPD at30 �C, YPD at 40 �C, YPDþ 1.25 M NaCl at 30 �C, YPDþ 8%EtOH at 30 �C, YPDþ 4 mM paraquat (superoxide) at 30 �C,YPDþ 3 mM Hydrogen peroxide at 30 �C, and YPDþ 1 mMcobalt chloride at 30 �C, respectively.

To determine the frequency of strains in the pooled pop-ulation at a given time point, we extracted genomic DNAfrom samples using a Puregene Yeast/Bacteria DNA extrac-tion kit (Qiagen). DNA barcodes were amplified by PCR usingAccuprime pfx (Invitrogen). The primers used for barcodeamplification also added sequences necessary for cluster for-mation and sequencing primer annealing on the Illuminaplatform. Because the downstream barcode is known to bemissing in some deletion strains (Deutschbauer et al. 2005),only the upstream barcodes were used. Fifty base-pair single-end sequence reads were obtained using one lane of anIllumina Genome Analyzer IIx at the University of Michigan

DNA Sequencing Core. The Illumina Pipeline software version1.6 was used for base calling from the image data. Because allsequences started with the same 18 base pairs of the PCRprimer region and this uniformity adversely affected basecalling, we removed the first 18 sequencing cycles beforebase calling. We used the previously published “gene-barcodemap” (Qian et al. 2012a) with the addition of our own strains’barcode identities to assign each read to a particular strainallowing for only a single mismatch. We followed a previouslypublished outline of bar-seq analysis (Robinson et al. 2014).We required that barcodes be represented by at least 40 totalcounts in populations analyzed before (generation 0) andafter competition. Count numbers were then normalized us-ing the TMM method implemented in the R package egdeR(Robinson et al. 2010). Because we are interested in pheno-types that are specific to a given condition, as opposed to thegeneral fitness across environments, we computed fitness in aspecific condition relative to that in YPD (the benign condi-tion). Overall there were 79 natural S. cerevisiae strains and4,498 deletion strains with usable data.

Let the number of reads for a genotype of interest at thebeginning of the competitions be N0 and the correspondingnumber for the reference genotype (i.e., the strain lacking HO)be M0. Let the numbers of reads for the above two genotypesafter competition in the benign environment of YPD at 30 �Cbe N1 and M1, respectively, and the corresponding numbersin a stressful environment of interest be N2 and M2, respec-tively. The competition lasted for g¼�10 generations ineach environment. Note that the inaccuracy in g does notaffect comparison of relative fitness among strains becausethe same g applies to all strains. The fitness of the genotype ofinterest relative to that of the reference genotype in thestressful environment, relative to that in the benign environment

is R ¼ ½ðN2=N0Þ=ðM2=M0Þ�1=g

½ðN1=N0Þ=ðM1=M0Þ�1=g ¼ N2M1

N1M2

� �1=g

.

Two biological replicates of the competition and bar-seqwere conducted in YPD at 30 �C. Based on the two estimatesof N1/M1, we tested whether N2/M2 is significantly differentfrom N1/M1 (i.e., whether R is significantly different from 1),followed by FDR correction for multiple testing.

GWASWe followed a multistep GWAS approach (Listgarten et al.2012, 2013) outlined in supplementary fig. S13,Supplementary Material online. For each environment, wefirst scaled and centered the relative growth rates. We re-moved all sites with a minor allele frequency below 5% acrossthe set of strains for which we had phenotype data. We thenremoved sites for which data were missing in >5% of strainsand all mitochondrial sites. The remaining 123,121 sites wereconverted into a 0, 0.5 and 1 format to represent homozygousnonreference, heterozygous, and homozygous referencestates in each strain, respectively.

Besides true positives, individual SNPs can be significantlyassociated with a phenotype because of chance or the con-founding effect of population structure. Because over- or un-der-correction of population structure can lead to a loss ofstatistical power or false positives, respectively, we performed

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the GWAS in a number of steps to attempt to find an ap-propriate balance. First, we performed an association betweenthe normalized phenotype values and each SNP without con-trolling for population structure by a simple linear regressionmethod without covariates. Second, we used this unstruc-tured association to rank all SNPs based on their statisticalsignificance of association. From this list, we performed aseries of associations by maximum likelihood using EMMA(Kang et al. 2008). To control for population structure, weestimated a kinship matrix based on a specific set of SNPs. Todefine this set, we started with the 1,000 SNPs most signifi-cantly associated with the phenotype in the unstructuredanalysis and then successively added the next 1,000 mostsignificant SNPs from the unstructured analysis until120,000 SNPs were included. For each association, the geno-mic control factor, lambda, was calculated using gcontrol2within R (Devlin and Roeder 1999). We identified the minimalkinship set that controlled for population structure based onwhere lambda first hit 1, or if it failed to do so, was minimized(supplementary fig. S12, Supplementary Material online).Third, we ran an additional series of associations centeredon the 3,000 SNP region identified by lambda using kinshipsets in 50 SNP windows. Again, we found the smallest kinshipset where lambda hit 1 or was at its minimum. Finally, weperformed an association for the 500 most significant SNPsfrom the unstructured association. In each case, we used theestimated kinship set that was optimal for lambda, minus anySNPs within 10 kb of the focal SNP, to estimate populationstructure. Any variant in this final association with a P-valuebelow 0.0001 (i.e., 5%/500) was classified as significant. Forthese SNPs, we identified the coding region nearest to thevariant as well as its immediate neighbors. These candidateSNPs generally cover a 4–10 kb region which is approximately4–8 times the range over which LD is seen to break down.

Experimental Validation of GWAS ResultsTo confirm the validity of our GWAS approach and to narrowdown the genes responsible for the observed phenotypic var-iations within the regions surrounding the significant SNPs,we performed reciprocal hemizygosity tests (Steinmetz et al.2002). We chose to concentrate on a subset of SNPs signifi-cantly associated with relative fitness at 40 �C. The samecandidate genes were deleted from haploid MATahoD::HygMX4 backgrounds that showed a high relative fitnessand a low relative fitness using a KanMX4 marker.Appropriate strain hybrids were formed by mating of thesedeletion strains to MATa cells. Two types of hybrid wereformed to allow for the relative fitness of reciprocal hemi-zygotes to be ascertained. The first was formed by mating ofdeletion-carrying haploids to MATa hoD::NatMX4 of theother genetic background and the second was formed bymating to MATa hoD::TDH3p-YFP-NatMX4 cells. This re-sulted in four hybrid strains for each gene to be tested, fluo-rescent and nonfluorescent hybrids lacking the high-fitnessallele (DH-F and DH-NF) and fluorescent and nonfluorescenthybrids lacking the low-fitness allele (DL-F and DL-NF). Allstrains were frozen at�80 �C until needed. To perform com-petitive fitness assays, freezer samples were inoculated into

1 ml of YPD and grown with shaking for 24 h at 30 �C. Diploidhybrids were paired so that each competition included twohybrids carrying reciprocal deletions of the alternative allelesand identifying markers. That is, DH-F was paired with DL-NFin the first competition, and DL-F was paired with DH-NF inthe second competition. Competitions were performed bymixing equal volumes of the two strains to form the t0 pop-ulation which was diluted 1,000 fold into 1.5 ml of fresh YPDmedia pre-warmed at 40 �C. Strains competed for 36 h, or�10 generations, at 40 �C with shaking till saturation (t1). Allcompetitions were performed in 96 deep-well plates withfour replicates of each competition. The frequencies of fluo-rescent and nonfluorescent cells within t0 and t1 populationswere determined by flow cytometry using a BD Accuri C6attached to a hypercyt sampling robot. After data cleaning toremove artifacts, the relative fitness of the competing strains

was calculated using F ¼ ð#of DH cells at t1=# of DL cells at t1

#of DH cells at t0=# of DL cells at t0Þ1=G,

where G¼ 10 denotes the number of generations of compe-tition. The same experiment was repeated in YPD at 30 �C.Relative fitness was computed by dividing fitness at 40 �C byfitness at 30 �C. Five replicate competitions were performedfor each set of strains and the relative fitness was calculated byaveraging over the reciprocal YFP-marked strain pairs for eachgene. Statistical significance was obtained by t tests.

Data AccessThe yeast genome sequences determined in this work havebeen deposited to NCBI with the BioProject ID ofPRJNA320792.

Supplementary MaterialSupplementary data are available at Molecular Biology andEvolution online.

AcknowledgmentsWe thank Gianni Liti and Leonid Kruglyak for sharing theyeast strains that are sequenced in this study, AudreyGasch for help accessing read data from previously sequencedstrains, and two anonymous reviewers for constructive com-ments. This work was supported by a research grant from theU.S. National Science Foundation (MCB-1329578) to J.Z.

ReferencesBergstrom A, Simpson JT, Salinas F, Barre B, Parts L, Zia A, Nguyen Ba AN,

Moses AM, Louis EJ, Mustonen V, et al. 2014. A high-definition viewof functional genetic variation from natural yeast genomes. Mol BiolEvol. 31:1–17.

Besemer J, Lomsadze A, Borodovsky M. 2001. GeneMarkS: a self-trainingmethod for prediction of gene starts in microbial genomes.Implications for finding sequence motifs in regulatory regions.Nucleic Acids Res. 29:2607–2618.

Bloom JS, Ehrenreich IM, Loo WT, Lite T-LV, Kruglyak L. 2013. Finding thesources of missing heritability in a yeast cross. Nature 494:234–237.

Borneman AR, Pretorius IS. 2015. Genomic insights into theSaccharomyces sensu stricto complex. Genetics 199:281–291.

Brem RB, Yvert G, Clinton R, Kruglyak L. 2002. Genetic dissection oftranscriptional regulation in budding yeast. Science 296:752–755.

Broco N, Tenreiro S, Viegas CA, S�a-Correia I. 1999. FLR1 gene (ORFYBR008c) is required for benomyl and methotrexate resistance in

Maclean et al. . doi:10.1093/molbev/msx151 MBE

2500Downloaded from https://academic.oup.com/mbe/article-abstract/34/10/2486/3797322/Deciphering-the-Genic-Basis-of-Yeast-Fitnessby University of Michigan useron 03 October 2017

Saccharomyces cerevisiae and its benomyl-induced expression is de-pendent on pdr3 transcriptional regulator. Yeast 15:1595–1608.

Connelly CF, Akey JM. 2012. On the prospects of whole-genome asso-ciation mapping in Saccharomyces cerevisiae. Genetics191:1345–1353.

Cromie GA, Hyma KE, Ludlow CL, Garmendia-Torres C, Gilbert TL, MayP, Huang A. a, Dudley AM, Fay JC. 2013. Genomic sequence diversityand population structure of Saccharomyces cerevisiae assessed byRAD-seq. G3 3:2163–2171.

Cubillos FA, Louis EJ, Liti G. 2009. Generation of a large set of geneticallytractable haploid and diploid Saccharomyces strains. FEMS Yeast Res.9:1217–1225.

Cubillos FA, Parts L, Salinas F, Bergstrom A, Scovacricchi E, Zia A,Illingworth CJR, Mustonen V, Ibstedt S, Warringer J, et al. 2013.High-resolution mapping of complex traits with a four-parent ad-vanced intercross yeast population. Genetics 195:1141–1155.

Daneck P, Nellaker C, McIntyre RE, Buendia-Buendia JE, Bumpstead S,Ponting CP, Flint J, Durbin R, Keane TM, Adams DJ. 2012. High levelsof RNA-editing site conservation amongst 15 laboratory mousestrains. Genome Biol. 13:R26.

Deutschbauer AM, Jaramillo DF, Proctor M, Kumm J, Hillenmeyer ME,Davis RW, Nislow C, Giaever G. 2005. Mechanisms of haploinsuffi-ciency revealed by genome-wide profiling in yeast. Genetics169:1915–1925.

Devlin B, Roeder K. 1999. Genomic control for association studies.Biometrics 55:997–1004.

Diao L, Chen KC. 2012. Local ancestry corrects for population structurein Saccharomyces cerevisiae genome-wide association studies.Genetics 192:1503–1511.

Doniger SW, Kim HS, Swain D, Corcuera D, Williams M, Yang S-P, Fay JC.2008. A catalog of neutral and deleterious polymorphism in yeast.PLOS Genet. 4:e1000183.

Douglas AC, Smith AM, Sharifpoor S, Yan Z, Durbic T, Heisler LE, Lee AY,Ryan O, Gottert H, Surendra A, et al. 2012. Functional analysis with abarcoder yeast gene overexpression system. G3 2:1279–1289.

Dujon B, Sherman D, Fischer G, Durrens P, Casaregola S, Lafontaine I,Montigny J, De Blanchin S, Beckerich J, Beyne E, et al. 2004. Genomeevolution in yeasts. Nature 35–44.

Edwards MD, Gifford DK. 2012. High-resolution genetic mapping withpooled sequencing. BMC Bioinformatics 13 Suppl 6:S8.

Eyre-Walker A, Keightley PD. 2009. Estimating the rate of adaptive mo-lecular evolution in the presence of slightly deleterious mutationsand population size change. Mol Biol Evol. 26:2097–2108.

Fay JC. 2013. The molecular basis of phenotypic variation in yeast. CurrOpin Genet Dev. 23:672–677.

Fay JC, Wu CI. 2000. Hitchhiking under positive Darwinian selection.Genetics 155:1405–1413.

Fu YX, Li WH. 1993. Statistical tests of neutrality of mutations. Genetics133:693–709.

Gallone B, Steensels J, Baele G, Maere S, Verstrepen KJ, Prahl T, Soriaga L,Saels V, Herrera-Malaver B, Merlevede A, et al. 2016. Domesticationand divergence of Saccharomyces cerevisiae beer yeasts. Cell166:1397–1410.

Gbelska Y, Krijger J-J, Breunig KD. 2006. Evolution of gene families: themultidrug resistance transporter genes in five related yeast species.FEMS Yeast Res. 6:345–355.

Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B, et al. 2002. Functional profiling of theSaccharomyces cerevisiae genome. Nature 418:387–391.

Giaever G, Nislow C. 2014. The yeast deletion collection: a decade offunctional genomics. Genetics 197:451–465.

Goffeau A, Barrell BG, Bussey H, Davis RW, Dujon B, Feldmann H,Galibert F, Hoheisel JD, Jacq C, Johnston M, et al. 1996. Life with6000 genes. Science 274:546–567.

Goldstein AL, McCusker JH. 1999. Three new dominant drug resistancecassettes for gene disruption in Saccharomyces cerevisiae. Yeast15:1541–1553.

Goncalves M, Pontes A, Almeida P, Barbosa R, Serra M, Libkind D,Hutzler M, Goncalves P, Sampaio JP. 2016. Distinct domestication

trajectories in top-fermenting beer yeasts and wine yeasts. Curr Biol.26:2750–2761.

He X, Qian W, Wang Z, Li Y, Zhang J. 2010. Prevalent positive epistasis inEscherichia coli and Saccharomyces cerevisiae metabolic networks.Nat Genet. 42:272–276.

Hillenmeyer ME, Fung E, Wildenhain J, Pierce SE, Hoon S, Lee W, ProctorM, St Onge RP, Tyers M, Koller D, et al. 2008. The chemical genomicportrait of yeast: uncovering a phenotype for all genes. Science320:362–365.

Hittinger CT. 2013. Saccharomyces diversity and evolution: a buddingmodel genus. Trends Genet. 29:309–317.

Ho CH, Magtanong L, Barker SL, Gresham D, Nishimura S, Natarajan P,Koh JLY, Porter J, Gray CA, Andersen RJ, et al. 2009. A molecularbarcoded yeast ORF library enables mode-of-action analysis of bio-active compounds. Nat Biotechnol. 27:369–377.

Hose J, Yong CM, Sardi M, Wang Z, Newton MA, Gasch AP. 2015.Dosage compensation can buffer copy-number variation in wildyeast. Elife 4:e05462.

Juneau K, Miranda M, Hillenmeyer ME, Nislow C, Davis RW. 2006.Introns regulate RNA and protein abundance in yeast. Genetics174:511–518.

Jungwirth H, Wendler F, Platzer B, Bergler H, Hogenauer G. 2000.Diazaborine resistance in yeast involves the efflux pumps Ycf1pand Flr1p and is enhanced by a gain-of-function allele of geneYAP1. Eur J Biochem. 267:4809–4816.

Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, EskinE. 2008. Efficient control of population structure in model organismassociation mapping. Genetics 178:1709–1723.

Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES. 2003. Sequencingand comparison of yeast species to identify genes and regulatoryelements. Nature 423:241–254.

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA,Mardis ER, Ding L, Wilson RK. 2012. VarScan 2: Somatic mutationand copy number alteration discovery in cancer by exome sequenc-ing. Genome Res. 22:568–576.

Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie2. Nat Methods 9:357–359.

Langmead B, Trapnell C, Pop M, Salzberg SL. 2009. Ultrafast andmemory-efficient alignment of short DNA sequences to the humangenome. Genome Biol. 10:R25.

Legras JL, Merdinoglu D, Cornuet JM, Karst F. 2007. Bread, beer and wine:Saccharomyces cerevisiae diversity reflects human history. Mol Ecol.16:2091–2102.

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G,Abecasis GR, Durbin R. 2009. The sequence alignment/map formatand SAMtools. Bioinformatics 25:2078–2079.

Lissina E, Young B, Urbanus ML, Guan XL, Lowenson J, Hoon S,Baryshnikova A, Riezman I, Michaut M, Riezman H, et al. 2011. Asystems biology approach reveals the role of a novel methyltransfer-ase in response to chemical stress and lipid homeostasis. PLOS Genet.7:e1002332.

Listgarten J, Lippert C, Heckerman D. 2013. FaST-LMM-Select for ad-dressing confounding from spatial structure and rare variants. NatGenet. 45:470–471.

Listgarten J, Lippert C, Kadie CM, Davidson RI, Eskin E, Heckerman D.2012. Improved linear mixed models for genome-wide associationstudies. Nat Methods 9:525–526.

Liti G, Carter DM, Moses AM, Warringer J, Parts L, James SA, Davey RP,Roberts IN, Burt A, Koufopanou V, et al. 2009. Population genomicsof domestic and wild yeasts. Nature 458:337–341.

Liti G, Louis EJ. 2012. Advances in quantitative trait analysis in yeast. PLOSGenet. 8:e1002912.

Liti G, Nguyen Ba AN, Blythe M, Muller CA, Bergstrom A, Cubillos FA,Dafhnis-Calas F, Khoshraftar S, Malla S, Mehta N, et al. 2013. Highquality de novo sequencing and assembly of the Saccharomycesarboricolus genome. BMC Genomics 14:69.

Long A, Liti G, Luptak A, Tenaillon O. 2015. Elucidating the moleculararchitecture of adaptation via evolve and resequence experiments.Nat Rev Genet. 16:567–582.

Genic Basis of Yeast Fitness Variation . doi:10.1093/molbev/msx151 MBE

2501Downloaded from https://academic.oup.com/mbe/article-abstract/34/10/2486/3797322/Deciphering-the-Genic-Basis-of-Yeast-Fitnessby University of Michigan useron 03 October 2017

Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q, Liu Y,et al. 2012. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1:18.

Martin M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17:10.

Martin MV. 1999. The use of fluconazole and itraconazole in the treat-ment of Candida albicans infections: a review. J AntimicrobChemother. 44:429–437.

McDonald JH, Kreitman M. 1991. Adaptive protein evolution at the Adhlocus in Drosophila. Nature 351:652–654.

McIlwain SJ, Peris D, Sardi M, Moskvin OV, Zhan F, Myers KS, Riley NM,Buzzell A, Parreiras LS, Ong IM, et al. 2016. Genome sequence andanalysis of a stress-tolerant, wild-derived strain of Saccharomycescerevisiae used in biofuels research. G3 6:1757–1766.

Menichetti F, Fiorio M, Tosti A, Gatti G, Bruna Pasticci M, Miletich F,Marroni M, Bassetti D, Pauluzzi S. 1996. High-dose fluconazole ther-apy for cryptococcal meningitis in patients with AIDS. Clin Infect Dis.22:838–840.

Messer PW, Petrov D. a. 2013. Frequent adaptation and the McDonald-Kreitman test. Proc Natl Acad Sci USA. 110:8615–8620.

Mortimer RK, Johnston JR. 1986. Genealogy of principal strains of theyeast genetic stock center. Genetics 113:35–43.

Notredame C, Higgins DG, Heringa J. 2000. T-Coffee: a novel method forfast and accurate multiple sequence alignment. J Mol Biol.302:205–217.

Novo M, Bigey F, Beyne E, Galeote V, Gavory F, Mallet S, Cambon B,Legras J-L, Wincker P, Casaregola S, et al. 2009. Eukaryote-to-eukary-ote gene transfer events revealed by the genome sequence of thewine yeast Saccharomyces cerevisiae EC1118. Proc Natl Acad Sci USA.106:16333–16338.

Ohya Y, Sese J, Yukawa M, Sano F, Nakatani Y, Saito TL, Saka A, Fukuda T,Ishihara S, Oka S, et al. 2005. High-dimensional and large-scale phe-notyping of yeast mutants. Proc Natl Acad Sci USA.102:19015–19020.

Parenteau J, Durand M, Veronneau S, Lacombe A-A, Morin G, Guerin V,Cecez B, Gervais-Bird J, Koh C-S, Brunelle D, et al. 2008. Deletion ofmany yeast introns reveals a minority of genes that require splicingfor function. Mol Biol Cell 19:1932–1941.

Parts L, Cubillos FA, Warringer J, Jain K, Salinas F, Bumpstead SJ, Molin M,Zia A, Simpson JT, Quail MA, et al. 2011. Revealing the geneticstructure of a trait by sequencing a population under selection.Genome Res. 21:1131–1138.

Pierce SE, Fung EL, Jaramillo DF, Chu AM, Davis RW, Nislow C, Giaever G.2006. A unique and universal molecular barcode array. Nat Methods3:601–603.

Qian W, Ma D, Xiao C, Wang Z, Zhang J. 2012a. The Genomic landscapeand evolutionary resolution of antagonistic pleiotropy in yeast. CellRep. 2:1399–1410.

Qian W, Yang J-R, Pearson NM, Maclean C, Zhang J. 2012b. Balancedcodon usage optimizes eukaryotic translational efficiency. PLoSGenet. 8:e1002603.

Raj A, Stephens M, Pritchard JK. 2014. fastSTRUCTURE: Variational in-ference of population structure in large SNP datasets. Genetics573–589.

Robinson DG, Chen W, Storey JD, Gresham D. 2014. Design and analysisof bar-seq experiments. G3 4:11–18.

Robinson MD, McCarthy DJ, Smyth GK. 2010. edgeR: a bioconductorpackage for differential expression analysis of digital gene expressiondata. Bioinformatics 26:139–140.

Rohland N, Reich D. 2012. Cost-effective, high-throughput DNA se-quencing libraries for multiplexed target capture. Genome Res.22:939–946.

Scannell DR, Zill OA, Rokas A, Payen C, Dunham MJ, Eisen MB, Rine J,Johnston M, Hittinger CT. 2011. The awesome power of yeast evo-lutionary genetics: new genome sequences and strain resources forthe Saccharomyces sensu stricto genus. G3 1:11–25.

Schacherer J, Shapiro J. a, Ruderfer DM, Kruglyak L. 2009. Comprehensivepolymorphism survey elucidates population structure ofSaccharomyces cerevisiae. Nature 458:342–345.

Sinha H, David L, Pascon RC, Clauder-Munster S, Krishnakumar S,Nguyen M, Shi G, Dean J, Davis RW, Oefner PJ, et al. 2008.Sequential elimination of major-effect contributors identifies addi-tional quantitative trait loci conditioning high-temperature growthin yeast. Genetics 180:1661–1670.

Sinha H, Nicholson BP, Steinmetz LM, McCusker JH. 2006. Complexgenetic interactions in a quantitative trait locus. PLoS Genet.2:140–147.

Skelly DA, Merrihew GE, Riffle M, Connelly CF, Kerr EO, Johansson M,Jaschob D, Graczyk B, Shulman NJ, Wakefield J, et al. 2013. Integrativephenomics reveals insight into the structure of phenotypic diversityin budding yeast. Genome Res. 23:1496–1504.

Slater GSC, Birney E. 2005. Automated generation of heuristics for bio-logical sequence comparison. BMC Bioinformatics 6:31.

Smith AM, Durbic T, Oh J, Urbanus M, Proctor M, Heisler LE, Giaever G,Nislow C. 2011. Competitive genomic screens of barcoded yeastlibraries. J Vis Exp. 54:2–8.

Smith AM, Heisler LE, Mellor J, Kaper F, Thompson MJ, Chee M, Roth FP,Giaever G, Nislow C. 2009. Quantitative phenotyping via deep bar-code sequencing. Genome Res. 19:1836–1842.

Sopko R, Huang D, Preston N, Chua G, Papp B, Kafadar K, Snyder M,Oliver SG, Cyert M, Hughes TR, et al. 2006. Mapping pathways andphenotypes by systematic gene overexpression. Mol Cell 21:319–330.

Steinmetz LM, Sinha H, Richards DR, Spiegelman JI, Oefner PJ, McCuskerJH, Davis RW. 2002. Dissecting the architecture of a quantitative traitlocus in yeast. Nature 416:326–330.

Strope PK, Skelly DA, Kozmin SG, Mahadevan G, Stone EA, MagwenePM, Dietrich FS, McCusker JH, Carolina N, Sciences B, et al. 2015. The100-genomes strains, an S. cerevisiae resource that illuminates itsnatural phenotypic and genotypic variation and emergence as anopportunistic pathogen. Genome Res. 125:762–774.

Swinnen S, Thevelein JM, Nevoigt E. 2012. Genetic mapping of quanti-tative phenotypic traits in Saccharomyces cerevisiae. FEMS Yeast Res.12:215–227.

Tajima F. 1989. Statistical method for testing the neutral mutation hy-pothesis by DNA polymorphism. Genetics 123:585–595.

Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. 2011.MEGA5: molecular evolutionary genetics analysis using maximumlikelihood, evolutionary distance, and maximum parsimony meth-ods. Mol Biol Evol. 28:2731–2739.

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ,Salzberg SL, Wold BJ, Pachter L. 2010. Transcript assembly and quan-tification by RNA-Seq reveals unannotated transcripts and isoformswitching during cell differentiation. Nat Biotechnol. 28:511–515.

Tsai IJ, Bensasson D, Burt A, Koufopanou V. 2008. Population genomicsof the wild yeast Saccharomyces paradoxus: quantifying the life cycle.Proc Natl Acad Sci USA. 105:4957–4962.

Wach A, Brachat A, Pohlmann R, Philippsen P. 1994. New heterologousmodules for classical or PCR-based gene disruptions inSaccharomyces cerevisiae. Yeast 10:1793–1808.

Wang Q-M, Liu W-Q, Liti G, Wang S-A, Bai F-Y. 2012. Surprisingly di-verged populations of Saccharomyces cerevisiae in natural environ-ments remote from human activity. Mol Ecol. 21:5404–5417.

Warringer J, Zorgo E, Cubillos F. a F, Zia A, Gjuvsland A, Simpson JT,Forsmark A, Durbin R, Omholt SW, Louis EJ, et al. 2011. Trait vari-ation in yeast is defined by population history. PLoS Genet7:e1002111.

Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, AndreB, Bangham R, Benito R, Boeke JD, Bussey H, et al. 1999. Functionalcharacterization of the S. cerevisiae genome by gene deletion andparallel analysis. Science 285:901–906.

Wolters JF, Chiu K, Fiumera HL. 2015. Population structure of mitochon-drial genomes in Saccharomyces cerevisiae. BMC Genomics 16:451.

Maclean et al. . doi:10.1093/molbev/msx151 MBE

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