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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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.
1 Recombining your way out of trouble: The genetic architecture of hybrid
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 Genetics, Department of Zoology, Stockholm University,
10 Svante Arrheniusväg 18 B, 106 91 Stockholm, Sweden
11 2Max Planck Institute for Evolutionary Biology, 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:
1 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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.
21 Abstract
22 Hybridization between species is a fundamental evolutionary force that can both
23 promote and delay adaptation. 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 epistasis and heterozygote advantage 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 chromosomes). Linear
32 mixed effect models revealed strong genotype-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
2 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.
47 Introduction
48 Populations exposed to gene flow, introgression or hybridization contain vast
49 amounts of genetic variation, sometimes producing phenotypes with more extreme
50 adaptations 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
3 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.
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
4 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.
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, dominance (the reciprocal masking of
108 deleterious alleles at multiple loci; Bruce 1910)) and overdominance (the intrinsic
109 benefit of being heterozygous for at least one locus) 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 chromosome 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
5 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.
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 mutations 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 genotypes 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 -
6 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.
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 genes, which, as
7 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.
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
8 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.
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
9 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.
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 ploidy (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.
10 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.
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.
11 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.
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
12 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.
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).
13 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.
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.
14 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.
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 allele (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).
15 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.
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,
16 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.
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,
17 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.
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
18 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.
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 (underdominance), 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 heterosis
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
19 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.
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
20 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.
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
21 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.
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),
22 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.
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’-
23 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.
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
24 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.
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
25 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.
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
28 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.
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.
29 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.
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).
30 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.
752 References
753 Abbott R, Albach D, Ansell S, Arntzen JW, Baird SJE, Bierne N, Boughman JW, 754 Brelsford A, Buerkle CA, Buggs R, et al. 2013. Hybridization and speciation. Journal 755 of Evolutionary Biology 26:229-246.
756 Akaike H. 1974. A new look at the statistical model identification. IEEE Transactions 757 on Automatic Control 19:716-723.
758 Anderson E, Stebbins GL. 1954. Hybridization as an evolutionary stimulus. Evolution 759 8:378-388.
760 Armbruster P, Bradshaw WE, Steiner AL, Holzapfel CM. 1999. Evolutionary 761 responses to environmental stress by the pitcher-plant mosquito, Wyeomyia smithii. 762 Heredity 83:509-519.
763 Arnold ML. 2016. Anderson's and Stebbins' prophecy comes true: genetic exchange 764 in fluctuating environments. Systematic Botany 41:4-16.
765 Arnold ML. 2006. Evolution through genetic exchange. Oxford, New York: Oyxford 766 University Press.
767 Arnold ML, Ballerini ES, Brothers AN. 2012. Hybrid fitness, adaptation and 768 evolutionary diversification: lessons learned from Louisiana Irises. Heredity 108:159- 769 166.
770 Barreto FS, Burton RS. 2013. Elevated oxidative damage is correlated with reduced 771 fitness in interpopulation hybrids of a marine copepod. Proceedings of the Royal 772 Society B: Biological Sciences 280:20131521.
773 Barton NH. 2000. Estimating multilocus linkage disequilibria. Heredity 84:373-389.
774 Barton NH, Gale KS. 1993. Genetic analysis of hybrid zones. In: Harrison RG, 775 editor. Hybrid Zones and the Evolutionary Process. New York: Oxford University 776 Press. p. 13-45.
777 Blein-Nicolas M, Albertin W, da Silva T, Valot B, Balliau T, Masneuf-Pomarède I, Bely 778 M, Marullo P, Sicard D, Dillmann C, et al. 2015. A systems approach to elucidate 779 heterosis of protein abundances in yeast. Molecular & Cellular Proteomics : MCP 780 14:2056-2071.
781 Boynton PJ, Janzen T, Greig D. 2018. Modeling the contributions of chromosome 782 segregation errors and aneuploidy to Saccharomyces hybrid sterility. Yeast 35:85-98.
783 Bruce AB. 1910. The mendelian theory of heredity and the augmentation of vigor. 784 Science 32.
785 Buerkle CA. 2005. Maximum-likelihood estimation of a hybrid index based on 786 molecular markers. Molecular Ecology Notes 5:684-687.
31 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.
787 Buerkle CA, Rieseberg LH. 2008. The rate of genome stabilization in homoploid 788 hybrid species. Evolution 62:266-275.
789 Chang S-L, Lai H-Y, Tung S-Y, Leu J-Y. 2013. Dynamic Large-Scale Chromosomal 790 Rearrangements Fuel Rapid Adaptation in Yeast Populations. PLoS Genet 791 9:e1003232.
792 Coyne JA, Orr HA. 2004. Speciation. Sunderland, MA: Sinauer Associates.
793 Cubillos FA, Billi E, Zörgo E, Parts L, Fargier P, Omholt S, Blomberg A, Warringer J, 794 Louis EJ, Liti G. 2011. Assessing the complex architecture of polygenic traits in 795 diverged yeast populations. Molecular Ecology 20:1401-1413.
796 de Vos MGJ, Poelwijk FJ, Battich N, Ndika JDT, Tans SJ. 2013. Environmental 797 Dependence of Genetic Constraint. PLoS Genetics 9:e1003580.
798 Dhar R, Sagesser R, Weikert C, Yuan J, Wagner A. 2011. Adaptation of 799 Saccharomyces cerevisiae to saline stress through laboratory evolution. J Evol Biol 800 24:1135-1153.
801 Dittrich-Reed DR, Fitzpatrick BM. 2012. Transgressive hybrids as hopeful monsters. 802 Evolutionary Biology 40:310-315.
803 Dobzhansky T. 1936. Studies on hybrid sterility. II. Localization of sterility factors in 804 Drosophila pseudoobscura hybrids. Genetics 21:113-135.
805 Dunn B, Paulish T, Stanbery A, Piotrowski J, Koniges G, Kroll E, Louis EJ, Liti G, 806 Sherlock G, Rosenzweig F. 2013. Recurrent rearrangement during adaptive 807 evolution in an interspecific yeast hybrid suggests a model for rapid introgression. 808 PLoS Genet 9:e1003366.
809 Edmands S, Deimler JK. 2004. Local adaptation, intrinsic coadaptation and the 810 effects of environmental stress on interpopulation hybrids in the copepod Tigriopus 811 californicus. Journal of Experimental Marine Biology and Ecology 303:183-196.
812 Falush D, Stephens M, Pritchard JK. 2003. Inference of population structure using 813 multilocus genotype data: linked loci and correlated allele frequencies. Genetics 814 164:1567-1587.
815 FastQC. 2018. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
816 Filteau M, Hamel V, Pouliot MC, Gagnon-Arsenault I, Dube AK, Landry CR. 2015. 817 Evolutionary rescue by compensatory mutations is constrained by genomic and 818 environmental backgrounds. Mol Syst Biol 11:832.
819 Fitzpatrick BM. (Fitzpatrick2012 co-authors). 2012. Estimating ancestry and 820 heterozygosity of hybrids using molecular markers. BMC Evolutionary Biology 12:1- 821 14.
32 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.
822 Fitzpatrick BM, Shaffer HB. 2007. Hybrid vigor between native and introduced 823 salamanders raises new challenges for conservation. Proceedings of the National 824 Academy of Sciences of the United States of America 104:15793-15798.
825 Garrison E, Marth G. 2012. Haplotype-based variant detection from short-read 826 sequencing.
827 Gompert Z, Buerkle CA. 2013. Analyses of genetic ancestry enable key insights for 828 molecular ecology. Molecular Ecology 22:5278-5294.
829 Gompert Z, Buerkle CA. 2016. What, if anything, are hybrids: enduring truths and 830 challenges associated with population structure and gene flow. Evolutionary 831 Applications:n/a-n/a.
832 Greig D. 2007. A screen for recessive speciation genes expressed in the gametes of 833 F1 hybrid yeast. PLoS Genetics 3:281-286.
834 Greig D, Louis EJ, Borts RH, Travisano M. 2002. Hybrid speciation in experimental 835 populations of yeast. Science 298:1773-1775.
836 Greig D, Travisano M, Louis EJ, Borts RH. 2003. A role for the mismatch repair 837 system during incipient speciation in Saccharomyces. Journal of Evolutionary Biology 838 16:429-437.
839 Harrison XA, Donaldson L, Correa-Cano ME, Evans J, Fisher DN, Goodwin CED, 840 Robinson BS, Hodgson DJ, Inger R. 2018. A brief introduction to mixed effects 841 modelling and multi-model inference in ecology. PeerJ 6:e4794-e4794.
842 Holzman R, Hulsey CD. 2017. Mechanical Transgressive Segregation and the Rapid 843 Origin of Trophic Novelty. Scientific Reports 7:40306.
844 Hose J, Yong CM, Sardi M, Wang Z, Newton MA, Gasch AP. 2015. Dosage 845 compensation can buffer copy-number variation in wild yeast. eLife 4:e05462.
846 Huang DW, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of 847 large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44-57.
848 Hunter N, Chambers S, Louis E, Borts R. 1996. The mismatch repair system 849 contributes to meiotic sterility in an interspecific yeast hybrid. The EMBO Journal 850 15:1726–1733.
851 Hwang AS, Pritchard VL, Edmands S. 2016a. Recovery from hybrid breakdown in a 852 marine invertebrate is faster, stronger and more repeatable under environmental 853 stress. Journal of Evolutionary Biology 29:1793-1803.
854 Hwang AS, Pritchard VL, Edmands S. 2016b. Recovery from hybrid breakdown in a 855 marine invertebrate is faster, stronger and more repeatable under environmental 856 stress. Journal of Evolutionary Biology:n/a-n/a.
33 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.
857 Jaffe M, Dziulko A, Smith JD, St Onge RP, Levy SF, Sherlock G. 2019. Improved 858 discovery of genetic interactions using CRISPRiSeq across multiple environments. 859 Genome Res.
860 King KC, Stelkens RB, Webster JP, Smith DF, Brockhurst MA. 2015. Hybridization in 861 parasites: consequences for adaptive evolution, pathogenesis, and public health in a 862 changing world. PLoS Pathog 11:e1005098.
863 Koevoets T, Van De Zande L, Beukeboom LW. 2012. Temperature stress increases 864 hybrid incompatibilities in the parasitic wasp genus Nasonia. Journal of Evolutionary 865 Biology 25:304-316.
866 Koide Y, Sakaguchi S, Uchiyama T, Ota Y, Tezuka A, Nagano AJ, Ishiguro S, 867 Takamure I, Kishima Y. 2019. Genetic Properties Responsible for the Transgressive 868 Segregation of Days to Heading in Rice. G3: 869 Genes|Genomes|Genetics:g3.201011.202019.
870 Kuczyńska A, Surma M, Adamski T. 2007. Methods to predict transgressive 871 segregation in barley and other self-pollinated crops. Journal of Applied Genetics 872 48:321-328.
873 Kvitek DJ, Sherlock G. 2011. Reciprocal Sign Epistasis between Frequently 874 Experimentally Evolved Adaptive Mutations Causes a Rugged Fitness Landscape. 875 PLoS Genetics 7:e1002056.
876 Kwon-Chung KJ, Chang YC. 2012. Aneuploidy and drug resistance in pathogenic 877 fungi. Plos Pathogens 8:e1003022.
878 Lai Z, Nakazato T, Salmaso M, Burke JM, Tang S, Knapp SJ, Rieseberg LH. 2005. 879 Extensive chromosomal repatterning and the evolution of sterility barriers in hybrid 880 sunflower species. Genetics 171:291-303.
881 Laiba E, Glikaite I, Levy Y, Pasternak Z, Fridman E. 2016. Genome scan for 882 nonadditive heterotic trait loci reveals mainly underdominant effects in 883 Saccharomyces cerevisiae. Genome 59:231-242.
884 Lee JT, Coradini ALV, Shen A, Ehrenreich IM. 2019. Layers of Cryptic Genetic 885 Variation Underlie a Yeast Complex Trait. Genetics 211:1469-1482.
886 Leonhardt SA, Fearson K, Danese PN, Mason TL. 1993. HSP78 encodes a yeast 887 mitochondrial heat shock protein in the Clp family of ATP-dependent proteases. Mol 888 Cell Biol 13:6304-6313.
889 Lewontin RC, Birch LC. 1966. Hybridization as a source of variation for adaptation to 890 new environments. Evolution 20:315-336.
891 Li H, Ruan J, Durbin R. 2008. Mapping short DNA sequencing reads and calling 892 variants using mapping quality scores. Genome Res 18:1851-1858.
34 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.
893 Lindtke D, Buerkle CA, Barbará T, Heinze B, Castiglione S, Bartha D, Lexer C. 2012. 894 Recombinant hybrids retain heterozygosity at many loci: new insights into the 895 genomics of reproductive isolation in Populus. Molecular Ecology 21:5042-5058.
896 Liti G, Carter DM, Moses AM, Warringer J, Parts L, James SA, Davey RP, Roberts 897 IN, Burt A, Koufopanou V, et al. 2009. Population genomics of domestic and wild 898 yeasts. Nature 458:337-341.
899 Luo Z, van Vuuren HJ. 2009. Functional analyses of PAU genes in Saccharomyces 900 cerevisiae. Microbiology 155:4036-4049.
901 Lynch M. 1991. The genetic interpretation of inbreeding depression and outbreeding 902 depression. Evolution 45:622-629.
903 Mallet J. 2007. Hybrid speciation. Nature 446:279-283.
904 Marks VD, Ho Sui SJ, Erasmus D, van der Merwe GK, Brumm J, Wasserman WW, 905 Bryan J, van Vuuren HJ. 2008. Dynamics of the yeast transcriptome during wine 906 fermentation reveals a novel fermentation stress response. FEMS Yeast Res 8:35- 907 52.
908 Marullo P, Bely M, Masneuf-Pomarede I, Pons M, Aigle M, Dubourdieu D. 2006. 909 Breeding strategies for combining fermentative qualities and reducing off-flavor 910 production in a wine yeast model. Fems Yeast Research 6:268-279.
911 McFarlane SE, Pemberton JM. 2019. Detecting the True Extent of Introgression 912 during Anthropogenic Hybridization. Trends Ecol Evol 34:315-326.
913 Miller EL, Greig D. 2014. Spore germination determines yeast inbreeding according 914 to fitness in the local environment. American Naturalist 185:291-301.
915 Müller HJ. 1942. Isolating mechanisms, evolution and temperature. Biological 916 Symposium 6:71-125.
917 Nolte AW, Freihof J, Stemshorn KC, Tautz D. 2005. An invasive lineage of sculpins, 918 Cottus sp. (Pisces, Teleostei) in the Rhine with new habitat adaptations has 919 originated from hybridisation between old phylogeographic groups. Proceedings of 920 the Royal Society of London Series B-Biological Sciences 272:2379-2387.
921 Nolte AW, Tautz D. 2010. Understanding the onset of hybrid speciation. Trends 922 Genetics 26:54-58.
923 NovoAlign. 2018. http://novocraft.com.
924 Patel RK, Jain M. 2012. NGS QC Toolkit: A Toolkit for Quality Control of Next 925 Generation Sequencing Data. Plos ONE 7:e30619.
35 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.
926 Pavelka N, Rancati G, Zhu J, Bradford WD, Saraf A, Florens L, Sanderson BW, 927 Hattem GL, Li R. 2010. Aneuploidy confers quantitative proteome changes and 928 phenotypic variation in budding yeast. Nature 468:321-325.
929 Payseur BA, Rieseberg LH. 2016. A genomic perspective on hybridization and 930 speciation. Molecular Ecology 25:2337-2360.
931 Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. 2012. Double digest 932 RADseq: An inexpensive method for de novo SNP discovery and genotyping in 933 model and non-model species. Plos ONE 7:e37135.
934 Picard. 2019. https://broadinstitute.github.io/picard/.
935 Piotrowski JS, Nagarajan S, Kroll E, Stanbery A, Chiotti KE, Kruckeberg AL, Dunn B, 936 Sherlock G, Rosenzweig F. 2012. Different selective pressures lead to different 937 genomic outcomes as newly-formed hybrid yeasts evolve. BMC Evol Biol 12:46.
938 Plech M, de Visser JAGM, Korona R. 2014. Heterosis is prevalent among 939 domesticated but not wild strains of Saccharomyces cerevisiae. G3: Genes, 940 Genomes, Genetics 4:315-323.
941 Pritchard JK, Stephens M, Donnelly P. 2000. Inference of Population Structure Using 942 Multilocus Genotype Data. Genetics 155:945-959.
943 Pritchard VL, Knutson VL, Lee M, Zieba J, Edmands S. 2013. Fitness and 944 morphological outcomes of many generations of hybridization in the copepod 945 Tigriopus californicus. Journal of Evolutionary Biology 26:416-433.
946 Rieseberg LH, Archer MA, Wayne RK. 1999. Transgressive segregation, adaptation 947 and speciation. Heredity 83:363-372.
948 Rieseberg LH, Raymond O, Rosenthal DM, Lai Z, Livingstone K, Nakazato T, Durphy 949 JL, Schwarzbach AE, Donovan LA, Lexer C. 2003. Major ecological transitions in 950 wild sunflowers facilitated by hybridization. Science 301:1211-1216.
951 Rogers DW, McConnell E, Ono J, Greig D. 2018. Spore-autonomous fluorescent 952 protein expression identifies meiotic chromosome mis-segregation as the principal 953 cause of hybrid sterility in yeast. Plos Biology 16:e2005066.
954 Rossignol T, Dulau L, Julien A, Blondin B. 2003. Genome-wide monitoring of wine 955 yeast gene expression during alcoholic fermentation. Yeast 20:1369-1385.
956 Schumer M, Rosenthal GG, Andolfatto P. 2014. How common is homoploid hybrid 957 speciation? Evolution 68:1553-1560.
958 Seehausen O. 2004. Hybridization and adaptive radiation. Trends in Ecology & 959 Evolution 19:198-207.
36 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.
960 Selmecki AM, Dulmage K, Cowen LE, Anderson JB, Berman J. 2009. Acquisition of 961 aneuploidy provides increased fitness during the evolution of antifungal drug 962 resistance. PLoS Genetics 5: e1000705.
963 Shahid M, Han S, Yoell H, Xu J. 2008. Fitness distribution and transgressive 964 segregation across 40 environments in a hybrid progeny population of the human- 965 pathogenic yeast Cryptococcus neoformans. Genome 51:272-281.
966 Shapira R, Levy T, Shaked S, Fridman E, David L. 2014. Extensive heterosis in 967 growth of yeast hybrids is explained by a combination of genetic models. Heredity 968 113:316-326.
969 Shivaprasad PV, Dunn RM, Santos BACM, Bassett A, Baulcombe DC. 2012. 970 Extraordinary transgressive phenotypes of hybrid tomato are influenced by 971 epigenetics and small silencing RNAs. The EMBO Journal 31:257-266.
972 Smukowski Heil CS, DeSevo CG, Pai DA, Tucker CM, Hoang ML, Dunham MJ. 973 2016. Selection on heterozygosity drives adaptation in intra- and interspecific 974 hybrids. bioRxiv.
975 Stelkens RB, Brockhurst MA, Hurst GDD, Greig D. 2014. Hybridization facilitates 976 evolutionary rescue. Evolutionary Applications 7:1209–1217.
977 Stelkens RB, Brockhurst MA, Hurst GDD, Miller EL, Greig D. 2014. The effect of 978 hybrid transgression on environmental tolerance in experimental yeast crosses. 979 Journal of Evolutionary Biology 27:2507-2519.
980 Stelkens RB, Seehausen O. 2009. Genetic distance between species predicts novel 981 trait expression in their hybrids. Evolution 63:884-897.
982 von Janowsky B, Major T, Knapp K, Voos W. 2006. The Disaggregation Activity of 983 the Mitochondrial ClpB Homolog Hsp78 Maintains Hsp70 Function during Heat 984 Stress. 357:793-807.
985 Willett CS. 2012. Hybrid breakdown weakens under thermal stress in population 986 crosses of the copepod Tigriopus californicus. Journal of Heredity 103:103-114.
987 Zörgö E, Gjuvsland A, Cubillos FA, Louis EJ, Liti G, Blomberg A, Omholt SW, 988 Warringer J. 2012. Life History Shapes Trait Heredity by Accumulation of Loss-of- 989 Function Alleles in Yeast. Molecular Biology and Evolution 29:1781-1789. 990
37 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.
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
38 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.
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
39 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.
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
40 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.
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
41 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.
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
42 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.
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
43 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.
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.
44 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.
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.
45