the effect of spatially varying selection in panmixia: more
TRANSCRIPT
The Genetic Consequences of Spatially Varying Selection in the Panmictic American Eel 1
(Anguilla rostrata) 2
3
Pierre-Alexandre GAGNAIRE*, Eric NORMANDEAU*, Caroline CÔTÉ*, Michael Møller 4
HANSEN** and Louis BERNATCHEZ* 5
6
*Institut de Biologie Intégrative et des Systèmes (IBIS), Département de Biologie, Université 7
Laval, Pavillon Charles-Eugène-Marchand, Québec G1V 0A6, Canada. 8
**Department of Biological Sciences, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus 9
C, Denmark 10
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Genetics: Published Articles Ahead of Print, published on December 14, 2011 as 10.1534/genetics.111.134825
Copyright 2011.
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Running title: Spatially varying selection in panmixia 14
15
Key words: G×E interactions, genome scan, Levene’s model, polymorphisms’ stability, 16
spatially varying selection. 17
18
Corresponding Author: 19
PierreAlexandre GAGNAIRE 20
Institut de Biologie Intégrative et des Systèmes (IBIS), Département de Biologie, Université 21
Laval, Pavillon CharlesEugèneMarchand, 1030 Avenue de la Médecine, Québec G1V 0A6, 22
Canada. 23
Phone: 14186563402 24
Fax: 14186567176 25
Email: pierre[email protected] 26
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ABSTRACT 29
30
Our understanding of the genetic basis of local adaptation has recently benefited from the 31
increased power to identify functional variants associated with environmental variables at the 32
genome scale. However, it often remains challenging to determine whether locally adaptive 33
alleles are actively maintained at intermediate frequencies by spatially varying selection. 34
Here, we evaluate the extent to which this particular type of balancing selection explains the 35
retention of adaptive genetic variation in the extreme situation of perfect panmixia, using the 36
American eel (Anguilla rostrata) as a model. We first conducted a genome scan between two 37
samples from opposite ends of a latitudinal environmental gradient using 454 sequencing of 38
individually tagged cDNA libraries. Candidate SNPs were then genotyped in 992 individuals 39
from 16 sampling sites at different life stages of the same cohort (including larvae from the 40
Sargasso Sea, glass eels and 1 year old individuals) as well as in glass eels of the following 41
cohort. Evidence for spatially varying selection was found at 13 loci showing correlations 42
between allele frequencies and environmental variables across the entire species range. 43
Simulations under a multipleniche Levene’s model using estimated relative fitness values 44
among genotypes rarely predicted a stable polymorphic equilibrium at these loci. Our results 45
suggest that some geneticbyenvironment interactions detected in our study arise during the 46
progress toward fixation of a globally advantageous allele with spatially variable effects on 47
fitness. 48
49
50
4
INTRODUCTION 51
52
Variable environmental conditions across species’ ranges provide a basis for differential 53
selection at polymorphic loci involved in local adaptation. In consequence, the level of locally 54
adaptive genetic variation may be potentially increased, through a particular type of balancing 55
selection whereby protected polymorphisms result from selection for different alleles in 56
different environments. Depending on population structure, the degree of habitat choice and 57
the strength of selection, this process can lead to habitat specialization and eventually to 58
ecological speciation (MAYNARD SMITH 1966). However, when both dispersal across habitats 59
and mating are random processes, local adaptation is impossible and polymorphism may be 60
either lost by drift, or under special conditions, protected by selection (YEAMAN and OTTO 61
2011). This evolutionary mechanism has first been investigated more than half a century ago 62
(LEVENE 1953), through a local density regulation model integrating variation in fitness of 63
genotypes across niches, and differential contribution of the niches to a panmictic 64
reproductive pool. Levene demonstrated that a sufficient condition for a locally adaptive 65
polymorphism to be maintained by selection requires the harmonic mean fitness of the 66
heterozygote genotype to be higher than that of each homozygote, a process called “harmonic 67
mean overdominance”. 68
There is an increasing body of empirical evidence for cases of polymorphisms maintained 69
by environmental heterogeneity (reviewed by HEDRICK et al. 1976; HEDRICK 1986; HEDRICK 70
2006).The most famous examples come from studies of allozyme variation (e.g. KREITMAN 71
1983; SEZGIN et al. 2004), color polymorphism (NACHMAN et al. 2003; HOEKSTRA et al. 72
2004), adaptation to climate (HANCOCK et al. 2008; KOLACZKOWSKI et al. 2011) and soil type 73
(TURNER et al. 2010), as well as pathogen and insecticide resistance (GARRIGAN and HEDRICK 74
2003; PELZ et al. 2005; WEILL et al. 2003). Since habitat choice or reduced gene flow 75
5
increase the opportunity for the maintenance of locally adaptive polymorphisms in subdivided 76
populations (FELSENSTEIN 1976), these cases are usually more fully understood under 77
migrationselection models. However, the framework of Levene’s model remains highly 78
relevant to the study of species for which random mating and dispersal exist over large (e.g. 79
marine fishes and invertebrates) or local spatial scales (e.g. sympatric host races of insects). 80
To date, the principal limitation to evaluating the retention of locally adaptive alleles in 81
such species has been the lack of genomic resources. Only two studies which focused on one 82
or two selected genes have empirically tested the maintenance of polymorphism under 83
Levene’s model, in the leafhopper (PROUT and SAVOLAINEN 1996) and the acorn barnacle 84
(SCHMIDT and RAND 2001). In practice, the discovery of locally adaptive polymorphisms in 85
panmixia is not straightforward for several reasons. Firstly, there might be a negative tradeoff 86
between the number of loci influenced by spatially varying selection and individual locus 87
effects on fitness, such that the whole adaptive load due to selection on unlinked loci remains 88
sustainable for the population. Secondly, environmental changes can shift the frequency of 89
some protected variants out of their domain of stability, resulting in the loss of formerly stable 90
polymorphisms. Thirdly, recombination should rapidly erase the effects of selection on the 91
chromosomal neighborhood of the selected sites (CHARLESWORTH et al. 1997; PRZEWORSKI 92
2002). As such, partial selective sweeps provoked by the establishment of new protected 93
variants should only leave transient genomic footprints, further reducing the chance to find 94
them. Nevertheless, these difficulties can be partly overcome by tracking protected 95
polymorphisms using a highdensity genome scan approach. Owing to the development of 96
highthroughput sequencing techniques, this strategy is now achievable in most nonmodel 97
species (STAPLEY et al. 2010). 98
The American eel Anguilla rostrata is one of the most appropriate organisms for studying 99
the evolutionary effects of spatially varying selection within Levene’s model framework 100
6
(KARLIN 1977). Mating of the whole species occurs in the Sargasso Sea (SCHMIDT 1923), 101
after which planktonic larvae are dispersed by the Antilles Current and the Gulf Stream to the 102
eastern North American coast over a large continental habitat, extending from Florida to 103
Québec and Labrador (TESCH 2003). Studies based on neutral molecular markers have 104
showed that in this textbook example of panmixia, random mating occurs at the species scale 105
(AVISE et al. 1986; WIRTH and BERNATCHEZ 2003). Evidence from the literature also supports 106
that leptocephali larvae passively drift with the currents (BONHOMMEAU et al. 2010) and that, 107
following metamorphosis, newly transformed unpigmented glass eels use a selective tidal 108
stream transport mechanism to move landward (MCCLEAVE and KLECKNER 1982). Thus, 109
genotypedependant habitat choice is unlikely to occur over a large geographical scale due to 110
oriented horizontal swimming and newly recruited glass eels are exposed to highly 111
unpredictable conditions with respect to environmental parameters (e.g. temperature, salinity, 112
pathogens and pollutants) during their early life history. Consistent with these observations, 113
clinal variation attributed to singlegeneration footprints of spatiallyvarying selection was 114
found at three allozyme loci (WILLIAMS et al. 1973; KOEHN and WILLIAMS 1978). However, 115
allozyme studies only focused on few metabolic genes and did not assess the retention of 116
locally adaptive polymorphisms by spatially varying selection. Population genomics now 117
offers powerful tools to bring empirical data to bear on this fundamental question in 118
ecological genetics. Here, we discovered and typed annotated Single Nucleotide 119
Polymorphisms (SNPs) in transcribed regions of the American eel genome to identify 120
candidate genes potentially associated with environmental variables. An extensive 121
spatiotemporal set of samples was then used to further test for selection at candidate loci, 122
estimate nichespecific relative fitness among genotypes and investigate conditions for the 123
maintenance of polymorphism under a finite population Levene’s model. 124
125
126
7
MATERIAL AND METHODS 127
128
Preparation of cDNA libraries, contig assembly and SNP discovery: 129
130
We prepared cDNA libraries for 454 sequencing following the protocol described in 131
PIERRON et al. (2011). Two samples of 20 glass eels were collected just prior to settlement in 132
freshwater at two river mouths located near the extreme ends of the species’ latitudinal range: 133
the lower St. Lawrence estuary (RB, 48°78’N 67°70’W) and Florida (FL, 30°00’N 81°19’W). 134
Briefly, PolyA RNAs were individually extracted from entire glass eels and used as template 135
for cDNA amplification. Amplified cDNA were then fragmented by sonication, and 136
fragments from 300 to 800 bp were ligated to the standard 454 B primer and the standard 454 137
A primer holding a 10 bp barcode extension at its 3’ end. Therefore, each individual could be 138
identified by its unique barcode. For each sampling site, the 20 individually tagged libraries 139
were pooled in equal amounts and sequenced on a halfplate of Roche GSFLX DNA 140
Sequencer at Genome Quebec Innovation Center (McGill University, Montreal, Canada). 141
Base calling was performed using PyroBayes (QUINLAN et al. 2008) after trimming 142
adapters. Each read was then renamed according to its individual barcode, which was 143
subsequently removed together with potential primers used for cDNA amplification. We 144
performed a de novo assembly of the whole sequencing data using CLC Genomic Workbench 145
3.7 (CLC bio), with a minimal read length fraction of 0.5 and a similarity parameter of 0.95. 146
The consensus sequence of each de novo built contig was then used as template for a 147
reference assembly under the same parameters. This second round of assembly aimed at 148
screening for additional reads which were not included into contigs during the step of de novo 149
assembly, and excluding poor quality contigs which did not recruit any read during the 150
reference assembly procedure. 151
8
SNP discovery was performed using the Neighborhood Quality Standard (NQS) algorithm 152
(ALTSHULER et al. 2000; BROCKMAN et al. 2008) implemented in CLC Genomic Workbench 153
3.7 (CLC bio). This method takes into account the base quality values in order to distinguish 154
sequencing errors from actual SNPs. We set a minimum coverage of 20× per SNP site and 155
used either a frequency threshold of 5% or a count threshold of 5 for the rarest variant (when 156
the coverage exceeded 100×) in order to avoid the detection of sequencing errors as SNPs. 157
Only biallelic SNPs were considered. 158
159
Individual genotype inference: 160
161
There is a significant risk to misscore a heterozygote genotype by repeatedly sampling the 162
same allele when the individual coverage is below 5× (see Figure S1). We corrected such 163
artefactual heterozygote deficiencies by supposing withinsample HardyWeinberg 164
equilibrium (HWE) while taking into account the stochasticity induced by the binomial 165
sampling process of homologous sequences at each locus for each individual. For each SNP 166
having more than 10 individuals sequenced in each sample (i.e. total coverage ≥ 20×), allele 167
counts were used to determine the observed genotype of each individual (𝐴𝐴, 𝐴𝑎, 𝑎𝑎, or NA 168
when no sequence data was available) in order to calculate the observed allelic frequencies. 169
We then supposed withinsample HWE to estimate the number of expected individuals within 170
each genotypic class in each sample given the number of individuals sequenced and the 171
observed allelic frequencies in the sample. When the observed number of heterozygotes was 172
below HWE predictions, new genotypes that were consistent with observed individual data 173
were randomly drawn from a trinomial distribution with event probabilities 174
(𝑃(𝐴𝐴)&,(; 𝑃(𝐴𝑎)&,(; 𝑃(𝑎𝑎)&,() corresponding to the probabilities of each genotype, given the 175
observed data for the jth individual in sample i. For each locus showing HW deficiency, a new 176
9
array of individual genotypes was generated until HWE expectations were verified for the 177
sample. The individual genotype probabilities used to parameterize the trinomial sampling 178
process were obtained from the following equation giving the probabilities of real genotypes 179
(𝐺+) knowing the observed data (𝐺,) at a given locus: 180
181
𝑃(𝐺+|𝐺,)&,( = /𝑃(𝐴𝐴)&,(; 𝑃(𝐴𝑎)&,(; 𝑃(𝑎𝑎)&,(0 =
⎝
⎜⎜⎜⎜⎛
456
456745(8945):6(;5,<=:) 0 0
45(8945):6(;5,<=:)
456745(8945):6(;5,<=:) 1 45(8945):6
(;5,<=:)
(8945)6745(8945):6(;5,<=:)
0 0 (8945)6
456745(8945):6(;5,<=:) ⎠
⎟⎟⎟⎟⎞× 𝐺,&,( 182
183
where 𝑁&,( is the number of reads (i.e. individual coverage) of individual j in sample i, 𝐺,&,( is 184
its observed genotype (with 𝐺,(𝐴𝐴) = E100F, 𝐺,(𝐴𝑎) = E
010F and 𝐺,(𝑎𝑎) = E
001F ), and 𝑝& is the 185
frequency of the 𝐴 allele in sample i. Under this procedure, the genotype of an observed 186
heterozygote was never modified, whereas observed homozygotes could be probabilistically 187
assigned to heterozygotes. Since the sequencing error rate was already taken into account by 188
the SNP detection method, it was neglected at this step to simplify the approach. 189
Methodological validation performed on simulated datasets showed that our correction 190
efficiently restored up to 50% of the hidden heterozygotes (see Figure S1). 191
192
Outlier detection: 193
194
Individual genotypes obtained after treating for the heterozygote deficiency bias were used 195
to detect SNPs potentially affected by diversifying selection between the two samples RB and 196
FL. The empirical distribution of pairwise FST as a function of withinsamples heterozygosity 197
was compared to a neutral distribution simulated under a symmetrical twoisland model 198
10
assuming near random mating (BEAUMONT and NICHOLS 1996) using ARLEQUIN ver. 3.5 199
(EXCOFFIER and LISHER 2010). This approach is more conservative than drawing random 200
samples from a single panmictic population to derive the neutral distribution. For each outlier 201
locus (i.e. FST value located above the 99.5% quantile of the simulated distribution), the 202
contig’s consensus sequence was blasted against the nonredundant NCBI protein database 203
(nr) using BLASTX with an Evalue threshold of 105 (ALTSCHUL et al. 1997). 204
205
SNP genotyping: 206
207
Individual SNP assays were developed using the KBiosciences Competitive Allele208
Specific PCR genotyping system (KASPar). For each candidate contig, we targeted the SNP 209
showing the highest FST value when possible. We also developed assays for SNPs identified 210
within contigs of allozyme coding genes showing clinal variation in WILLIAMS et al. (1973): 211
the Sorbitol dehydrogenase gene (SDH), two Phosphoglucose isomerase isoforms (PGI1 and 212
PGI2) and the Alcohol dehydrogenase gene (ADH3). Our validation panel was finally 213
completed with nonoutlier SNPs to 100 markers. All assays were tested with 80 individuals 214
and only successfully genotyped SNPs were retained for subsequent genotyping. 215
A total of 992 individuals belonging to four distinct sample categories were genotyped 216
(Table 1): (i) A reference sample of the 2007 cohort (before selection) consisting of 48 young 217
leptocephali larvae collected in the Sargasso Sea soon after hatching in March and April 2007 218
(SAR7) during the Galathea III expedition (MUNK et al. 2010); (ii) The first wave of recruiting 219
glass eels belonging to the 2007 cohort, collected between January and July 2008 at 16 river 220
mouths distributed from Florida to Quebec (GLASS8); (iii) One year old individuals from the 221
2007 cohort, sampled between February and June 2009 from four localities previously 222
sampled in 2008, ranging between South Carolina and Quebec (OYO9); (iv) Glass eels 223
11
belonging to the 2008 cohort, collected in 2009 at five river mouths distributed from South 224
Carolina to Quebec (GLASS9) and that were also sampled in 2008 for the 2007 cohort. 225
226
Statistical analyses: 227
228
We tested for HWE at each diploid locus within each of the four eel sample categories 229
using ARLEQUIN ver. 3.5 (EXCOFFIER and LISHER 2010). We corrected for multiple 230
independent tests using the false discovery rate correction (α = 0.05). Multilocus global FST 231
values among localities within sample categories were estimated and tested through 10000 232
permutations. Outlier SNPs were searched based on their level of genetic differentiation 233
among localities within categories as well as between pairs of localities using coalescent 234
simulations under a symmetrical island model assuming near random mating. 235
For each locus, statistical associations between allelic frequencies and a set of four 236
explanatory variables (sample category, latitude, longitude, temperature) were assessed 237
through logistic regressions using the R package glmulti (CALCAGNO and DE MAZANCOURT 238
2010). Temperature data were obtained from a NOAA database containing georeferenced 239
seasurface temperatures along North America’s coastlines (SST14NA), with a nominal 240
spatial resolution of 14 km and a 48h update frequency. More precisely, we took the sea241
surface temperature at river mouth averaged across the 10 days preceding the sampling date in 242
each locality, which corresponded to recruitment at river mouths for the GLASS8 category. 243
Because the exact date of arrival at river mouths was not known for the two other categories 244
of samples, we used different temperature criteria: the three winter months (DecFeb) average 245
river mouth temperature was used for the OYO9 category, and the sampling month average 246
river mouth temperature was used for the GLASS9 category (Table 1). All possible models 247
involving the four explanatory variables (including pairwise interactions) were fitted using 248
12
samples from the three continental categories (GLASS8, OYO9 and GLASS9), and the best 249
model was identified using a Bayesian Information Criterion (BIC). Because the best 250
geographical coverage was achieved for the 2008 glass eels, the same approach was also 251
performed based on samples from the GLASS8 category only. For each SNP found in 252
association with explanatory variables, individual haplotype information was retrieved from 253
454 sequencing data and used to evaluate between sites linkage disequilibrium (LD) using the 254
method for partially phased haplotypes in Haploview v4.2 (BARRETT et al. 2005). 255
The multilocus spatial component of genetic variability at loci inferred to be influenced by 256
spatially varying selection was determined using the spatial Principal Component Analysis 257
method (sPCA, JOMBART et al. 2008) implemented in the R package adegenet_1.22 258
(JOMBART 2008). The sPCA includes spatial information in the analysis of genetic data, which 259
helps to reveal subtle global spatial structures such as geographic clines. The spatial proximity 260
network among localities was built based on the neighborhood by distance method. An abrupt 261
decrease of the eigenvalues obtained by decomposing the genetic diversity from the spatial 262
autocorrelation was used as a criterion to choose the principal component to interpret. 263
264
Evolution under Levene’s model: 265
266
The classical one locus two allele model of Levene (1953) was extended by the addition 267
of a genetic drift component. At each generation, mating occurs in panmixia, followed by 268
random dispersal of genotypes across niches. Selection is a nichespecific process in which 269
the frequency of allele A before selection in the ith niche, noted 𝑞&, passes to 𝑞&′ after selection 270
following the equation: 271
𝑞&J =𝑊&𝑞&L + 𝑞&(1 − 𝑞&)
𝑊&𝑞&L + 2𝑞&(1 − 𝑞&) + 𝑉&(1 − 𝑞&)L
13
Where 𝑊& and 𝑉& respectively denote the fitness of the homozygote genotypes AA and aa 272
relative to that of the heterozygote genotype in the ith niche. Genetic drift is then modeled by 273
randomly drawing 𝑁R × 𝐶& genotypes in each niche from a trinomial distribution with event 274
probabilities: 275
/𝑃(𝐴𝐴)& = 𝑞&JL; 𝑃(𝐴𝑎)& = 2𝑞&J(1 − 𝑞&J); 𝑃(𝑎𝑎)& = (1 − 𝑞&J)L0
The new frequency of allele A after selection and drift in the ith niche is noted 𝑞&JJ, and since 𝐶& 276
corresponds to the relative contribution of niche i to the global reproductive pool of effective 277
size 𝑁R, the frequency of allele A equals ∑ 𝐶&& 𝑞&JJ in the next mating pool. 278
In order to test for equilibrium under Levene’s model, empirical values of 𝑊& and 𝑉& were 279
estimated from the observed genotypic frequencies in the SAR7 larval pool (𝑓VV; 𝑓VW; 𝑓WW) and 280
the modeled nichespecific genotypic frequencies after selection (𝑓VV5X;𝑓VW5X;𝑓WW5X) following: 281
𝑊& =YZZ5X
YZ[YZZ YZ[5X
and 𝑉& =Y[[5X
YZ[Y[[ YZ[5X
282
Where the ratios YZZ5X YZ[5X
and Y[[5X YZ[5X
were predicted by the regression models of YZZX
YZZX7YZ[X and 283
Y[[5XY[[5X
7YZ[X using the observed genotypic frequencies in the GLASS8 samples and the 284
explanatory variables previously selected for this category (see Results). For each locus 285
inferred to be influenced by spatially varying selection, the 16 estimated pairs of (𝑊&;𝑉&) were 286
used to parameterize a 16 niche Levene’s model in which the allelic frequencies observed in 287
SAR7 were used as starting values. Different distributions of the 𝐶& were explored, from 288
uniform to normally distributed outputs among niches, and the population effective size 289
parameter was set between 104 and 106 to assess genetic drift effects. 290
291
292
14
RESULTS 293
294
Sequence assembly and SNP discovery: 295
296
A total of 292.6 Mb of sequences were obtained from the two halfruns of 454 GSFLX 297
pyrosequencing, among which were 482322 reads from the St. Lawrence estuary sample (RB, 298
mean read length of 296 bp) and 495482 reads from the Florida sample (FL, mean read length 299
of 303 bp). These sequences were deposited in the NCBI sequence read archive SRA045712. 300
Trimming adapters and individual barcodes and then filtering for sequence quality removed 301
5.3% of the reads from the RB dataset and 5.1% for FL. Processed reads were assembled into 302
22093 contigs with an average length of 464 bp. In silico SNP detection allowed identifying 303
70912 putative SNPs, 13293 of which were retained after filtering for a minimal coverage of 304
10 reads from at least 10 different individuals in both samples RB and FL (i.e. total coverage 305
≥ 20×). This filtering step allowed inferring 78.1% of the 265860 genotypes (20 individuals × 306
13293 SNPs) in sample RB and 79.7% in sample FL. 307
308
Candidate SNP detection and genotyping: 309
310
A total of 163 outlier SNPs with estimated FST values ranging from 0.167 to 0.637 were 311
detected (see Figure S2). However, for most candidate SNPs exhibiting the highest FST 312
values, BLAST searches revealed the presence of reads matching alternative copies of 313
duplicated genes within contigs. These false SNPs, which probably reflected differential 314
expression patterns of paralogous genes (principally myosin isoforms) between samples RB 315
and FL, were removed from subsequent analyses. 316
15
After these filtering procedures, our validation panel included 57 outlier SNPs and was 317
completed to 100 markers with nonoutlier SNPs selected across the full range of 318
heterozygosity. Successful genotyping was obtained for 73 out of these 100 SNPs, 70 of 319
which were functionally annotated using BLASTX (see Table S1) and 44 were outliers from 320
the initial screen. The genotyping success rate across all samples and loci was above 98%, 321
KASPar primers used for genotyping are provided Table S2. 322
323
SNP variation patterns: 324
325
Only one locus departed significantly from HWE expectations within the SAR7 category, 326
whereas 15, 7 and 10 loci showed significant HW disequilibrium within the continental 327
categories GLASS8, OYO9 and GLASS9, respectively (see Table S3). Overall multilocus FST 328
values calculated among locality samples were not significantly different from zero within 329
each of the three continental categories (GLASS8: FST = 0.0003, p = 0.318; OYO9: FST = 330
0.0015, p = 0.171; GLASS9: FST = 0.0022, p = 0.060). 331
Logistic regressions between allelic frequencies and explanatory variables based on the 332
dataset containing the three continental categories (GLASS8, OYO9 and GLASS9) revealed 333
contrasting patterns across loci. For 61 out of 73 SNPs, all possible models involving the 334
explanatory variables and their pairwise interactions were rejected. However, significant 335
associations were detected for 10 loci and marginally significant associations for 2 loci (Table 336
2). The same approach performed within the GLASS8 category alone revealed statistical 337
associations for 8 loci, all but MDH being already detected in the analysis including all 338
continental samples (Table 2). Among the 13 loci for which significant associations were 339
found, 11 were also detected in outlier tests due to atypically high FST values ranging from 340
0.052 to 0.175 between some pairs of localities. After removing these 13 loci from the dataset, 341
16
the global multilocus FST values calculated among locality samples became null in two 342
continental categories (GLASS8: FST = −0.0015, p = 0.981; OYO9: FST = −0.0007, p = 0.647) 343
and was reduced in the third one (GLASS9: FST = 0.0016, p = 0.151). 344
Most selected regression models revealed significant interactions between spatial variables 345
and river mouth temperature (Table 2), which was measured at different time scales for the 346
three continental categories (Table 1). River mouth temperature at recruitment was selected as 347
the best model for one locus in the GLASS8 category (AcylCarrier Protein, ACP, Figure 1a). 348
In order to identify more precisely when this parameter was the most biologically relevant for 349
this locus, we used a sliding window analysis to test if the correlation could be improved 350
using temperature data from different time periods surrounding glass eels recruitment. We 351
found that the correlation was rapidly lost when the 10 days window used to calculate the 352
average seasurface temperature was shifted around the period corresponding to recruitment 353
(Figure 1b). Furthermore, because the timing of glass eels’ recruitment varied considerably 354
across sampling locations, river mouth temperatures were neither correlated with latitude (R² 355
= 0.113, p = 0.203) nor with longitude (R² = 0.036, p = 0.484) during the recruitment period 356
(Table 1). 357
Multivariate analysis of the eight loci significantly associated with explanatory variables in 358
the GLASS8 category showed that most of the variability was explained by the first principal 359
component, since the first eigenvalue of the sPCA was highly positive (Figure 2, upper right). 360
The global structure illustrated by individual lagged scores on the first principal component 361
showed a synthetic latitudinal cline (Figure 2), corresponding to the multilocus spatial 362
component of genetic variation at the eight loci inferred to be under spatially varying 363
selection. A logistic regression of locality scores against latitude (R² = 0.76, p < 0.0001) 364
showed that the center of the cline coincides with the coastal zone where the latitudinal 365
gradient of nearshore seasurface temperature is the strongest over the sampling period. 366
17
Moreover, river mouth temperature averaged across the whole sampling period was a better 367
predictor of locality scores than latitude (R² = 0.88, p < 0.0001). A highly similar synthetic 368
latitudinal cline was obtained when analyzing the three continental categories together (see 369
Figure S3), supporting the temporal stability of the observed pattern. 370
One of the 13 SNPs associated with explanatory variables was a nonsynonymous 371
polymorphism (Nucleolar Complex Protein 2, NCP2), whereas for 9 of the 12 other SNPs, at 372
least one nonsynonymous segregating site was identified within a 1kb region (see Figure S4). 373
Heterozygosity within the 13 contigs usually followed a monotonical trend and substantial 374
levels of linkage disequilibrium (r² > 0.5) were sometimes found between remote SNPs. 375
These results suggest that the indirect influence of selection through linkage with a nearby 376
functional mutation was more likely than direct selection at the focal SNPs. However, this 377
should not introduce any bias in the estimation of the relative fitness values used in the 378
following simulations. 379
380
Assessment of polymorphism stability under Levene’s model: 381
382
Simulating the evolution of allelic frequencies for the eight loci significantly associated 383
with explanatory variables in the GLASS8 category led to two different predictions. Under the 384
hypothesis of uniform contribution among niches and an effective population size (Ne) of 105, 385
simulations predicted polymorphism stability for two SNPs and allele fixation for the 386
remaining six loci (Table 3). The simulated evolution of allelic frequencies over generations 387
as generated by the model can be found for each of the eight loci Figure S5. Frequency at 388
equilibrium was fairly close to that measured in the SAR7 sample for the two SNPs predicted 389
to be protected by spatially varying selection. Concerning the six transient polymorphisms, 390
allele fixation was generally reached within less than 80 generations. Most importantly, the 391
18
invading allele was always the derived state after identifying the ancestral allele through 392
BLASTN search. 393
The results obtained under different assumptions on the relative contributions among 394
niches and population size did not radically change these predictions, as the same two 395
protected polymorphisms were repeatedly inferred across scenarios. However, estimating the 396
nichespecific relative fitness directly from the observed genotypes frequencies in the 397
GLASS8 category (i.e. instead of using values predicted by the regression models) increased 398
the number of protected polymorphisms from two to four (see Table S4). 399
400
DISCUSSION 401
402
Evidence for singlegeneration footprints of spatially varying selection: 403
404
Our results provide strong indications that young glass eels colonizing different areas of 405
the species range are exposed to differential patterns of selection, resulting in significant shifts 406
in allele frequencies within a short timescale. The alternative hypothesis of a subtle neutral 407
population genetic structure in the American eel was not supported by previous works, since 408
panmixia has never been rejected based on neutral markers (AVISE et al. 1986; WIRTH and 409
BERNATCHEZ 2003). This conclusion was reiterated here based on 60 neutral SNPs genotyped 410
over 944 individuals distributed across three temporal categories. Moreover, a neutral pattern 411
imposed by spatially restricted gene flow is not consistent with the finding that 85% (11 out 412
of 13) of the loci associated with explanatory variables were also detected as outliers, and that 413
different regression models were selected across these markers. Consequently, our data do not 414
support the existence of a spatial population structure due to deviation from panmixia in A. 415
rostrata. Alternatively, passive genotypespecific habitat choice could possibly occur if the 416
19
genes associated with environmental variables underlie differences in leptocephalus stage 417
duration. However, the observation that earlymetamorphosing leptocephali preferentially 418
recruit to the center of the continental distribution range, whereas latemetamorphosing larvae 419
mostly settle in northern and southern locations (WANG and TZENG 1998) is inconsistent with 420
the clinal multilocus spatial component detected at these loci (Figure 2). Moreover, half of 421
these loci displayed HWE deviations in continental samples but not in the larval sample, 422
which is incompatible with the habitat choice hypothesis. Therefore, we propose that spatially 423
varying selection is the most parsimonious mechanism underlying the observed patterns of 424
genetic variation. 425
The diversity of the statistical models retained to explain genetic variation across selected 426
loci may suggest the implication of different locusspecific selective factors. Without detailed 427
information on such agents, the choice of spatial and temperature variables as proxies for the 428
ecological conditions experienced by eels was justified, as for instance several environmental 429
factors covary with latitude along the North Atlantic coasts (SCHMIDT et al. 2008). 430
Covariation between latitude and spatially varying selective factors has previously been used 431
to illustrate multilocus spatial patterns attributed to selection in heterogeneous environment in 432
Drosophila melanogaster (SEZGIN et al. 2004). Here, we additionally used this synthetic 433
multilocus signal to demonstrate the overall temporal stability of the observed patterns 434
(Figure S3). Owing to the apparent panmixia and the lack of evidence for largescale habitat 435
choice in the American eel, any genetic pattern left by spatially varying selection at a given 436
generation will be inevitably erased at the next generation. Consequently, the temporal 437
stability of the observed genetic patterns between the GLASS8 and GLASS9 categories 438
probably reflects the repeated action of similar natural selection pressures in the two 439
consecutive year cohorts covered by this study. Given that only a very small proportion 440
(<0.5%) of the larvae survive until glass eels reach the coasts and that the glass eel survival 441
20
rate is about 10% (BONHOMMEAU et al. 2009), our sampling scheme was designed with the 442
intent to detect changes in allelic frequencies occurring during the early stages of eels’ life 443
cycle. Moreover, we sampled the first wave of early recruiting glass eels before potential 444
settlement cues may affect upestuary migration depending on individual condition and 445
temperature (SULLIVAN et al. 2009). 446
Although we used variables that mirror continental factors better than openocean factors, 447
a decoupling between river mouth and nearshore continental shelf seasurface temperatures in 448
the northeastern part of the species range (localities GAS, NF and PEI, Table 1) may have 449
influenced our results. Glass eels recruiting in this region during early summer first face cold 450
water temperatures while crossing the continental shelf before entering warmer estuary waters 451
influenced by river outflows. Differential mortality during the crossshelf transport may thus 452
have resulted in the selection of explanatory models involving interactions between river 453
mouth temperature and latitude or longitude, as observed for six loci out of eight in the 454
GLASS8 category. This interpretation is further supported by the close correspondence 455
between the multilocus spatial variation component and the nearshore averaged seasurface 456
temperature pattern in figure 2. Although it suggests that the continental shelf sea surface 457
temperature should have been used in the regression analyses, this variable remains too 458
difficult to measure without knowing the trajectories of eels during the crossshelf transport. 459
Using hydrodynamic models for backtracking larval transport may thus help in selecting 460
additional meaningful variables in future studies. Admittedly, as in any other study of this 461
type, our approach cannot fully capture the signal of all spatially varying selection pressures, 462
and probably underestimates the number of genes under spatially varying selection. On the 463
other hand, the strong association found at locus AcylCarrier Protein (ACP) between allele 464
frequencies and temperature at recruitment (Figure 1) shows that the river mouth temperature 465
21
is a relevant variable. Indeed, settlement in estuaries is a critical period during which glass 466
eels do not feed (SULLIVAN et al. 2009) and probably live on their fatty acids reserves. 467
Three of the five allozymes previously studied (WILLIAMS et al. 1973) were included in 468
our analysis to assess whether clines observed at the protein level could be detected at the 469
DNA level. The SNP developed for the Malate dehydrogenase gene (MDH), which was also 470
detected as outlier in our initial 454 transcriptome scan, was the only one to show a significant 471
association with environmental variables. In the allozyme study, however, genetic 472
heterogeneity at this locus was only observed among samples of adults and not at the glass eel 473
stage. The lack of significant pattern for the SNPs developed at the ADH and PGI loci may be 474
due to problems of paralogy or to a lack of LD with the SNPs under selection. 475
476
The fate of selected polymorphisms under Levene’s model: 477
478
Covariation between environmental variables such as temperature and the direction and 479
strength of selection have been suspected for a long time to actively maintain polymorphisms 480
in heterogeneous environments (reviewed by HEDRICK et al. 1976; KARLIN 1977). Depending 481
on the overall sum of local selective effects, spatially varying selection can however lead to 482
two different outcomes: (i) balanced selection for different alleles in different environments 483
can maintain polymorphism’s stability over generations, while (ii) globally unbalanced local 484
effects of directional selection may lead to allelic fixation (LEVENE 1953). Recent theoretical 485
developments have shown that substantial multilocus polymorphism can be maintained under 486
Levene’s model, in particular when locally advantageous alleles are partially dominant 487
(BÜRGER 2010). This includes cases of local dominance that specifically arise with enzymes 488
when fitness is a concave function of the activity level while the heterozygote’s enzymatic 489
activity is intermediate to that of both homozygotes (GILLESPIE and LANGLEY 1974). 490
22
Here, equilibrium was tested through simulations under Levene’s model in order to 491
account for combinations of parameters leading to nontrivial evolution of allelic frequencies 492
within a finite population. This approach is relevant since the underlying conditions of 493
Levene’s model perfectly fit the American eel’s life cycle, which is characterized by random 494
mating and dispersal, and a local density regulation (VOLLESTAD and JONSSON 1988). While 495
exploring a realistic range of parameter values, stable equilibrium was predicted at only two 496
out of eight tested loci. This relatively low proportion may be partly explained by 497
uncertainties due to the methodological approach. For instance, a lack of precision in the 498
estimation of fitness parameters, but also in the relative outputs among niches, will obviously 499
affect the realism of the simulations (SCHMIDT and RAND 2001). Here, we only considered 16 500
river mouths among a much greater number of existing rivers harboring the American eel 501
along the North Atlantic coast. Yet, those sampling locations are distributed evenly across the 502
species range and are therefore representative of the variation in selection direction and 503
intensity potentially encountered by glass eels. By considering only the earliest life stages, we 504
may fail to catch differential selection acting later in the life cycle. While we cannot rule it 505
out, the fact that most of the mortality occurs before entering freshwater (BONHOMMEAU et al. 506
2009) reduces this possibility. Moreover, it is likely that selection acting at later life stages 507
plays on different sets of genes. Finally, successive waves of recruiting glass eels can face 508
different conditions depending on their date of arrival (i.e. seasurface temperature), and inter509
annual global variations in the selective parameters may also exist. The natural settings are 510
thus likely more complex than considered in the model. However, it has been showed that 511
temporal variation in selection is less efficient in maintaining locally adaptive polymorphisms 512
compared to spatially varying selection (EWING 1979). 513
Thus, it appears plausible that the low proportion of protected polymorphisms truly reflects 514
the relatively restrictive conditions required for equilibrium (LEVENE 1953). When a locally 515
23
advantageous allele appears by mutation and successfully escapes random loss when rare, it 516
has more chance to invade the panmictic gene pool and to become fixed than to stabilize at an 517
intermediate, stable frequency. Because the transitory phase to fixation will often last for a 518
few hundreds of generations, loci which are undergoing incomplete selective sweeps may not 519
be easily discovered unless they are frequent enough to be detected with a genome scan 520
approach. In populations with a large census size, however, new adaptive mutations can 521
frequently occur (KARASOV et al. 2010) and may result in selective sweeps if the overall 522
effects of spatially varying selection are unbalanced. The finding that, in our simulations, the 523
invading allele was always a derived state for each of the six predicted unstable SNPs 524
supports the hypothesis of linkage with such a globally advantageous mutation which has not 525
already reached fixation. 526
Incomplete sweeps are expected to leave a specific pattern in the haplotype structure. 527
Because the derived allele increasing in frequency has an atypically longrange LD compared 528
to neutral ancestral variants segregating at the same frequency (SABETI 2006; VOIGHT et al. 529
2006), the measure of LD can be used to detect ongoing directional selection. Here, sequence 530
information retrieved from 454 sequencing data was insufficient to perform such tests based 531
on the haplotype structure, which require phased haplotypes extending outside the selected 532
gene. However, measuring LD based on available information within contigs revealed the 533
existence of substantial linkage (r² > 0.5) between some sites which are likely separated by a 534
few kilobases if the presence of introns is taken into account. 535
536
Implications for adaptation and conservation of American eel: 537
538
The Gene Ontology (GO) molecular functions of the genes inferred to be involved in G×E 539
interactions mostly encompassed major metabolic functions, among which are found: lipid 540
24
metabolism (ANX2: inhibition of phospholipase A2; ACP: acyl carrier activity; GPX4: 541
phospholipidhydroperoxide glutathione peroxidase activity), saccharide metabolism (MDH: 542
malate dehydrogenase activity; UGP2: UDPglucose pyrophosphorylase activity) and protein 543
biosynthesis (EIF3F: translation initiation factor; PRP40: PremRNAprocessing activity). 544
The best predictive models of all these genes included temperature, a factor known to have a 545
strong influence on the level of metabolism in the American eel (WALSH et al. 1983). 546
Moreover, the center of the synthetic multilocus latitudinal cline coincided with the region 547
where the warm waters of the Gulf Stream drift away from the coasts. Although the American 548
eel occupies a wide latitudinal range, its thermal preferendum is rather elevated for the 549
temperate zone, since glass eels have a highly reduced swimming ability below 7° 550
(WUENSCHEL and ABLE 2008), elvers optimally grow at 28° (TZENG et al. 1998), and yellow 551
eels stop feeding and become metabolically depressed below 10° (WALSH et al. 1983). 552
Therefore, selective effects are logically expected at the relatively low temperatures locally 553
encountered between metamorphosis and recruitment to estuaries, although phenotypic 554
plasticity may also account for the wide range of temperature tolerance in A. rostrata 555
(DAVERAT et al. 2006). 556
Two genes involved in defense response were also detected (CST: cysteine endopeptidase 557
inhibitor activity; SN4TDR: nuclease activity). Since selective factors related to pathogen 558
exposure do not always correlate with temperature or geographic coordinates, other genes 559
whose variation patterns could not be explained with our set of explanatory variables may 560
also play a role in resistance to pathogens in A. rostrata. For instance, the innate immune 561
response gene TRIM35 showed strong departure to HWE in glass eels and atypically high 562
levels of genetic differentiation between some localities (FST values up to 0.174). In parallel, 563
simulating the evolution of allelic frequencies at this locus based on observed genotype 564
frequencies predicted a stable equilibrium (result not shown). This observation warrants 565
25
further investigation, especially since the TRIM35 gene cluster, which is located in a region 566
of significantly elevated nucleotide diversity in the threespine stickleback (Gasterosteus 567
aculeatus), is also a candidate target of balancing selection in this species (HOHENLOHE et al. 568
2010). 569
In conclusion, we have screened more than 13000 SNPs in transcribed regions of the 570
American eel genome and identified several genes undergoing spatially varying selection 571
associated with the highly heterogeneous habitat used by this species. Due to our 572
methodological approach, however, the number of genes involved in G×E interactions has 573
likely been underestimated, and the causative agents of selection remain partially unknown. 574
Nevertheless, the higher proportion of transient versus stable polymorphisms suggests that 575
locally adaptive polymorphisms are not easily maintained by spatially varying selection when 576
local adaptation is impossible. Under such conditions, theory predicts that phenotypic 577
plasticity, by broadening the environmental tolerance of individual genotypes, provides a 578
more functionally adaptive response to spatial environmental variation (SULTAN and SPENCER 579
2002). Indeed, the costs induced by selection on locally adaptive traits are particularly severe 580
in the case of random mating and in the absence of habitat choice (LENORMAND 2002). For 581
eels, as for other highly fecund marine species facing huge mortality rates during larval 582
stages, phenotypic plasticity may represent the main mechanism for coping with habitat 583
heterogeneity (EDELINE 2007), and our results suggest that differential expression of 584
paralogous genes may be involved in this regulation. Nevertheless, the finding of locally 585
selected mutations spreading to fixation in A. rostrata suggests that this high census size 586
species may be regularly subject to new locally adaptive mutations. How the recent 587
population decline of Atlantic eels (WIRTH and BERNATCHEZ 2003) affects their adaptability 588
to changing environments is still poorly understood and will be a matter of further 589
investigations. 590
26
591
Acknowledgments: We are grateful to M. Castonguay for his help in sample collection and 592
to R. MacGregor as well as anonymous referees and Associate Editor D. Begun for their 593
constructive and helpful comments on the manuscript. We also thank F. Pierron, M.H. 594
Perreault, V. Bourret and G. Côté for their precious help with molecular experiments and C. 595
Sauvage for help with bioinformatic analyses. This research was founded by a grant from 596
Natural Science and Engineering Research Council of Canada (NSERC, Discovery grant 597
program) as well as a Canadian Research Chair in Genomics and Conservation of Aquatic 598
Resources to L.B. P.A.G. was supported by a Government of Canada PostDoctoral 599
Fellowship. M.M.H. was supported by the Danish Council for Independent Research | Natural 600
Sciences (grant 09072120). 601
602
Author Contributions: L.B. and P.A.G. designed research; P.A.G. performed research; C.C., 603
M.H., E.N., P.A.G. contributed reagents/materials/analysis tools; P.A.G. Analyzed data; and 604
P.A.G., E.N., M.H. and L.B. wrote the paper. 605
606
607
27
FIGURES AND TABLES 608
609
Figure 1: Correlation between river mouth temperature and allele frequencies at locus ACP. 610
Logistic regression based on all 3 continental categories (a.). Allele frequencies in the 611
GLASS8 category are represented with black squares, OYO9 with light gray circles, and 612
GLASS9 with dark gray triangles. Sliding window analysis of the coefficient of determination 613
(R²) between allele frequencies at locus ACP in the GLASS8 category and the values predicted 614
based on river mouth temperature data (b.). For each day within a 3 month period centered on 615
the sampling date (which also corresponded approximately to the date of arrival at river 616
mouths), surface temperature was taken as the mean value across the 10 previous days. 617
618
619
620
621
28
Figure 2: Synthetic multilocus spatial variation component in the 2008 glass eels. The spatial 622
component analysis was based on genetic variation at the 8 loci significantly associated with 623
explanatory variables in the GLASS8 category. The 16 sampling sites are represented on the 624
map by squares colored according to each locality’s lagged score on the first principal 625
component, as indicated in the caption. Seasurface temperatures averaged across the whole 626
sampling period (from Jan. 08th to Jul. 16th 2008) are represented on the same color scale for 627
indication (purple: 0.2°, red: 27.3°). The plot on the right shows the shape of the synthetic 628
multilocus cline, as well as the decomposition of the product of the variance and the spatial 629
autocorrelation into positive, null and negative components (upper right corner). The clinal 630
structure corresponds to the highly positive eigenvalue in red. 631
632
633
634
635
29
Table 1: Sampling location and date, sample size, developmental stage, and river mouth 636
temperature for each analyzed sample. 637
638
639
640
641
30
Table 2: Models selected for 13 loci associated with explanatory variables, for both the 642
GLASS8 dataset and the 3 continental categories. 643
644
645
646
Table 3: Simulated evolution of allelic frequencies under Levene’s model for the 8 loci 647
statistically associated with explanatory variables in the GLASS8 category. Uniform 648
contribution among niches and a population effective size of 105 were assumed in these 649
simulations. 650
651
652
653
31
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