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1 Recombining your way out of trouble: The genetic architecture of

2 fitness under environmental stress

3 Zebin Zhang1*, Devin P. Bendixsen1*, Thijs Janzen2,3, Arne W. Nolte2,3, Duncan

4 Greig2,4, Rike Stelkens1,2

5

6

7 *These authors contributed equally to the study.

8

9 1Division of Population , Department of Zoology, Stockholm University,

10 Svante Arrheniusväg 18 B, 106 91 Stockholm, Sweden

11 2Max Planck Institute for Evolutionary , August-Thienemann Straße 2, 24306

12 Plön, Germany

13 3Institute of Biology and Environmental Sciences, University of Oldenburg, Carl-von-

14 Ossietzky-Straße 9-11, 26111 Oldenburg, Germany

15 4The Galton Laboratory, Department of Genetics, Evolution, and Environment,

16 University College London, Gower Street, London WC1E 6BT, United Kingdom

17

18 Corresponding author: Rike Stelkens, Department of Zoology, Stockholm University,

19 Svante Arrheniusväg 18 B, 106 91 Stockholm, Sweden, tel: +46 (0) 816 4223, email:

20 [email protected]

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21 Abstract

22 Hybridization between species is a fundamental evolutionary force that can both

23 promote and delay . There is a deficit in our understanding of the genetic

24 basis of hybrid fitness, especially in non-domesticated organisms. We also know little

25 about how hybrid fitness changes as a function of environmental stress. Here, we

26 made genetically variable F2 hybrid populations from two divergent Saccharomyces

27 yeast species, exposed populations to ten toxins, and sequenced the most resilient

28 hybrids on low coverage using ddRADseq. We expected to find strong negative

29 and in the hybrid genomes. We investigated three

30 aspects of hybridness: 1) hybridity, 2) interspecific heterozygosity, and 3) epistasis

31 (positive or negative associations between non-homologous ). Linear

32 mixed effect models revealed strong -by-environment interactions with

33 many chromosomes and chromosomal interactions showing species-biased content

34 depending on the environment. Against our predictions, we found extensive selection

35 against heterozygosity such that homozygous allelic combinations from the same

36 species were strongly overrepresented in an otherwise hybrid genomic background.

37 We also observed multiple cases of positive epistasis between chromosomes from

38 opposite species, confirmed by epistasis- and selection-free simulations, which is

39 surprising given the large divergence of the parental species (~15% genome-wide).

40 Together, these results suggest that stress-resilient hybrid genomes can be

41 assembled from the best features of both parents, without paying high costs of

42 negative epistasis across large evolutionary distances. Our findings illustrate the

43 importance of measuring genetic trait architecture in an environmental context when

44 determining the evolutionary potential of hybrid populations.

45 Keywords: Saccharomyces, hybridization, fitness, environmental stress, ddRADseq,

46 heterozygosity, epistasis, genome evolution

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47 Introduction

48 Populations exposed to flow, introgression or hybridization contain vast

49 amounts of , sometimes producing phenotypes with more extreme

50 than found in the parent populations (Shahid, et al. 2008; Stelkens and

51 Seehausen 2009; Pritchard, et al. 2013; Stelkens, Brockhurst, Hurst, Miller, et al.

52 2014; Holzman and Hulsey 2017). The average fitness of hybrid crosses is usually

53 lower than that of non-hybrid crosses due to interspecific incompatibilities or other

54 negative effects, e.g. the break-up and loss of co-adapted beneficial gene complexes

55 for local adaptation (Coyne and Orr 2004). This applies especially to the hybrid

56 offspring of genetically divergent lineages. However, some hybrids show high fitness

57 (Rieseberg, et al. 1999; Dittrich-Reed and Fitzpatrick 2012), which is exploited in

58 agricultural breeding to improve the yield, taste or other desirable traits of cultivars

59 (e.g. Marullo, et al. 2006; Kuczyńska, et al. 2007; Shivaprasad, et al. 2012; Koide, et

60 al. 2019). High hybrid fitness can also be relevant for macroevolutionary dynamics

61 and the generation of biodiversity, when ecologically divergent hybrid phenotypes

62 form their own evolutionary lineage and become reproductively isolated from the

63 parents (Anderson and Stebbins 1954; Lewontin and Birch 1966; Rieseberg, et al.

64 2003; Seehausen 2004; Arnold 2006; Mallet 2007; Nolte and Tautz 2010; Abbott, et

65 al. 2013; Schumer, et al. 2014).

66 Experimental evolution with interspecific hybrids of the budding yeast

67 Sacchromyces spp. has demonstrated that hybrids can colonize new ecological

68 niches (Stelkens, Brockhurst, Hurst and Greig 2014; Stelkens, Brockhurst, Hurst,

69 Miller, et al. 2014), and gain resistance to stress (Greig, et al. 2002; Piotrowski, et al.

70 2012; Dunn, et al. 2013). However, the genetic mechanisms allowing some hybrids

71 to express high fitness phenotypes in the face of negative epistasis between

72 divergent parental genomes are largely unknown, especially in non-domesticated

73 organisms. Also, we know very little about the impact of stressful and deteriorating

74 environmental conditions on the evolutionary potential of hybrid populations, although

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75 this is becoming increasingly relevant in the face of global environmental change. So

76 far empirical studies of hybrid fitness in an environmental context are scant and the

77 results are mixed. Some found environmental stress to intensify the negative effects

78 of hybridization (Koevoets, et al. 2012; Barreto and Burton 2013), others found hybrid

79 fitness to increase with stress (Edmands and Deimler 2004; Willett 2012; Hwang, et

80 al. 2016b); again others detected no effects of stress on hybrid fitness (Armbruster,

81 et al. 1999).

82 Here, we made genetically highly variable F2 hybrids by crossing two

83 divergent species of Saccharomyces (S. cerevisiae and S. paradoxus). These

84 species have well-sequenced, well-assembled genomes that differ at ~15% of

85 nucleotides genome-wide (Liti, et al. 2009). Due to their large divergence these

86 species produce only ~1% viable F2 hybrid offspring (Hunter, et al. 1996; Boynton, et

87 al. 2018; Rogers, et al. 2018). Because of this, we expected to find strong negative

88 epistasis between genetic material from opposite species and, potentially,

89 heterozygote advantage in the genomes of viable hybrids. We exposed 240

90 populations of these viable F2 hybrids to ten stressful environments containing high

91 concentrations of different toxins (e.g. caffeine, ethanol, lithium acetate; Table S1). At

92 the end of the growth period, we sequenced the genomes of 240 hybrid survivors at

93 low coverage using double digest, restriction-site associated DNA (ddRAD) markers

94 and mapped hybrid genomes to both reference genomes. To identify the genetic and

95 environmental factors shaping hybrid genomes in our experiment, we measured

96 three aspects of ‘hybridness’; 1) hybridity (proportion of hybrid genome mapping to

97 one or other parent species); 2) interspecific heterozygosity (homologous

98 chromosomes from opposite species), and 3) epistasis (positive or negative

99 associations between non-homologous chromosomes from opposite species).

100 Hybridity is a continuous measure, e.g. the proportion of genomic material

101 inherited from one over the other parental species (Gompert and Buerkle 2016),

102 often referred to as hybrid index (Barton and Gale 1993; Buerkle 2005) or admixture

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103 proportion (Pritchard, et al. 2000; Falush, et al. 2003). Hybridity is useful to place

104 hybrid individuals on a single axis ranging from zero to one with pure parental

105 individuals at opposite ends. However, this simple measure can mask potentially

106 important fitness effects of individual loci (or chromosomes) deviating from the

107 average hybridity of the genome. For instance, (the reciprocal masking of

108 deleterious at multiple loci; Bruce 1910)) and overdominance (the intrinsic

109 benefit of being heterozygous for at least one ) can produce hybrids with high

110 fitness. This is known as ‘interspecific heterozygosity’ or ‘inter-population ancestry’

111 (Barton 2000; Fitzpatrick 2012; Lindtke, et al. 2012; Gompert and Buerkle 2013). As

112 an example, while every in a diploid F1 hybrid is heterozygous,

113 maximizing within-locus hybridity, the genome still carries a full haploid set of both

114 parental chromosomes. At the same time, a diploid F2 or higher generation hybrid

115 can, theoretically, be composed of fully homozygous chromosomes (i.e. homologous

116 chromosomes are from the same species), which minimizes within-locus hybridity.

117 But this F2 hybrid may contain half the chromosomal set from either parent,

118 maximizing between-locus hybridity. Because these types of hybrids would be

119 indistinguishable with a single hybrid index (which would be 0.5 in both examples),

120 we also measured interspecific heterozygosity, i.e. chromosomes in the F2 hybrid

121 genome carrying interspecific, homologous combinations.

122 Epistasis is caused by interactions between alleles from at least two different

123 loci increasing or decreasing fitness more than the sum of the individual contributions

124 of these loci. Negative epistasis is the core element of the Dobzhansky-Muller model

125 of genetic incompatibilities often recruited to explain the evolution of reproductive

126 isolation and outbreeding depression between biological species, with increasing

127 negative impact the more divergent the parental genomes are (Dobzhansky 1936;

128 Müller 1942; Lynch 1991). Given the large nucleotide divergence between the

129 parental lineages used here (~15% genome-wide), we expected negative epistasis to

130 be prominent in F2 hybrids, determining variation in fitness to a large extent

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131 (Cubillos, et al. 2011; Shapira, et al. 2014). We measured epistasis by testing for

132 significant associations (presence and absence) between non-homologous

133 chromosomes from different species in F2 hybrid yeast genomes, and compared our

134 experimental data to computer-simulated data, assuming free segregation of

135 chromosomes, no epistasis and no selection in a theoretical F2 hybrid population.

136 We also predicted species biases on individual chromosomes and

137 associations between chromosomes, and predicted species biases to be determined

138 by environmental changes (Kvitek and Sherlock 2011; de Vos, et al. 2013). For

139 instance, Jaffe et al. (2019) showed that adding more environmental conditions

140 tripled the number of genetic interactions detected in fitness assays between double

141 mutants of yeast (Jaffe, et al. 2019). Filteau et al. (2015) found that the course of

142 compensatory evolution rescuing yeast populations from the negative fitness effects

143 of deleterious was strongly contingent on environmental context (different

144 carbon sources) (Filteau, et al. 2015). Similarly, Lee et al., dissecting the genetic

145 basis of a yeast colony phenotype, found that environmental stress affected the

146 impact of epistasis, additivity, and genotype-environment interactions (Lee, et al.

147 2019). We tested for species biases and genotype-by-environment interactions using

148 linear mixed effect models.

149

150 Results

151 We sequenced a total of 240 F2 hybrid strains, of which 184 were mostly diploid and

152 53 were mostly haploid (three genomes were discarded due to low read quality).

153 Thus, the majority of spores germinated and mated in the 96-well plates, forming

154 diploid F2 hybrids homozygous for both drug resistance markers cyh2r and can1r.

155 The haploid detected by sequencing were unmated F2 (i.e. F1 spores),

156 and hemizygous for cyh2r and can1r. Occasional chromosomal copy number

157 variation within genomes was apparent, but variation in read depth along

158 chromosomes did not allow for a systematic quantification of disomies (or higher -

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159 somies). Because we saw no significant differences between F2 samples isolated

160 from high concentration of a toxin and low concentration in any of the tests, we

161 proceeded by pooling all samples for analysis.

162

163 Interspecific heterozygosity

164 Assuming no selection and free segregation of chromosomes, interspecific

165 heterozygosity (i.e. having homologous chromosomes from both species) was lower

166 than expected in the 187 diploid F2 genomes across all toxins (mean heterozygosity

167 = 0.44 median = 0.36; Figure 1A). On average, only 7 of the 16 chromosomes per

168 genome carried a heterospecific combination. In total, there were almost 1.3 times

169 more homozygous chromosomes (n = 1450; 57%) than heterozygous chromosomes

170 (n = 1126; 43%). Environments differed substantially in the proportions of homo- and

171 heterozygous chromosomes (Figure 1B). While in some environments, F2 hybrids

172 were mainly homozygous (e.g. zinc sulfate), other environments promoted the growth

173 of mainly heterozygote genotypes (e.g. NaCl). This was confirmed using simulations.

174 Five stress environments (zinc sulfate, citric acid, ethanol, salicylic acid and caffeine)

175 produced mean interspecific heterozygosities below the range expected based on

176 random segregation simulations (Figure S1). Seven environments produced mean

177 homozygosities above the expected range: Three environments (zinc sulfate, citric

178 acid and ethanol) produced F2 genomes more homozygous than the simulated range

179 of expected homozygosity for S. cerevisiae predicted. Four environments (caffeine,

180 citric acid, salicylic acid and zinc sulfate) produced F2 genomes with higher

181 homozygosity than the simulated range of expected homozygosity for S. paradoxus.

182 Testing for variation in zygosity between chromosomes, across all

183 environments, we found three chromosomes with a significantly larger proportion of

184 S. cerevisiae homozygotes (chromosomes 4, 5, 14), and three chromosomes with a

185 significantly larger proportion of S. paradoxus homozygotes (chromosomes 6, 7, 10;

186 Figure 1C). Note that chromosomes 5 and 7 carried resistance , which, as

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187 predicted, selected them to be homozygous for S. cerevisiae and S. paradoxus,

188 respectively (chromosome 5 also carried some S. paradoxus content due to

189 recombination at one end of the chromosome). Three chromosomes were more often

190 heterozygous than expected by chance (chromosomes 2, 3, 12).

191 Using a linear mixed-effect model to understand what causes variation in

192 zygosity between environments, chromosomes and genomes, we found that the

193 interaction between chromosome ID and environment best predicted zygosity (Table

194 S2). Thus, which chromosome was homozygous, and which chromosome was

195 heterozygous, was largely determined by the stress environment from which it was

196 sampled.

197

198

199 200 Figure 1 Homo-/heterozygosity of diploid F2 hybrid genomes 201 (A) Mean zygosity of 187 diploid F2 hybrids across all chromosomes (except 202 chromosome 5 and 7) and across all environments. Dashed horizontal line (at 0.5) 203 shows expected heterozygosity without selection and free segregation. Black lines in 204 boxes are medians and large diamonds indicate means. Each black dot represents a 205 genome. Boxes are interquartile ranges (IQR). Whiskers are 1.5 x IQR (B) Mean F2 206 hybrid zygosity per environment. (C) Mean F2 hybrid zygosity per chromosome. 207 Chromosome 5 and 7 harbour drug markers selecting for S. cerevisiae and S. 208 paradoxus chromosomes, respectively. Each dot represents one of 10 environments. 209 Asterisks indicate significant differences in proportions of S. cerevisiae vs. S. 210 paradoxus homozygotes in Wilcoxon signed-rank tests (p = *0.05, **0.01, ***0.001). 211

212

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213 Genome-wide hybridity

214 Average genome-wide hybridity (defined to be % markers inherited from S.

215 cerevisiae) was 0.52 ± 0.05 (µ±SD) across all chromosomes and all environments

216 (Figure S2A). Thus, genomic contributions were roughly balanced between the two

217 parental species with a slight overrepresentation of S. cerevisiae. The most biased

218 genome towards S. cerevisiae was 0.68 and the most biased towards S. paradoxus

219 was 0.35. Average hybridity per toxin ranged from 0.49 ± 0.07 in salicylic acid

220 (paradoxus-biased) to 0.55 ± 0.07 (cerevisiae-biased) in Nipagin, but no significant

221 differences were found between toxins (Figure S2B). The distributions and means of

222 genome-wide hybridity observed in each environment were all within the range

223 expected from simulations based on random chromosome segregation (Figure S3).

224 Chr. 5 and 7 were excluded from these analyses because they carried antibiotic

225 resistance markers selecting for S. cerevisiae and S. paradoxus hemi- or

226 homozygosity, respectively.

227

228 Chromosome hybridity

229 A closer examination of hybridity (defined to be % markers inherited from S.

230 cerevisiae) at the chromosomal level revealed species biases in inheritance patterns

231 (Figure 2A). For most chromosomes the average chromosome hybridity across all

232 environments was approximately 0.5, suggesting equal inheritance from S.

233 cerevisiae and S. paradoxus. As expected chromosome 5 was inherited primarily

234 from S. cerevisiae and chromosome 7 was inherited primarily from S. paradoxus

235 because they carried species-specific drug markers. Interestingly, chromosome 4

236 and 14 were also inherited predominantly from S. cerevisiae (0.77 and 0.88

237 respectively). Nine of the 16 chromosomes exhibited varying levels of hybridity

238 depending on environment (ANOVA, p < 0.05), with six showing significant variation

239 (Figure 2B, p < 0.001). For some chromosomes, the hybridity variation resulting from

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240 environment did not change the species bias (chromosomes 7, 14). However, some

241 chromosomes shifted from unbiased (~ 0.5 hybridity) to biased for S. paradoxus in

242 some environments (chromosomes 10, 15). Two chromosomes exhibited

243 environment-dependent species bias (chromosomes 12, 13). In some environments

244 these chromosomes were biased towards S. cerevisiae (> 0.65%) and in other

245 environments biased towards S. paradoxus (< 0.35%). Using a linear mixed-effect

246 model we found that the interaction between chromosome ID and environment and

247 the interaction between chromosome ID and (haploid or diploid) best predicted

248 the hybridity of the chromosome (Table S3). The optimal model did not change if

249 data from chromosome 5 and 7 were excluded.

250

251

252 Figure 2 Chromosome hybridity of 237 haploid and diploid F2 genomes 253 (A) Chromosome hybridity for each chromosome across all environments. 254 Chromosome hybridity is measured as the percent chromosome mapping to S. 255 cerevisiae. Boxplots as in Figure 1, but colored according to where the median falls.

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256 Dashed line (0.5) indicates equal amounts of the chromosome mapping to S. 257 cerevisiae. Species biases on chromosome 5 (~65% from S. cerevisiae) and 7 (~5% 258 from S. cerevisiae) are by design and result from alternative recessive drug markers. 259 (B) Chromosome hybridity for each chromosome within environments. Numbers in 260 coloured boxes indicate average chromosome hybridity across 24 F2 diploid 261 genomes. Asterisks indicate significant differences in average chromosome hybridity 262 across environments (p = *0.05, **0.01, ***0.001). 263

264 Chromosome hybridity interactions

265 Testing for chromosomal hybridity interactions within a given F2 genome revealed

266 strong environment-dependent effects. A large difference in chromosome hybridity

267 for each chromosome combination, defined as delta chromosome hybridity (DCH),

268 indicated that two chromosomes mapped primarily to opposing species within a given

269 F2 genome. This could potentially result from negative epistatic interactions within

270 species (i.e. chromosome incompatibility), from positive epistatic interactions

271 between species or a combination of the two. Some chromosomes maintained high

272 DCH suggesting that they were preferably from opposing species across all

273 environments (Figure 3A, chr7 x chr4, chr7 x chr14, chr7 x chr5). Chromosome 7 and

274 5 were designed to come from opposing species and were therefore expected to

275 have high DCH. However, the remaining high DCH interactions with chromosome 7

276 suggest potential chromosome incompatibilities or strong positive epistatic

277 interactions between species. Chromosome 7 was designed to be inherited from S.

278 paradoxus and potentially resulting from this, chromosome 4 and chromosome 14

279 were primarily inherited from S. cerevisiae. Analyzing the DCH in each stress

280 environment independently showed that different environments exhibited varying

281 levels and distributions of DCH (Figure 3B). Two environments (zinc sulfate, salicylic

282 acid) exhibited higher mean DCH than the range expected from simulations based on

283 no chromosome interactions (Figure 4). Four environments (NaCl, hydrogen

284 peroxide, lithium acetate, DMSO) exhibited lower mean DCH than the simulated

285 expected range.

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286 Analyzing directional correlations between hybridities of chromosomes within

287 a genome revealed similar environment-dependent interactions (Figure 5). Significant

288 positive correlations suggest positive epistatic interactions within species, meaning

289 that as chromosome hybridity in one chromosome shifted towards one species (1 =

290 S. cerevisiae, 0 = S. paradoxus), the chromosome hybridity in the linked

291 chromosome also shifted towards the same species (Fig. 5A). Inversely, negative

292 correlations suggested negative epistatic interactions within species. A lack of

293 correlation suggested no significant epistatic interaction. For all environments

294 combined and individually, the distribution of correlations was approximately normal

295 with a mean near zero. There was a range of significant correlations (p < 0.01) found

296 across environments ranging in numbers from 0 in salicylic acid and citric acid to 11

297 in NaCl (Figure 5B). The distributions and means for each environment all fell within

298 the range that was expected from simulations based on no chromosome interactions

299 (Figure S4). However, although the distributions and means were within the range

300 simulated, three environments (caffeine, lithium acetate and NaCl) exhibited more

301 significant correlations (p < 0.01) than expected (Figure S5). The NaCl environment

302 resulted in a total of 11 significant hybridity correlations (8 positive, 3 negative),

303 whereas 10,000 simulations never encountered more than 7 correlations by chance.

304

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305 306 307 Figure 3 Interactions of chromosome hybridities altered by environment 308 (A) Average change in chromosome hybridity (percent chromosome mapping to S. 309 cerevisiae) between chromosomes across all environments. Inset depicts the 310 distribution of delta chromosome hybridity for all chromosome interactions. Delta 311 chromosome hybridity is determined for each chromosome interaction by taking the 312 difference between the hybridity measurements for each chromosome within an F2 313 genome (n genomes = 237). These values are then averaged across all diploid F2 314 genomes, and medians and means are reported and colored accordingly. A large 315 delta chromosome hybridity (green) suggests that the chromosomes map primarily to 316 opposing species. A small delta chromosome hybridity (brown) suggests that the 317 chromosomes have similar levels of hybridity. These chromosomes may come from 318 primarily the same species or at least have similar hybridity proportions. (B) Average 319 change in chromosome hybridity between chromosomes for each environment. 320 Calculations are performed the same as panel A for each environment independently 321 (n genomes ~ 24).

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322

323 Figure 4 Delta chromosome hybridity (DCH) of simulated chromosomes 324 (A) The distribution of DCH in a simulated environment resulting from simulated 325 chromosomes based on no chromosomal interactions. Each environment consists of 326 24 genomes, each consisting of 16 simulated chromosomes. Each simulated 327 environment was repeated 10,000 times. (B) The distribution of DCH found in our 328 experimental environments. Distributions are colored according to their mean DCH. 329 (C) Mean DCH of our experimental environments as compared to the range of mean 330 DCH found in simulations (grey-black lines). 331

332 Genotype x Environment Interactions and Gene Ontology

333 Sampling all F2 hybrid genomes, a total of 315 10kb bins fixed for either S.

334 cerevisiae or S. paradoxus were detected (406 bins including chromosome 5 and 7;

335 Figure S6A). We found large variation between environments ranging from 36

336 regions in salicylic acid to 372 regions in NaCl. Seventy-eight of these regions were

337 environment-specific, i.e. they were inherited from either S. cerevisiae or S.

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338 paradoxus across all 24 hybrid genomes per environment, but only found in one

339 environment.

340 We identified 1884 genes that were either located in or overlapped with the

341 315 fixed regions, ranging from 51 genes in salicylic acid to 1533 genes in NaCl

342 (Figure S6C). The large differences between environments in the number of fixed

343 alleles suggest that some stress conditions require more complex, quantitative

344 adaptations with a polygenic basis, which is well-known to be the case for yeast adapting

345 to salt for instance (Cubillos, et al. 2011; Dhar, et al. 2011).

346 Gene annotation analysis showed that most of the 1884 genes are involved in

347 transport and pathways related to fungal cell wall organization (Figure S6B). Across

348 environments, we found almost three times more genes fixed for the S. cerevisiae

349 (n = 1373) than fixed for the S. paradoxus allele (n = 511). Only the F2 hybrid

350 genomes isolated from salicylic acid showed more genes fixed for S. paradoxus (46

351 of 51 genes in total).

352 Only one region was consistently inherited from S. cerevisiae across all

353 environments (chromosome 4: 970 000 - 980 000 bp, harbouring 5 genes), and one

354 from S. paradoxus across all environments (chromosome 15: 10 000 - 20 000 bp,

355 harbouring 7 genes). Among these are two general stress response genes, HSP78

356 and PAU20. HSP78 on chromosome 4 is associated with heat stress and

357 mitochondrial genome maintenance (Leonhardt, et al. 1993; von Janowsky, et al.

358 2006). PAU20 on chromosome 15 is associated with proteome stress and is

359 upregulated during wine fermentation (Rossignol, et al. 2003; Marks, et al. 2008; Luo

360 and van Vuuren 2009). It is surprising that the S. paradoxus allele of PAU20 became

361 fixed across all environments as we normally expect S. cerevisiae to be better adapted to

362 fermentation environments.

363 We did not identify fixations of alternative alleles at the same locus in different

364 environments (e.g. a bin that was consistently inherited from S. cerevisiae across all 24

365 hybrid genomes in ethanol but from S. paradoxus in salicylic acid).

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366

367 368 369 Figure 5 Epistatic interactions between chromosome hybridity across 370 environments 371 (A) Pearson correlation coefficient (r) of chromosome hybridity (percent chromosome 372 mapping to S. cerevisiae) between chromosomes across all environments (n 373 genomes = 237). Inset depicts the distribution of Pearson correlation coefficients for 374 all chromosome interactions. Positive correlations are shown in green and negative 375 correlations are shown in purple. Statistically significant correlations (p < 0.01) are 376 highlighted in black. Examples of strong correlations are shown for the interactions 377 between chromosome 13 and 11 (positive) and chromosome 13 and 1 (negative) in 378 the Caffeine environment. (B) Pearson correlation between chromosome hybridity 379 within each environment. Calculations are performed as in panel B but for each 380 environment independently (n ~ 24 genomes). Environments are sorted from top left 381 to bottom right according to the number of significant (p < 0.01) correlations found. 382

383 Discussion

384 Hybrid fitness varies among genotypes, generations, and environments (e.g. Nolte

385 and Tautz 2010; Arnold, et al. 2012; Stelkens, Brockhurst, Hurst, Miller, et al. 2014;

386 Hwang, et al. 2016b) but the genetic basis of hybrid fitness is usually unknown,

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387 especially in non-domesticated organisms (but see Rieseberg, et al. 1999; Payseur

388 and Rieseberg 2016). In addition, the interaction between the environment and the

389 targets of selection in hybrid genomes remains almost entirely unexplored (but see

390 Shahid, et al. 2008; Hwang, et al. 2016a). Whether or not the variation contained in

391 hybrid populations contributes to adaptive evolution crucially depends on the type of

392 genetic mechanism underlying fitness. Under heterozygote advantage, for instance,

393 the most fit genotype cannot breed true in sexual, diploid populations because

394 segregation will inevitably break up beneficial allelic combinations (Buerkle and

395 Rieseberg 2008). But if the most fit genotype is homozygous for alleles derived from

396 each lineage at different loci, such recombinant homozygotes can breed true, may

397 become fixed through drift or selection, and potentially establish new lineages

398 (Fitzpatrick and Shaffer 2007).

399 Here, we describe the composition of 237 stress-resistant F2 hybrid

400 genomes, made from two divergent Saccharomyces yeast species. We measured

401 variance in overall hybridity, interspecific heterozygosity, and epistasis found

402 between and within hybrid genomes. We found that the composition of hybrid

403 genomes was strongly contingent on environmental context. Genomes from different

404 environments varied in every aspect of hybridness measured. First, genomes from

405 different environments differed in the proportion of interspecific heterozygosity. While

406 some environments clearly selected for more homozygous genomes (e.g. zinc

407 sulfate), others selected for more heterozygous genomes (e.g. NaCl; Figure 1B).

408 Second, individual chromosomes exhibited strong species biases depending on the

409 type of stress they were exposed to (Figure 2B). This is presumably because genes

410 important for resistance to the specific toxin are located on these chromosomes, and

411 alleles from parental species differ in how well they tolerate this toxin (but note that

412 we did not observe fixation of opposite species alleles in different environments;

413 Figure S6). Third, we found evidence for environment-dependent, non-homologous

414 chromosomal associations (Figure 3). Positive same-species and to our surprise,

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415 also positive opposite-species chromosome associations occurred, depending in

416 frequency and type on the environment (Figure 5). Computer simulations in a

417 selection- and epistasis-free space with independently segregating chromosomes

418 (Figure 4, S3, S4, S5) produced a smaller number of significant associations

419 between chromosomes in hybrid genomes, suggesting that some heterospecific

420 chromosomal combinations in experimental hybrid genomes were indeed under

421 positive epistatic selection.

422 The presence of beneficial interspecific interactions between chromosomes

423 from species with such large evolutionary divergence (~15%) is surprising. At this

424 genetic distance, which is roughly three times larger than the distance between

425 humans and chimpanzees, one would expect hybrids to be ridden with Dobzhansky-

426 Muller incompatibilities (negative epistasis) and other negative fitness effects of

427 species divergence, such as the missegregation of chromosomes due to

428 antirecombination (Hunter, et al. 1996; Greig, et al. 2003; Rogers, et al. 2018). By

429 virtue of our experimental design we only sampled from an already viable subset of

430 F2 hybrids. Still, even in this subset this we would expect selection to favour same-

431 species chromosomal combinations (in the resolution possible given limited rounds of

432 segregation and recombination in the F2 generation).

433 Interestingly, when screening for recessive incompatibilities in the same hybrid

434 cross, a previous study found that replacing chromosomes 4, 13, and 14 in S. cerevisiae

435 with homologous chromosomes from S. paradoxus did not yield any viable haploids

436 (Greig 2007). In our experiment, focusing here on the NaCl environment because it

437 revealed the most significant epistatic interactions, the same three chromosomes (4, 13,

438 14) were involved in five of eight positive same-species interactions, suggesting that

439 different-species combinations with any of these three chromosomes may indeed cause

440 low fitness in hybrid genomes, and are selected against (Figure 5B). In addition, we found

441 the three positive opposite-species associations in NaCl involving five chromosomes (6,

442 8, 9, 10, 16). Of these, the previous study found four chromosomes (6, 8, 9, 10) not to be

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443 problematic to transfer into a different species background (there was no suitable

444 auxotrophic marker for chromosome 16, so this was a technical limitation, not an

445 incompatibility), suggesting positive heterospecific epistasis, or at least an absence of

446 incompatibilities on these chromosomes.

447 Individual chromosomes also differed in in their level of interspecific

448 heterozygosity independently of the environment. For instance, chromosomes 4, 6,

449 and 14 were more beneficial to fitness as homozygotes, while chromosomes 2, 3 and

450 12 were more beneficial as heterozygotes (Figure 1C). Overall however, interspecific

451 heterozygosity was unexpectedly low across all hybrid genomes (Figure S1),

452 suggesting that same-species allelic combinations (homozygosity) in an otherwise

453 hybrid genomic background can provide high fitness. These results speak against

454 strong effects of dominance or overdominance to hybrid fitness and are more

455 consistent with selection against heterozygotes (), which has been

456 shown in Saccharomyces (Laiba, et al. 2016), natural Populus (Lindtke, et al. 2012)

457 and Helianthus hybrid populations (Lai, et al. 2005). Yet, dominance

458 complementation of recessive alleles and overdominant interactions within loci are

459 often reported from laboratory crosses of Saccharomyces where heterozygotes are

460 fitter than both homozygotes due to simple or reciprocal complementation of one or

461 several recessive deleterious mutations (Zörgö, et al. 2012; Plech, et al. 2014;

462 Shapira, et al. 2014; Blein-Nicolas, et al. 2015; Laiba, et al. 2016). In some cases,

463 this inconsistency with our results may be explained by the much smaller genomic

464 divergence (usually <1%) between parental yeast lineages used in these studies

465 (mostly all S. cerevisiae strains). In agreement with our results, a study on

466 in the same interspecific yeast cross (S. cerevisiae x S. paradoxus) suggested that

467 heterosis in hybrids was likely a result of both dominance complementation of

468 recessive deleterious alleles, and additional overdominant or epistatic effects.

469 The excess of homozygosity in our experiment is consistent with many if not

470 most high fitness alleles being recessive and only becoming expressed in the

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471 homozygous state. Alternatively, selection on some aspects of fitness in the haploid

472 phase (during sporulation and germination) may have caused strains with the same

473 beneficial combinations of parental chromosomes to mate, resulting in homozygous

474 diploids. For instance, strains may be able to maximize their fitness by germinating

475 early (Miller and Greig 2014), which could result in a process similar to assortative

476 mating.

477 We acknowledge that the large size of the populations we used here, the

478 facultative asexual reproduction and the ability of yeast to self-fertilize (hybrids do not

479 rely on backcrossing), can help restore fitness quickly after outbreeding depression in

480 the F2 hybrid generation. Together, this can catalyze the propagation of hybrid

481 genotypes even with small and temporary fitness advantages, which is not a likely

482 outcome in small populations of purely sexual species. It is also possible that some

483 of the hybrids in our experiment were aneuploid, i.e. they lost or gained one or more

484 chromosomes. Aneuploidy is a common by-product of chromosomal mis-segregation

485 in the F1 hybrid meiosis of interspecific yeast crosses (Hunter, et al. 1996), and gene

486 dosage effects ensuing from higher copy numbers may have helped some hybrids in

487 our experiment survive stress. Aneuploidy has been associated with stress

488 resistance in yeast and other fungi (Selmecki, et al. 2009; Pavelka, et al. 2010;

489 Kwon-Chung and Chang 2012), and has been suggested to serve as transient

490 adaptation mechanism (Chang, et al. 2013; Hose, et al. 2015; Smukowski Heil, et al.

491 2016).

492 Hybridization mostly occurs at the margins of species ranges where

493 conditions are more extreme than in the center of their distribution, i.e. in more

494 stressful environments. Hybridization also occurs more frequently in human- or

495 otherwise perturbed habitats, where geographic and ecological species barriers are

496 lost (King, et al. 2015; Arnold 2016; Gompert and Buerkle 2016; McFarlane and

497 Pemberton 2019). Thus, the circumstances leading to hybridization often coincide

498 with times of increased environmental stress, which in turn creates ecological

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499 opportunity. The large number of significant chromosome-by-environment

500 interactions we found in our hybrid populations showcases the genetic variation

501 characteristic of hybrid swarms. This can facilitate their functional diversification and,

502 potentially, the colonization of novel and challenging environments as shown for

503 instance in Helianthus sunflowers and African cichlid fish (Rieseberg, et al. 2003;

504 Seehausen 2004; Nolte, et al. 2005; Abbott, et al. 2013). But in a genome that is a

505 patchwork of heterozygous and homozygous regions, knowing the type of genetic

506 mechanism responsible for increased fitness is essential for any predictions

507 regarding the role of hybridization in adaptive evolution. In addition, our data

508 suggests that each environment selects for different parental alleles and for different

509 epistatic interactions.

510 In conclusion, both environmental and genetic contingencies of hybrid fitness

511 limit our ability to predict the evolutionary and ecological outcomes of hybridization

512 (Gompert and Buerkle 2016). For instance, hybrids could outcompete and drive a

513 parent population to extinction in one locality, but the same hybrids may have inferior

514 fitness in a different habitat, posing no threat at all to the parents. The results of our

515 study show that in order to capture the risks and benefits of genetic exchange

516 between populations and species, it is important to measure multiple dimensions of

517 hybridity and measure hybrid fitness in multiple environmental contexts.

518

519 Materials and Methods

520 Parent and F1 hybrid strains

521 We chose two genetically tractable laboratory strains as parents: S. cerevisiae

522 haploid strain YDP907 (MATα, ura3::KanMX, can1r), isogenic with strain background

523 S288c, was crossed to S. paradoxus strain YDP728 (MATa, ura3::KanMX, cyh2r),

524 which is isogenic with strain background N17. This produced a diploid F1 hybrid

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525 (MATa/@, ura3::KanMX, cyh2r, can1r), which was purified by streaking and then

526 stored frozen at -80°C in 20% glycerol stock.

527

528 F2 hybrids

529 To make F2 hybrids, we grew a 5ml culture of the F1 hybrid in YEPD (1% yeast

530 extract, 2% peptone, 2% dextrose) for 24h at 30°C, and then transferred 200µl of this

531 to 50ml of KAC sporulation medium (1% potassium acetate, 0.1% yeast extract,

532 0.05% glucose, 2% agar) which was shaken at room temperature for 5 days to

533 induce meiosis and sporulation. Sporulation was confirmed under the microscope.

534 50µl of the spore culture, in serial dilutions and using three replicates per dilution,

535 were then plated onto arginine drop out agar plates supplemented with the drugs

536 canavanine (60mg/L) and cycloheximide (3.33mg/L). These drugs kill the fully

537 heterozygous F1 hybrids because the resistance alleles cyh2r and can1r are

538 recessive. But those meiotic spores (produced by the F1 hybrid) that contain both

539 resistance alleles can, if viable, germinate and form colonies. This method ensured

540 that only F2 hybrids were sampled and sequenced.

541

542 Environments and stress

543 50µl of spore culture (containing 25.5 double drug-resistant viable spores on average

544 confirmed by streaking out on double-drug medium) were used as the founding

545 population for inoculation and growth on flat-bottomed, 96-well cell culture plates.

546 Wells contained liquid minimal medium plus uracil, cycloheximide and canavanine

547 (0.67% yeast nitrogen base without amino acids, 2% glucose, 2% agar, 0.003%

548 uracil, 60 mg/L canavanine, 3.33mg/L cycloheximide), allowing for the germination,

549 mating, and growth of the double drug-resistant F2 progeny. This was supplemented

550 with a range of concentrations of ten toxins (one at a time): salicylic acid (C7H6O3),

551 caffeine (C8H10N4O2), ethanol (C2H6O), zinc sulfate (ZnSO4), hydrogen peroxide

552 (H2O2), methyl paraben (“Nipagin”; CH3(C6H4(OH)COO), sodium chloride (NaCl),

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553 lithium acetate (CH3COOLi), dimethyl sulfoxide (“DMSO”; C2H6OS), and citric acid

554 (C6H8O7). Toxicity gradients (or ‘environmental clines’) were generated along the y-

555 axis of the 96-well plates such that the bottom row contained the lowest

556 concentration, and the top row contained the highest concentration of the toxin, lethal

557 for all strains (exact concentrations in Table S1).

558 Plates were incubated at 30°C for 4 days. Then, 1µl from each well was

559 transferred to the same position on a new 96-well culture plate containing identical

560 concentrations. The optical density (OD) of each well was measured with a

561 microplate reader (Infinite M200 Pro, Tecan) (time point t0) and plates were

562 incubated at 30°C for another 3 days. After this, another OD measurement was taken

563 (t1) and plates were stored at 4°C until further processing. We calculated, for each

564 well, whether growth had occurred by subtracting ODt0 from ODt1. Assuming that a

565 doubling in optical density approximately equals a doubling of cell numbers, cells in

566 permissive environments at the bottom of the plate completed on average 3 cell

567 cycles (2.78 ± 0.15 across toxins) from t0 to t1 whereas cells in the topmost row did

568 not divide at all.

569 We sampled from all twelve wells of the bottom row (i.e. the lowest stress)

570 and from the twelve wells of the highest stress that allowed growth from each

571 column, from all ten plates. These 240 samples were streaked out for single colonies

572 on YEPD plates and grown for 48h at 30°C. We then picked a single colony from

573 each sample, and froze it for sequencing.

574

575 DNA extraction, PCR and sequencing protocols

576 DNA was extracted using the MasterPure Yeast DNA Purification Kit (Epicentre). A

577 genotyping-by-sequencing protocol was modified from microsatellite library

578 preparation and ddRAD sequencing approaches as follows (Nolte, et al. 2005;

579 Peterson, et al. 2012). The library construction is based on an efficient combined

580 restriction digest/adaptor ligation. The restriction enzymes Csp6I (which cleaves 5’-

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581 G^T A C -3’ sites) and PstI (which cleaves 5’- C T G C A^G -3’ sites) were used to

582 digest genomic DNA to generate sticky ends. The reaction conditions permit that

583 sticky end adapters and T4 ligase are added to the reaction such that adaptors are

584 ligated to the restriction sites. Importantly, the adaptors do not fully reconstitute the

585 restriction sites. Thus, once an adaptor is ligated, this site will not be recut while an

586 undesired ligation of two genomic DNA sticky ends will be recut until all DNA ends

587 are saturated with an adaptor. For this purpose we modified the ligation sites of the

588 Illumina Truseq adapters such that they matched the sticky ends generated by the

589 restriction enzymes. Further, we labeled the adapters to include 24 and 16 different

590 molecular barcodes (MIDs) respectively, which could be combined in 384 different

591 combinations for multiplexed sequencing (paired ends) of the libraries on an Illumina

592 Myseq sequencer.

593

594 Quality filtering of raw reads and mapping protocol

595 We examined the quality of raw ddRADseq reads of each sample using FastQC

596 (v0.11.8) (FastQC 2018). Illumina sequencing adapters, primer sequences,

597 ambiguous and low quality nucleotides (PHRED quality score < 20) were removed

598 from both paired-end reads according to the default parameters in the NGS QC

599 Toolkit (v2.3.3) (Patel and Jain 2012). Three of 240 F2 genomes were abandoned

600 due low sequence yield. A total of 8.2 Gb high-quality reads were generated after

601 quality control with mean depth of 12.84 per marker.

602 Parental strain sequences were downloaded from the Saccharomyces

603 Genome Database website. The S288c reference genome used was R64-2-

604 1_20150113. The N17 reference genome was obtained from

605 ftp://ftp.sanger.ac.uk/pub/users/dmc/yeast/latest/, in the para_assemblies/ N_17

606 folder.

607 Given the high sequence divergence between S. cerevisiae and S. paradoxus

608 (15%), correct assignment of reads to the parental species was efficient using

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609 NovoAlign (NovoAlign 2018) with default parameters. For this, both parental

610 genomes were concatenated and used as a combined reference for mapping, and

611 species affiliations and chromosomal positions of successfully mapped reads were

612 written to the same output file. Mapped reads were considered correctly assigned to

613 the reference sequence when the mapping quality was >= 20, indicating a single and

614 unique best match.

615

616 Ploidy determination

617 To differentiate haploid and diploid F2 hybrid genomes (to be able to score the

618 prevalence of homo- and heterozygosity), we mapped paired-end reads again, this

619 time only to the S. cerevisiae S288c reference genome using NovoAlign (NovoAlign

620 2018) with the default parameters. We sorted mapped files according to their

621 genomic coordinates using Picard Tools v2.18.23 (Picard 2019) and performed

622 variant calling in FreeBayes (Garrison and Marth 2012) with at least five supporting

623 reads required to consider a variant. SNPs were selected and filtered using GATK

624 (Li, et al. 2008) according to stringent filtering criteria with the following settings: a)

625 QUAL >30.0; b) QD >5.0; c) FS <60.0; d) MQ >40.0; e) MQRankSum >-12.5; and f)

626 ReadPosRankSum >- 8.0. Additionally, if there were more than 3 SNPs clustered in

627 a 10-bp window, we considered all three SNPs false positives and removed them.

628 After filtering, we identified 14,975 SNPs, ranging between 10,725 and 12,345

629 among environments.

630 Haploidy or diploidy was called using the heterozygosity score of biallelic SNPs

631 using a custom bash script. Theoretically, both the number of heterozygous SNPs

632 and ratio of heterozygous SNPs over the total SNPs in haploids should be zero

633 (except in case of sequencing errors and aneuploidies). However, the absolute

634 number of heterozygous SNPs is affected by the overall sequence size of each

635 sample, and the ratio is affected by the number of SNPs derived from S. paradoxus

636 (because of the large nucleotide divergence between the parents, ~15% of the reads

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637 from S. paradoxus do not map to the S. cerevisiae reference genome). Also,

638 although unlikely, diploid F2 genomes can theoretically be fully homozygous with

639 both copies of eight chromosomes inherited from each parental species, assuming

640 genetic material from both parental species is equally represented in the F2

641 generation. At zero heterozygosity, haploid and diploid genomes are

642 indistinguishable by SNP calling and mapping to parental genomes. Thus, at the risk

643 of omitting extremely homozygous diploids, we applied two criteria to consider a

644 genome diploid: 1) The number of heterozygous SNPs in a diploid F2 genome should

645 be larger than the average number of heterozygous SNPs found in both parental

646 genomes. 2) The ratio of heterozygous over hetero- plus homozygous SNPs in a

647 diploid F2 genome should be larger than the average ratio of heterozygous over

648 hetero- plus homozygous SNPs in parental genomes. Since there are nearly zero

649 heterozygous SNPs in the S. cerevisiae genome, this can be simplified to:

!!"!#$ �(���!.!"# ) !!"!#$ !!"! �(���!.!"# ) + �(���!.!"# ) ����� ���!!"!#$ ≈ !"#$!%! 2

650 Using this analysis, we assigned 187 of the 237 F2 genomes to be diploid.

651

652 Chromosome zygosity

653 To determine the zygosity of each chromosome in diploid F2 genomes, we mapped

654 high quality reads of each sample to the reference combining both parental

655 genomes. We extracted the species affiliation of each read using a custom bash

656 script. The chromosomal content per species per sample was extracted using custom

657 perl code by calculating the proportion of markers mapping to only one species over

658 markers mapping to both species. We considered chromosomes heterozygous if

659 marker proportions fell between 0.25 – 0.75. Chromosomes with smaller or larger

660 species content were assigned homozygous.

26 bioRxiv preprint doi: https://doi.org/10.1101/622845; this version posted April 30, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

661 To understand what causes variation in zygosity between environments,

662 chromosomes and genomes, we modeled chromosome heterozygosity using a

663 mixed-effect linear model. Chromosome zygosity was used as the response variable

664 in the model and was predicted as a function of fixed predictors environment and

665 chromosome ID and their interactions and sample ID as a random effect. We

666 selected the most appropriate model by identifying the simplest model that

667 maintained the lowest Akaike Information Criteron (AIC; (Akaike 1974)). AIC

668 optimizes the relationship between the fit and complexity of a model by balancing the

669 fit of the model with the number of parameters estimated (Harrison, et al. 2018).

670

671 Genome-level and chromosome-level hybridity

672 To describe the genome-level and chromosome-level hybridity we calculated the

673 percentage of the entire genome or chromosome for each sample that mapped to

674 either the S. cerevisiae or the S. paradoxus genome. We defined hybridity as the

675 percent of markers per genome or chromosome mapping to S. cerevisiae (100% = S.

676 cerevisiae, 0% = S. paradoxus). This was calculated across all environments, as well

677 as for each environment individually. We then used mixed-effect linear models with

678 genome and chromosome hybridity as response variables. We predicted hybridity to

679 be a function of the fixed effects environment, chromosome ID, ploidy and their

680 interactions, and included sample ID as random effect. We selected the most

681 appropriate model by starting with the full model and removing insignificant

682 components, identifying the simplest model that maintained the lowest AIC.

683 To characterize the interactions between chromosomes within a F2 genome,

684 we calculated the difference in chromosome hybridity for each chromosome

685 combination, defined as delta chromosome hybridity (DCH). Large DCH indicates

686 that the two chromosomes map primarily to opposing species within a genome. Small

687 DCH indicates that the chromosomes have similar levels of hybridity, suggesting that

688 either the chromosomes come from primarily the same species or at least have

27 bioRxiv preprint doi: https://doi.org/10.1101/622845; this version posted April 30, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

689 similar hybridity proportions. DCH was calculated for all environments together, as

690 well as each environment individually. To investigate the epistatic interactions

691 between chromosomes we determined the Pearson correlation (r) between

692 chromosome hybridity for all pairwise chromosome combinations. A significant

693 positive correlation between two chromosomes suggests a positive epistatic

694 interaction within a species, meaning that as chromosome hybridity in one

695 chromosome shifts towards one species (1 = S. cerevisiae, 0 = S. paradoxus), the

696 chromosome hybridity in the linked chromosome also shifts towards the same

697 species. A significant negative correlation between two chromosomes suggests a

698 negative epistatic interaction within a species. In this case, as one chromosome

699 shifts towards one species, the linked chromosome shifts towards the other species.

700

701 Simulated chromosomes

702 In order to determine if the observed zygosity, hybridity and chromosomal

703 interactions found in each environment deviated from neutral expectations, we

704 developed simulations that determined the expected range for these measurements

705 based solely on chance, assuming no selection and free segregation of

706 chromosomes in hybrid genomes. Each simulation displayed the expected

707 distribution of zygosity, hybridity or chromosomal interactions that could be found in a

708 selection-free environment with no epistasis between chromosomes. Each simulated

709 environment was repeated 10,000 times. Each environment consisted of 24

710 genomes and each genome consisted of 16 chromosomes as found in our data.

711 Each chromosome was randomly assigned a chromosome hybridity score between 0

712 and 1. To study the expected zygosity, chromosomes were labeled as either

713 homozygous for S. cerevisiae or S. paradoxus or heterozygous based on the same

714 rules used in the analysis of our data (see Chromosome zygosity). The

715 heterozygosity and hybridity for each genome were then calculated for each

716 simulation. The means and distributions of these values were plotted and compared

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717 to the means and distributions we received from our data (Figure S1, S3). To study

718 the expected chromosome interactions, we calculated the 120 interactions that occur

719 within a simulated genome between chromosomes. Delta chromosome hybridity

720 (DCH) and Pearson correlation (r) were calculated in the same manner as our data

721 was analyzed (see Genome-level and chromosome-level hybridity). The

722 distributions of these interactions were then plotted and compared to the data from

723 our study (Figure 4 and S4). For the Pearson correlation (r), we also determined the

724 range of expected significant positive and negative correlations and compared them

725 to our data (Figure S5).

726

727 Genotype x Environment Interactions and Gene Ontology

728 To explore for gene-by-environment interactions, i.e. which gene from which species

729 background is more frequently found in which environment, we divided genomes into

730 10kb bins. Assuming each bin is a locus and the parental species affiliations are the

731 two possible alleles, we scored bins as 1 if all markers within that bin mapped to the

732 S. cerevisiae reference genome, and 0 if all markers mapped to the S. paradoxus

733 reference genome across all 24 genomes within a given environment (excluding

734 chromosome 5 and 7). Genes located in or overlapping with these fixed regions of

735 each environment were further inspected using the gene annotation data base

736 DAVID (Huang, et al. 2009). Environment-independent fixations of alleles (bins fixed

737 for the same species across all environments) and unique fixations (bin fixed only for

738 one environment) were extracted using custom R code.

739

740 Author contributions

741 RS and DG received the project idea and designed the experiment. RS performed

742 the experiment and collected the samples. AWN planned and performed the library

743 preparation and sequencing. ZZ processed the ddRADseq data. ZZ, DPB and TJ

744 analyzed the data. RS, ZZ and DPB wrote the manuscript.

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745

746 Acknowledgements

747 We thank Elke Bustorf for the preparation of libraries and David Rogers for advice on

748 mapping. This work was supported by the Max Planck Society (to DG), ERC starting

749 grant (“EVOLMAPPING” to AWN), Swedish Vetenskapsrådet (grant number 2017-

750 04963 to RS), Carl Trygger foundation (to ZZ) and Wenner-Gren Foundations (grant

751 number UPD2018-0196 to DPB).

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991 Supplementary Material

992

993 Table S1 Toxins and concentrations

994 Concentrations (in M or %) of the 10 toxins used in the experiment. Numbers 1-8

995 indicate row numbers from top (lethal) to bottom (permissive) on 96-well culture plates.

Substance Concentration

1 2 3 4 5 6 7 8

salicylic acid (C7H6O3) M 0.086 0.057 0.038 0.025 0.017 0.011 0.008 0.005

caffeine (C8H10N4O2) M 0.103 0.069 0.046 0.031 0.020 0.014 0.009 0.006

zinc sulfate (ZnSO4), M 0.186 0.124 0.083 0.055 0.037 0.024 0.016 0.011

Nipagin (CH3(C6H4(OH)COO)) M 0.053 0.035 0.024 0.016 0.010 0.007 0.005 0.003

sodium chloride (NaCl) M 4.0 2.667 1.778 1.185 0.790 0.527 0.351 0.234

citric acid (C6H8O7) M 0.115 0.077 0.051 0.034 0.023 0.015 0.010 0.007

lithium acetate (CH3COOLi) M 0.5 0.333 0.222 0.148 0.099 0.066 0.044 0.029

hydrogen peroxide (H2O2) % 0.014 0.009 0.006 0.004 0.003 0.002 0.001 0.001

ethanol (C2H6O) % 50 33.333 22.222 14.815 9.877 6.584 4.390 2.926

DMSO (C2H6OS) % 30 20.000 13.333 8.889 5.926 3.951 2.634 1.756 996

997

998

999 Table S2 Linear mixed-effect models used to predict zygosity

1000 The most appropriate model was selected by identifying the simplest model that

1001 maintained the lowest Akaike Information Criteron (AIC). The optimal model, given

1002 our data, is indicated by a black square.

1003

1004

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1005 Table S3 Linear mixed-effect models used to predict chromosome hybridity

1006 The most appropriate model was selected by identifying the simplest model that

1007 maintained the lowest Akaike Information Criteron (AIC). The optimal model, given

1008 our data, is indicated by a black square.

fixed effects variable Response random model Chromosome ID : Chromosome ID : AIC BIC count Variable Environment Chromosome ID Ploidy effects Environment Ploidy 1 5 Hybridity + + + + + 1|S -742.4 368.4 2 4 + + + + 1|S -742.4 368.4 3 + + + + 1|S -672.1 -403.8 4 + + + + 1|S -742.4 368.4 5 + + + + 1|S -742.4 368.4 6 + + + + 1|S -742.4 368.4 7 3 + + + 1|S -644.8 470 8 + + + 1|S -727.2 283.8 9 + + + 1|S -672.1 -403.8 10 + + + 1|S -726.1 291.2 11 + + + 1|S -672.1 -403.8 12 + + + 1|S -742.4 368.4 13 + + + 1|S -726.1 291.2 14 + + + 1|S -664.8 -452.6 15 + + + 1|S -742.4 368.4 16 + + + 1|S -742.4 368.4 17 2 + + 1|S -645.9 -477.4 18 + + 1|S 1109.7 1190.8 19 + + 1|S -727.2 283.8 20 + + 1|S -672.1 -403.8 21 + + 1|S -637.4 -518.4 22 + + 1|S -727.2 283.8 23 + + 1|S -664.8 -452.6 24 + + 1|S -726.1 291.2 25 + + 1|S -664.8 -452.8 26 + + 1|S -742.4 368.4 27 1 + 1|S 1108.3 1183.2 28 + 1|S -634.8 -522.5 29 + 1|S 1109.2 1134.1 30 + 1|S -727.2 283.8 31 + 1|S -664.8 -452.6 32 0 1|S 1110.6 1129.3 1009 1010

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1011

1012 Figure S1 Genome-wide zygosity of simulated chromosomes 1013 (A) The distributions of homozygosity for S. cerevisiae or S. paradoxus and 1014 heterozygosity in a simulated environment resulting from simulated chromosomes 1015 based on no chromosomal interactions (no epistasis) and random segregation. Each 1016 environment consists of 24 genomes, each consisting of 16 simulated chromosomes. 1017 Each simulated environment was repeated 10,000 times. (B) The distribution of 1018 zygosities found in our experimental environments. Distributions are colored 1019 according to their zygosity (red = homozygous S. cerevisiae, grey = heterozygous, 1020 blue = homozygous S. paradoxus). (C) Mean zygosity of our experimental 1021 environments as compared to the range of mean zygosities found in simulations 1022 (grey-black lines). 1023

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1024

1025

1026 Figure S2 Genome-wide hybridity of 237 F2 hybrids 1027 (A) Genome hybridity across all chromosomes and all environments, measured as 1028 the mean of the 16 chromosome hybridities (Figure 2) within a genome. Boxplots 1029 indicate the median and interquartile range. Diamonds indicate the mean. Dashed 1030 line (0.5) indicates equal amount of the genome mapping to S. cerevisiae. (B) 1031 Genome hybridity within environments. Numbers indicate the average genome 1032 hybridity for each F2 genome within each environment (n genomes ~ 24). 1033

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1034

1035

1036 Figure S3 Genome-wide hybridity of simulated chromosomes 1037 (A) The distribution of genome-wide hybridity in a simulated environment resulting 1038 from simulated chromosomes based on no chromosomal interactions (no epistasis). 1039 Each environment consists of 24 genomes, each consisting of 16 simulated 1040 chromosomes. Each simulated environment was repeated 10,000 times. (B) The 1041 distribution of genome-wide hybridity found in our experimental populations. 1042 Distributions are colored according to their overall mean species bias (red = S. 1043 cerevisiae, blue = S. paradoxus). (C) Mean genome-wide hybridity of our 1044 experimental populations as compared to the range of genome-wide hybridity found 1045 in simulations (grey-black lines). 1046

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1047 1048 1049 Figure S4 Pearson correlation (r) of simulated chromosomes 1050 (A) The distribution of Pearson correlations in a simulated environment resulting from 1051 simulated chromosomes based on no chromosomal interactions (no epistasis). Each 1052 environment consists of 24 genomes, each consisting of 16 simulated chromosomes. 1053 Each simulated environment was repeated 10,000 times. (B) The distribution of 1054 Pearson correlations found in our experimental environments. Distributions are 1055 colored according to their mean Pearson correlation. (C) Mean Pearson correlation of 1056 our experimental environments as compared to the range of mean Pearson 1057 correlations found in simulations (grey-black lines). 1058

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1059 1060 1061 Figure S5 Distribution of significant (p < 0.01) positive and negative 1062 correlations resulting from simulated chromosomes 1063 Each environment consists of 24 genomes, each consisting of 16 simulated 1064 chromosomes. Each simulated environment was repeated 10,000 times. A grey 1065 square indicates that at least one simulated environment resulted in that many 1066 negative and positive correlations. The density plot indicates the distribution of 1067 simulated environments. Our experimental environments are indicated with squares 1068 and labels. The color of each environment indicates the bias towards positive (green) 1069 or negative (purple) correlations.

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A 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Caffeine

Citric_Acid

DMSO

EtOH Origin Hydrogen_Peroxide S.cerevisiae Lithium_Acetate S.paradoxus

NaCl

Max allel frequency = 1 Nipagin

Salicylic_Acid

ZnSO4 Chromosomes

B C NaCl

Hydrogen_Peroxide

Lithium_Acetate

DMSO Origin Caffeine S.cerevisiae Citric_Acid S.paradoxus

Environments EtOH

ZnSO4

Nipagin

Salicylic_Acid

0 500 1000 1070 Fixed gene count 1071 1072 Figure S6 Genotype-by-environment interactions 1073 (A) 10kb bins fixed for either one or the other parental allele across all genomes sampled 1074 from a given environment. All 16 chromosomes are shown. Bins fixed for the same 1075 species across all environments are marked with green triangles. Environment-specific 1076 regions fixed for the same species in all 24 genomes sampled from the same 1077 environment are marked with purple triangles. (B) Word cloud summarizing results of 1078 gene ontology analysis of the 1884 genes overlapping with or located within all fixed bins 1079 using DAVID (Huang, et al. 2009) (C) Bar chart showing the number of genes fixed for 1080 the S. paradoxus allele on the left and the S. cerevisiae allele on the right, by 1081 environment.

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