New Results

A polygenic architecture with conditionally neutral effects underlies ecological differentiation in Silene

Susanne Gramlich, Xiaodong Liu, Adrien Favre, C. Alex Buerkle, Sophie Karrenberg
doi: https://doi.org/10.1101/2021.07.06.451304

Summary

  • Ecological differentiation can drive speciation but it is unclear how the genetic architecture of habitat-dependent fitness contributes to lineage divergence. We investigated the genetic architecture of cumulative flowering, a fitness component, in second-generation hybrids between Silene dioica and S. latifolia transplanted into the natural habitat of each species.

  • We used reduced-representation sequencing and Bayesian Sparse Linear Mixed Models (BSLMMs) to analyze the genetic control of cumulative flowering in each habitat.

  • Our results point to a polygenic architecture of cumulative flowering. Allelic effects were mostly beneficial or deleterious in one habitat and neutral in the other. The direction of allelic effects was associated with allele frequency differences between the species: positive-effect alleles were often derived from the native species, whereas negative-effect alleles, at other loci, tended to originate from the non-native species.

  • We conclude that ecological differentiation is governed and maintained by many loci with small, conditionally neutral effects. Conditional neutrality may result from differences in selection targets in the two habitats and provides hidden variation upon which selection can act. Polygenic architectures of adaptive differentiation are expected to be transient during lineage divergence and may therefore be unrelated to high genetic differentiation at the underlying loci.

Introduction

Adaptation to different habitats can promote divergence and speciation and can mediate population persistence under changing conditions (Nosil, 2012; Savolainen et al., 2013). Evolutionary trajectories toward adaptive differentiation depend on the number and effect sizes of loci controlling fitness, on allelic effects of such loci in alternative habitats and on gene flow (Savolainen et al., 2013; Tigano & Friesen, 2016; Kokko et al., 2017). However, despite recent progress in the theoretical understanding of adaptation (Yeaman, 2015; Mee & Yeaman, 2019; Booker et al., 2021), empirical studies on the genetic control of fitness in natural habitats is still scarce (Wadgymar et al., 2017). Here we provide such a study using two ecologically differentiated but hybridizing campions (Silene).

Adaptive differentiation is often depicted as a classic cartoon with populations outperforming other populations in their home habitat but not in the foreign habitat (Kawecki & Ebert, 2004). A similar pattern has been proposed to operate at the genetic level: alleles with positive effects on fitness in one habitat have negative effects in the alternative habitat, termed antagonistic pleiotropy (Anderson et al., 2011; Savolainen et al., 2013). Empirical data, however, suggests that such antagonistic pleiotropy is rare in natural populations, although this could partially be caused by limited statistical power (Anderson et al., 2011; Anderson et al., 2014; Wadgymar et al., 2017). An alternative pattern, conditional neutrality, where alleles have neutral effects in one environment and are under positive or negative selection in the other, may be much more prevalent (Anderson et al., 2011; Savolainen et al., 2013; Wadgymar et al., 2017). In fact, even at the phenotypic level, populations often perform differently in one habitat but not in the other (Leimu & Fischer, 2008). Importantly, loci with antagonistic pleiotropy for fitness in alternative habitats are expected to be maintained by natural selection and contribute to population differentiation even with high gene flow, whereas conditionally neutral loci can only lead to transient allele frequency differences in high-gene flow scenarios (Mitchell-Olds et al., 2007; Savolainen et al., 2013; Tiffin & Ross-Ibarra, 2014; Mee & Yeaman, 2019; Booker et al., 2021). If adaptive allele frequency changes are predominantly transient, even in the presence of consistent selection, prospects will be poor for finding loci underlying adaptive differentiation with commonly used methods for quantifying and comparing genetic differentiation along the genome (i.e., genome scans of differentiation).

A further major determinant of adaptive differentiation concerns the number and effect sizes of the underlying loci. In general, the distribution of effect sizes for complex traits (including fitness) is expected to be exponential (Orr, 1998, 2005) such that adaptation is mainly due to many loci with small effects, while large-effect loci, although not generally unimportant, are rare (Orr, 1998; Orr, 2005; Rockman, 2012; Savolainen et al., 2013; Boyle et al., 2017; Selby & Willis, 2018). A large number of segregating loci underlying adaptation further promotes transient genetic architectures of adaptation (Yeaman & Whitlock, 2011; Yeaman, 2015; Booker et al., 2021). Polygenic or even omnigenic genetic architectures, involving most of the genome, render identification of all individual loci both practically impossible and undesirable (Rockman, 2012). As effect sizes decrease and the number of loci increases, it becomes progressively more difficult to detect individual genotype-phenotype associations and to distinguish them from spurious associations due to linkage. For this reason, promising methods, such as Bayesian Sparse Linear Mixed Models (BSLMMs), identify genotype-phenotype associations of all loci simultaneously rather than individually, assume polygenic genetic architectures and remove effects that can be attributed to linkage disequilibrium (Zhou et al., 2013; Gompert et al., 2017). The approach has been successfully applied in studies on adaptive divergence, for example in pine (Lind et al., 2017) and in Arabidopsis (Exposito-Alonso et al., 2019).

In this study, we investigate the genetic basis of differential adaptation in two dioecious sister species of Silene (Caryophyllaceae), Silene dioica (L.) Clairv. and S. latifolia Poiret. Demographic models indicate that the two species diverged with gene flow within the last 120 000 years reaching a neutral sequence divergence (Da, autosomes) of 0.0027 and genetic differentiation, FST, of 0.28 (Hu & Filatov, 2015; Liu et al., 2020). The pink-flowered S. dioica occurs in moister and colder habitats like meadows, pastures or forests and occupies a wide range of elevations up to more than 2300 m a. s. l., while the white-flowered S. latifolia is found on drier, warmer and more disturbed sites like dry meadows, arable fields or road sites at elevations up to 1000 m a. s. l. (Friedrich, 1979; Karrenberg & Favre, 2008). Ecological differentiation and assortative pollination constitute strong barriers to gene flow in this species pair, but the two species are still fully cross-fertile (Goulson & Jerrim, 1997; Karrenberg et al., 2019). Studies in an experimental garden point to a complex, genome wide genetic architecture of traits associated with reproductive isolation, such as flower color, flowering phenology, first-year flowering and specific leaf area in this system (Liu & Karrenberg, 2018).

Here we investigated the genetic architecture of adaptation using recombinant second-generation hybrids (F2) from a multi-site field transplant experiment where the two species exhibited strong evidence of habitat adaptation: each species outperformed the other in its own habitat in terms of flowering and survival over four years (Fig. 1, Favre et al., 2017). We focused on the following questions: (1) What is the genetic architecture underlying differential habitat adaptation? (2) Is antagonistic pleiotropy or conditional neutrality the predominant pattern when comparing allelic effects across habitats? and (3) Are fitness effects associated with allele frequency differences between the two species; for example, are beneficial alleles more likely to be derived from the native species?

Figure 1.
Figure 1.

Habitat adaptation in campions (Silene): cumulative flowering (number of times flowered over four years, plus 1 if the plant survived to the end of the experiment), a fitness component, in crosses within each of two campion species, Silene dioica (SD) and S. latifolia (SL), as well as in their first-generation hybrids (F1D with SD mother, F1L, SL mother) and second-generation hybrids (F2) in the habitat of each species, (a) SD habitat and (b) SL habitat; least square means and 95% confidence intervals are given and different letters indicate significant differences between cross types within each habitat. This data is published in Favre et al. (2017 and re-analyzed here for illustration (see text).

Materials and Methods

Transplant experiment, measurements and sampling

Second-generation (F2) hybrids between Silene dioica (L.) Clairv. and Silene latifolia Poir., derived from 36 F0 individuals of three populations of each species, were transplanted as juveniles into four natural sites, two in each species’s habitat, as part of a larger experiment with six sites (Favre et al., 2017, Supporting Information Tables S1, S2, and S3). Sites of S. dioica were situated at higher altitudes with a colder climate and shorter growing season as compared to the S. latifolia sites (Supporting Information Table S1). Leaf samples were collected prior to transplantation and silica-dried and we selected four of the six sites for this study based on availability and quality of leaf samples. Flowering and survival were assessed over four years and cumulative flowering was calculated as the number of times an individual flowered plus 1 if it survived to the end of the experiment, for detailed analyses of survival and flowering see Favre et al. (2017). We sampled 4-6 F2 individuals from each of 18 F2 families at each of the four selected sites (298 F2 individuals in total), striving to include both high and low fitness individuals from each family and site. To assess allele frequency in the two species we further included samples of 32 F0 individuals and used existing data for the remaining four F0 individuals (Liu & Karrenberg, 2018, Supporting Information Table S3).

For illustration purposes (Fig. 1), we re-analyzed cumulative flowering in the two species and their first- and second-generation hybrids for the four sites used here, (18-36 families per cross type, 5-20 individuals per family, 5 blocks per site, Favre et al. 2017, Supporting Information Table S2). We used linear mixed models of cumulative flowering in each habitat with cross type as a fixed factor and family and block nested in site as random factors using the lme4 package (Bates et al., 2015) for R version 4.02 (R Core Team, 2020). We extracted least square means of cumulative flowering and performed multiple comparisons between cross types within sites with Holm correction of P-values in the emmeans package (Lenth, 2020). Cumulative flowering was log(Y+1)-transformed to yield normally distributed residuals and improve model fit. Means and standard errors are reported back-transformed to the original scale.

DNA extraction and sequencing

We extracted genomic DNA from silica-dried leaf tissue with Qiagen’s DNeasy Plant Mini Kit (Qiagen, Germany) and quantified DNA using a Qubit dsDNA HS fluorometer (Life Technologies, Sweden). Double-digest RAD sequencing (ddRAD-seq) libraries were prepared with EcoRI and TaqαI restriction enzymes as described in Liu and Karrenberg (2018). After enzymatic digestion, DNA fragments were ligated with barcoded adaptors and size-selected to approximately 550 bp (Peterson et al., 2012). In total, eight 48-plex libraries were sequenced on an Illumina HiSeq 2500 system at the SNP&SEQ technology platform of SciLifeLab, Uppsala, Sweden using 125-bp paired-end chemistry and two libraries per lane. F0 individuals were included in 2 libraries to achieve higher coverage.

Bioinformatic analysis - processing of raw reads and variant filtering

The total sequencing output was 1,382,838,294 reads for 298 F2 individuals (mean with one standard error: 4,656,021 ± 233,639 reads) and 461,127,142 reads for 32 F0 individuals (mean: 14,410,223 ±1,093,409 reads); data on the remaining four F0 individuals (Supplementary Information Table S3) was available from (Liu & Karrenberg, 2018). We processed the ddRAD-sequence reads following the dDocent pipeline (Puritz et al., 2014). After de-multiplexing of raw reads using stacks 2.0b (Catchen et al., 2013) and trimming with fastp (Chen et al., 2018), we used BWA MEM 0.7.17 with default parameters (Li, 2013) to map reads to ddRAD-seq-generated reference contigs, which were previously assembled from eight deeply sequenced individuals of both species and hybrids and corresponded to 95,040,562 bp in total, corresponding to approximately 3.4% of the S. latifolia genome (Liu & Karrenberg, 2018; Liu et al., 2020). Only a partial genome sequence (one third of the 2.8 Gbp genome) with short scaffolds (N50 = 10,785 bp) is currently available for S. latifolia (Krasovec et al., 2018).

Variants were called with FreeBayes 1.1.0 (Garrison & Marth, 2012) without population priors using the following parameters: minimum mapping quality 30, minimum base quality 20, maximum complex gap 3, minimum repeat entropy 1, binominal-obs-priors 1, and use-best-n-alleles 10. Variants were filtered following O’Leary et al. (2018): First, VCFtools 0.1.15 (Danecek et al., 2011) was used to retain SNP sites with a minimum depth of 3, quality of 30, mean depth of 10 and allele count of 3. Secondly, we used Vcffilter implemented in vcflib/2017-04-04 (https://github.com/vcflib/vcflib) to retain sites with an allele balance either between 0.25 and 0.75 or lower than 0.01, a quality/depth ratio of > 0.25, and a mapping quality ratio between 0.9 and 1.05. We further used Vcffilter to remove loci with differences in read pairing between the alleles or with or excessive read depths (parameters as suggested in O’Leary et al., 2018). We reduced the dataset to bi-allelic sites and removed 6 F2 individuals with more than 99% missing data. SNPs in perfect linkage disequilibrium (r2 = 1) within the F2 individuals were removed using PLINK 1.9 (Purcell et al., 2007, http://pngu.mgh.harvard.edu/purcell/plink/). The filtered dataset contained 290 F2 individuals (S. dioica-habitat: 134, S. latifolia-habitat: 156) and 89’524 loci. Of these, 42’090 loci with both alleles in both habitats and genotypes for more than 95 F2 individuals in each habitat were used for further analyses. These loci had an average read depth of 15.02 ± 0.03 (median 13.83) for the F2 individuals, 36.85 ± 0.08 (median: 33.09) for the 32 F0 individuals sequenced in this study and 41.39 ± 0.17 (median: 36.25) for the 4 F0 individuals from Liu and Karrenberg (2018). We used genotype probabilities ranging from 0 to 2, calculated from genotype likelihoods (https://github.com/visoca/popgenomworkshop-gwas_gemma/tree/master/scripts/bcf2bbgeno.pl, accessed 28 January 2019), rather than called genotypes (Nielsen et al., 2011). Genotype probabilities of 0 and 2 denote homozygosity for the reference and alternative allele, respectively, while 1 indicates heterozygosity.

Genetic structure

We checked for genetic structure in the F2 individuals using Principal Components Analysis (PCA) with the ade4 package (Dray and Dufour, 2007), based on genotype probabilities. For this analysis we reduced the dataset to 220 SNP loci with data available in all 290 F2 individuals used in the genetic association analysis. Results did not differ from analyses on more or all loci with missing values replaced by average genotype probabilities.

Genetic association analysis

We used Bayesian Sparse Linear Mixed Models (BSLMM) in GEMMA 0.98.1 (Zhou et al., 2013) to investigate the genetic architecture of cumulative flowering. BSLMMs are a combination of linear mixed models, which assume that every variant has an effect, and Bayesian Variable Selection Regression, which assumes that only a small proportion of the variants has an effect (Zhou et al., 2013). BSLMM analyses were performed separately for each habitat using cumulative flowering values standardized within sites and genotype probabilities. A centered relatedness matrix was used as a covariate to take account of the family structure and the wide cross, according to the standard BSLMM method implemented in GEMMA (Zhou et al., 2013).

We characterized genetic architectures using estimates of the following hyperparameters: the proportion of phenotypic variance explained by all SNPs in the model (PVE), the proportion of PVE explained by SNPs with nonzero effects (PGE), and the number of SNPs with a measurable effect on the phenotype (n-γ). In studies with incomplete genome coverage such as this one, PVE can be interpreted as broad sense heritability H2, whereas the product of PVE and PGE can be interpreted as narrow-sense heritability h2 (Zhou et al., 2013; Gompert et al., 2017; Bresadola et al., 2019). For each SNP locus, we estimated the posterior inclusion probability (PIP, the proportion of iterations in which a SNP had a nonzero effect on phenotypic variation) and the effect size of the alternative allele on cumulative flowering. We report both raw effect estimates Embedded Image, the effect of a locus on the phenotype when it is included in the model, and model-averaged effect estimates Embedded Image that take into account the posterior inclusion probability of a locus in the models (Zhou et al., 2013; Gompert et al., 2017). BSLMMs were run five times with 10,000,000 burn-in steps and 40,000,000 iterations with a thinning interval of 10. Convergence of the five runs was checked graphically.

Hyperparameters were estimated after combining posterior distributions across runs; PIP, Embedded Image and Embedded Image (per locus) were averaged across runs. A threshold of PIP > 0.01 was used to identify SNPs with sparse effects (Gompert et al., 2013; Comeault et al., 2014).

Allelic effects and allele frequencies

We graphically evaluated whether alleles have universal effects or display antagonistic pleiotropy or conditional neutrality by plotting per-locus effect sizes (Embedded Image and Embedded Image) in the S. dioica habitat against effect sizes in the S. latifolia habitat. Note that β-values reflect effect estimates of the alternative allele replacing the reference allele. The de novo ddRADseq reference sequences used here contain alleles derived from both species (Liu & Karrenberg, 2018; Liu et al., 2020) and do not inform on allelic origin.

To analyze whether allelic origin is associated with fitness effects, we calculated the frequency of the alternative allele (AFalt) in the 18 F0 individuals of each species (Tables S2 and S3) for loci that had genotypes for > 12 F0 individuals per species at individual read depths of at least 6 (36,161 loci). For each SNP locus, we expressed AFalt as the average genotype probability per species divided by 2. We further estimated the allele frequency difference between species (AFDalt) as AFalt (S. dioica) - AFalt (S. latifolia). AFDalt values thus range from −1 (alternative allele fixed in S. latifolia F0 individuals, and reference allele fixed in S. dioica F0 individuals) to 1 (alternative allele fixed in S. dioica F0 individuals, and reference allele fixed in S. latifolia F0 individuals). We tested whether AFDalt differs between loci with positive and negative effects on cumulative flowering in each habitat using a general permutation test in the R package coin (Hothorn et al., 2008).

Method validation

We assessed the power of BSLMMs to detect genotype - phenotype associations in our data using a simulation approach similar to that in Gompert et al. (2017). Phenotypes were simulated on the basis of the observed genetic data in 290 individuals for four combinations of heritability (h2 = 0.2 or h2 = 0.05) and number of functional variants (N = 10 or N = 50). We simulated a normally distributed trait for which nine individuals of each family were randomly drawn. We reduced the simulated dataset to 150 individuals at random to match the sample size in our data. For each combination of h2 and n 30 sets of simulated phenotypes were used as input for BSLMMs with 10,000,000 burn-in steps and 40,000,000 iterations. For each simulation, PVE, PGE and n-γ were estimated as well as the correlation between the simulated effect size and effect size estimates (Embedded Image and Embedded Image).

Results

Cumulative flowering and genetic structure

Each species outperformed the other in its own habitat and performed best in its habitat in terms of cumulative flowering, providing strong evidence for habitat adaptation (Fig. 1, Favre et al., 2017). F1 hybrids were intermediate for cumulative flowering and F2 hybrids flowered less often than F1 hybrids on average (Fig. 1, Favre et al., 2017).

The F2 families exhibited extensive genetic variation in the PCA, with clustering of individuals within families but without any other emerging patterns (Supplementary Information Figs. S1 and S2). For most loci, the alternative allele occurred at low frequency in one or both species, but all combinations of allele frequencies between species were found in the data, including loci with high differentiation where the alternative allele was at high frequency in one species and at the same time near absent from the other species (Fig. 2).

Figure 2.
Figure 2.

Frequencies of the alternative allele (AFalt) in two campion species, Silene dioica (SD) and S. latifolia (SL) for 42’090 loci used in an association study: (a) distribution of alleles frequencies in SD, (b) distribution of allele frequencies in SL and (c) SD allele frequency (y-axis) against SL allele frequency (x-axis) with allele frequency differences (AFDalt) between the two species (SD - SL) indicated as a color gradient from red (allele fixed in SD and absent in SL) to white (equal frequency in both species) to blue (allele fixed in SL and absent in SD).

Association analysis

The proportion of phenotypic variation explained (PVE) by BSLMMs was 0.12 and 0.10 for the S. dioica and the S. latifolia habitat, respectively (medians of posterior distributions, Table 1, posterior distributions are shown in the Supporting Information Figs. S3 and S4). Less than half of the PVE could be attributed to sparse effects, leading to estimates of narrow-sense heritability (h2; PGE × PVE) of 0.05 and 0.03 for the S. dioica and S. latifolia habitat (Table 1). The number of loci with non-zero effects (n-γ) was estimated to be 11 in the S. dioica habitat and 16 in the S. latifolia habitat (Table 1). Median PIP for individual SNPs was 0.001 in both habitats and we detected 3 loci with PIP > 0.01, one in the S. dioica habitat and two in the S. latifolia habitat (Supporting information Table S4). PIP for individual SNPs could not be summed over genomic windows because no continuous reference genome is available (Krasovec et al., 2018) and we used a ddRADseq-generated de novo reference in this study (Liu & Karrenberg, 2018; Liu et al., 2020).

View this table:
Table 1.

Genetic architecture of cumulative flowering, a fitness component, in recombinant F2 hybrids between the campions Silene dioica and S. latifolia in a field transplant experiment at sites of each species; given are medians, means and 90 % equal tail credible intervals (90% cred. int.) of hyperparameters estimated in Bayesian Sparse Linear Mixed Models (BSLMMs): PVE (proportion variation explained, broad-sense heritability, H2), PGE (proportion variation explained by measurable effects), h2 (narrow-sense heritability, PVE * PGE) and n-γ (number of loci with measurable effects). Posterior distributions of hyperparameters are shown in Supplementary Figures S3 and S4.

Allele specific effects in alternative habitats and origin of alleles

Raw effect size estimates Embedded Image for cumulative flowering (in units of standard deviations, i.e., z-scores) ranged from −0.57 to 0.28 in the S. dioica habitat and from −0.52 to 0.34 in the S. latifolia habitat. Positive or negative allelic effects in one habitat (Embedded Image >> 0 or Embedded Image << 0) were associated with very low Embedded Image values (Embedded Image ≈ 0) in the other habitat, consistent with conditional neutrality (Fig. 3, cross pattern). A single locus showed opposing effects in the two habitats, Embedded Image << 0 in the S. dioica habitat and Embedded Image >> 0 in the S. latifolia habitat, suggestive of antagonistic pleiotropy (Fig. 3). Analyses for model-averaged effect sizes, Embedded Image, exhibited a very similar pattern (Supporting information Fig. S5), but with much smaller values due to multiplication with low posterior inclusion probabilities.

Figure 3.
Figure 3.

Locus-specific effect sizes of the alternative allele on cumulative flowering, a fitness component, measured in recombinant hybrids between two campion species, Silene dioica (SD) and S. latifolia (SL) that were transplanted into the habitat of each species (SD habitat, y-axis; SL habitat, x-axis). Given are raw effect estimates, Embedded Image, from Bayesian Sparse Linear Mixed Models (BSLMMs). Allele frequency differences for the alternative allele (AFDalt) between the two species (SD-SL) are indicated as a color gradient from blue (allele fixed in SL and absent in SD) to white (equal frequency in both species) to red (allele fixed in SD and absent in SL). Points are plotted in the order of increasing absolute AFDalt, such that highly differentiated loci are most visible. Loci with evidence for antagonistic pleiotropy would be have Embedded Image-values of opposite signs in two habitats (top left, and bottom right), whereas loci consistent with conditional neutrality lie at zero on one axis, but deviate substantially from zero on the other.

A striking pattern in our data is that alleles with effects on cumulative flowering were significantly associated with higher allele frequencies in F0 individuals of the native species whereas alleles with negative effects on cumulative flowering were significantly more likely to be derived from the non-native species (Fig. 3, Supporting Information Figs. S5, S6 and S7). In the S. dioica habitat, loci with positive effects of the alternative allele on cumulative flowering had a positive median AFDalt (alternative allele more common in S. dioica, red on Fig. 3), that were significantly different from the negative median AFDalt (alternative allele more common in S. latifolia, blue on Fig. 3) at negative-effect alleles (Supporting Information Figs. S6 and S7). In the S. latifolia habitat, we observed the reverse pattern: loci with positive effects had negative median AFDalt values that differed significantly from the positive median AFDalt values for negative-effect loci (Supporting information Figs. S6 and S7). These patterns were observed for both Embedded Image and Embedded Image, when using a subset of the approximately 0.5 % loci with strongest effect sizes (absolute values of Embedded Image over 0.1 and absolute values of Embedded Image over 0.003) and when using all loci (i.e., negative-effect loci with Embedded Image or Embedded Image > 0 and positiveeffect loci with Embedded Image or Embedded Image < 0, Supporting Information Figs. S6 and S7).

Alleles with positive effects on cumulative flowering in each habitat (Embedded Image > 0.1) were rare in the foreign species but at appreciable frequencies in the native species; this effect was stronger in the S. latifolia habitat than in the S. dioica habitat (Fig. 4). Loci with negative effects (Embedded Image < −0.1), in contrast, were rare in the native species but at moderate or high frequencies in the foreign species (Fig. 4). These allele frequency patterns across species differ strongly from the overall allele joint allele frequency pattern for all loci (Fig. 2c).

Figure 4.
Figure 4.

Frequencies of the alternative allele (AFalt) in two campion species, Silene dioica (SD, y-axis) and S. latifolia (SL, x-axis) for loci associated with cumulative flowering, a fitness component, in the habitat of each species. Shown are loci with absolute raw effect sizes, Embedded Image, over 0.1 (in standard deviation units) in Bayesian Sparse Linear Mixed Models (BSLMMs) with the number of loci (N): Embedded Image −0.1 in the SD habitat (negative selection), Embedded Image > 0.1 in the SD habitat (positive selection), Embedded Image < −0.1 in the SL habitat (negative selection), and Embedded Image > 0.1 in the SL habitat (positive selection). Allele frequency differences for the alternative allele (AFDalt) between the two species (SD-SL) are indicated as a color gradient from blue (allele fixed in SL and absent in SD) to white (equal frequency in both species) to red (allele fixed in SD and absent in SL). Positive selection is associated with native alleles, whereas alleles under negative selection are more common in the non-native species (see also text and Supporting Information Figs. S6 and S7).

Method validation

BSLMM estimates of narrow-sense heritability (h2) in simulated datasets responded to changes in simulated heritabilities, as expected (Fig. 5). BSLMMs on simulated data with a low h2 of 0.05 recovered h2 estimates in the correct range, while models on simulated data with a high h2 of 0.2 yielded lower than expected h2 estimates, especially when the number of sparse-effect loci was high (N = 50, Fig. 5). The number of sparse-effect loci was correctly estimated for simulated datasets with few loci (N = 10), but strongly underestimated when the simulated number of loci was high (Fig. 5). Correlations of simulated and estimated Embedded Image and Embedded Image-values were highest for datasets with high simulated heritabilities and declined for datasets with lower heritability and a higher number of functional loci (Fig. 5, Supporting Information Fig. S8). In simulations with h2 = 0.02, Embedded Image exhibited higher correlations with simulated effects sizes than Embedded Image (Supporting Information Fig. S8).

Figure 5.
Figure 5.

Performance analysis of Bayesian Sparse Linear Mixed Models (BSLMM) on simulated data based on a dataset from campions (Silene, 150 individuals, 42’090 loci). Simulated phenotypes had narrow-sense heritabilities (h2) of 0.05 or 0.20 with 10 or 50 functional loci (N); 30 replicate simulations per scenario were used and boxplots or violin plots summarize variation in medians of posterior distributions of hyperparameters among simulation replicates as well as of correlations of simulated and estimated per-locus effect sizes. (a) percent variation explained (PVE, interpreted as broad-sense heritability, H2), (b) narrow-sense heritability h2 (as PVE * PGE [PGE, proportion of sparse effects in PVE]), (c) number of sparse-effect loci (n-γ and (d), correlation of raw estimated effect sizes for individual loci Embedded Image with simulated effect sizes βsim. On (b) and (c) simulated values are indicated with red lines.

These simulations thus show that our analyses can recover heritabilities and general patterns in effect size, however, estimates of the number of sparse effect loci (n-γ) must be treated with great caution. The consistency between estimated and simulated h2 for datasets with low simulated h2 shows that the low h2 on our empirical data are reliable, especially as such low h2 estimates were very rare in the high h2 simulations. We expect that the apparent underestimation of n-γ also decreases posterior inclusion probabilities and thus Embedded Image.

Discussion

Our results suggest that ecological differentiation between the campions Silene dioica and S. latifolia has a polygenic architecture, based on a reciprocal transplant experiment with recombinant second-generation hybrids between the two species. Many alleles had small effects on cumulative flowering, a fitness component, but only in one of the two habitats, conforming to a conditional neutrality pattern. Despite small effect sizes, the direction of allelic effects was consistent with selection for native alleles and against non-native alleles. This suggests that habitat adaptation is maintained by species-specific variants and that habitat dependent selection on hybrids reduces the inflow of non-native alleles. Below we discuss the implications of these findings for lineage divergence as well as the advantages and limitations of our approach.

Low heritability of a fitness component in the field, based on many small-effect loci

Heritability of cumulative flowering, a fitness component, was low to moderate in F2 hybrids between S. dioica and S. latifolia in the habitats of both species. Using Bayesian Sparse Linear Mixed Models (BSLMM) we estimated narrow-sense heritability h2 (PVE × PGE) to be 0.04 and 0.03 and broad-sense heritability H2 (PVE) to be 0.12 and 0.10 for S. dioica and S. latifolia habitats, respectively. Our simulations indicate that such low h2 values can be recovered fairly well with BSLMM analyses, as also reported by Gompert et al. (2017). Similar low to moderate heritabilities have previously been found for complex traits, for example for height in Pinus albicaulis (Lind et al., 2017) and in Populus (Bresadola et al., 2019) as well as in Arabidopsis thaliana accessions, where heritability for fitness components varied strongly between experimental sites (Exposito-Alonso et al., 2019). Our analyses suggest a polygenic genetic architecture of cumulative flowering in Silene with estimates of the number of sparse-effect loci estimated to 11 in the S. dioica habitat and to 16 in the S. latifolia habitat; however, our simulations indicate that the number of sparse effect loci in our dataset is difficult to estimate with BSLMM analyses, similar to results from Gompert et al. (2017). In many other systems, oligo- or polygenic control of fitness components was detected in natural settings, especially when the number of contributing loci was explicitly modelled (Lind et al., 2017; Bresadola et al., 2019; Exposito-Alonso et al., 2019). A polygenic genetic architecture of fitness components in our study system does appear likely given that QTL studies detected many loci distributed throughout the genome that were associated with ecologically relevant traits (Liu & Karrenberg, 2018; Baena□Díaz et al., 2019).

Limitations of studies on the genetic architecture of fitness in natural habitats include the number of individuals and sites that can be studied as well as the genetic resolution in the experimental material. Where markers are used, as in our study, most associations will be the result of linkage to causal variants rather than due to causal variants themselves. This reduces estimates of effect sizes and may lead to a situation where multiple loci linked to the same causal variant are included in alternative BSLMM iterations reducing the posterior inclusion probability (PIP) of each linked locus (Bresadola et al., 2019). We used a highly variable multi-cross F2 population with a limited number of recombination events, where markers likely were associated with larger genomic regions derived from each species. Recombination and genetic variation included in our material differs markedly from other studies using largely homozygous selfing accessions (Arabidopsis, Exposito-Alonso et al., 2019), within-species crosses or recombinant inbred lines derived from a small number of individuals (Arabidopsis, Fournier-Level et al., 2013; Leinonen et al., 2013; Ågren et al., 2017) or naturally recombinant wild-collected hybrids (Populus, Bresadola et al., 2019). These differences can make it difficult to compare results and model performance across studies. Despite its limitations, our setup has the advantage that the multi-cross F2 population used here approximates a natural contact site situation, where early-generation hybrids grow in the habitats of the two species when they come into contact (Karrenberg & Favre, 2008).

Conditional neutrality is the prevailing pattern

Allelic effects conformed to a conditional neutrality pattern in our study: both positive and negative allelic effects on cumulative flowering in one habitat were associated with near-zero effects in the other habitat; only one locus deviated from this pattern. This finding further strengthens the view that conditional neutrality is more common in natural settings than antagonistic pleiotropy, as has been shown in monkeyflowers (Hall et al., 2010) and Arabidopsis (Fournier-Level et al., 2013; Ågren et al., 2017; Exposito-Alonso et al., 2019), switchgrass (Lowry et al., 2019) and Lycaeides butterflies (Gompert et al., 2015). However, strong evidence for conditional neutrality is generally difficult to provide, because this would require evidence for the absence of an effect in one of the habitats (Anderson et al., 2014; Mee & Yeaman, 2019). Interestingly, antagonistic pleiotropy appears to be detected more readily in controlled experimental evolution studies with microorganisms than in natural settings with higher plants or insects (Gompert & Messina, 2016; Bono et al., 2017; Wadgymar et al., 2017; Tusso et al., 2021). This is likely due to the control of selective agents in experiments as opposed to natural sites where both selective agents and traits under selection may vary (Wadgymar et al., 2017). In our system, high-elevation sites of S. dioica exhibit high winter mortality in S. latifolia and in hybrids in comparison to S. dioica (Favre et al., 2017) suggesting that frost tolerance or the regulation of carbohydrate storage could be under selection. At lowland S. latifolia sites, in contrast, S. dioica and hybrids suffer higher summer mortality than the native S. latifolia, most likely due to drought exposure (Favre et al., 2017). We therefore find it plausible that phenotypic and genetic targets of selection differ between habitats and this can contribute to the conditional neutrality pattern of effect sizes for fitness components as observed here.

Fitness is increased by native alleles and decreased by non-native alleles, but not at the same loci

A striking result in our study is the association of allele frequencies in the two species with allelic effects on cumulative flowering, even for loci with very small effects. Alleles with positive effects were rare in the non-native species and common or of intermediate frequency in the native species, especially in the S. latifolia habitat. Alleles with positive effects on cumulative flowering in one habitat were neutral in the alternative habitat and could thus easily spread through both species if not lost by drift (Savolainen et al., 2013; Mee & Yeaman, 2019). Alleles with negative effects on cumulative flowering, on the other hand, had intermediate to high frequencies in the non-native species but were rare in the native species. Negative-effect alleles also included numerous variants that were rare in both species but these loci are most likely linked to causal loci with unknown allele frequencies rather than being causal loci themselves. Negative-effect alleles are most readily interpreted as deleterious load that is only exposed to selection in the alternative habitat and rare, conditionally deleterious variants are expected to arise frequently (Orr, 2005). Conditionally deleterious loci may experience reduced gene flow due to negative selection in the alternative habitat and thereby increase between-lineage differentiation (Mee & Yeaman, 2019). However, rare variants with conditionally deleterious effects likely remain rare even in the lineage they arose in and will thus not exhibit high differentiation between lineages. Variants with conditionally neutral effects, as reported here, likely play an important role for adaptation, but they are most certainly under-reported, not only because many of these variants are rare, but also because studies in alternative and relevant environments are needed to detect them.

Implications for hybrid zones and speciation

Our results suggest that selection favors hybrids that are genetically similar to the native species. In early-generation hybrids with still large genomic regions of each species, as in our experiment, selection will favor genomic regions that contain positive-effect alleles that were more often derived from the native species. At the same time, selection will disfavor regions with negative-effect alleles that were often derived from the non-native species. Both types of conditionally neutral variants thus act as barriers to gene flow in each habitat. In natural hybrid zones between S. dioica and S. latifolia, intermediate individuals are rare and later-generation hybrids are heavily biased toward individuals that are genetically similar to the native species (Minder et al., 2007; Karrenberg & Favre, 2008). Our study suggests that this may be due not only to back-crossing with the native species but also to ecological selection in each habitat.

Regarding the evolution of adaptative differentiation, our results suggest adaptation through selection on many loci in each species. Alleles with small positive effects had intermediate to high frequencies in the native species and low frequencies in the non-native species. This may point to a redundant genetic control of adaptation as also suggested by a QTL study under controlled conditions in our study system (Liu & Karrenberg, 2018) and by studies in other systems, for example in Drosophila (Barghi et al., 2019). Polygenic architectures of adaptation with redundant effects are expected to be transient in time (Yeaman & Whitlock, 2011; Yeaman, 2015). Thus, even when ecological differentiation is a likely driver of divergence, as in our study system (Goulson & Jerrim, 1997; Karrenberg & Favre, 2008; Karrenberg et al., 2019), this may not necessarily manifest in strong, range-wide genetic differentiation at the underlying loci.

Conclusion

Overall, our study adds to the understanding of how ecological differentiation can promote reproductive isolation and lineage divergence. We detected a polygenic architecture for a fitness component, cumulative flowering, in the campions Silene dioica and S. latifolia using a reciprocal transplant experiment. Allelic effects were mostly beneficial or deleterious in one habitat and neutral in the other and native-derived alleles were favored. Conditionally neutral effects may result from differences in selection targets in the two habitat types. Variants with conditionally neutral effects provide hidden variation upon which selection can act and facilitate current and future adaptation. The challenges ahead lie in understanding how within-lineage variation for fitness affecting alleles can be reconciled with the evolution of genome-wide differentiation between species.

Author contribution

The study was designed by SK with input by AF and XL. AF conducted the transplant experiment with help from SK, XL performed molecular lab work, XL and SG did bioinformatic analyses, SG performed the association analysis and simulations with help from AB and SK. SK analyzed phenotypes and produced figures with help from SG. SK and SG wrote the paper with input from all other authors.

Data availability

Double-digest RAD (ddRAD) sequencing data is available on NCBI’s Short Read Archive (SRA, SUB8324195 https://www.ncbi.nlm.nih.gov/sra/PRJNA669447). The variant call format (VCF) file as well as GEMMA input files will be submitted to dryad (project number to be added).

Supporting Information

Figure S1. Principal components analysis including F0 and F2 generations.

Figure S2. Principal components analysis of F2 families.

Figure S3. Posterior distributions of hyperparameters, S. dioica habitat.

Figure S4. Posterior distributions of hyperparameters, S. latifolia habitat.

Figure S5. Model-averaged effect sizes Embedded Image in the S. dioica habitat, plotted against Embedded Image in the S. latifolia habitat.

Figure S6. Permutation test comparing between-species allele frequency differences among positive- and negative-effect alleles, based on raw effect size estimates Embedded Image.

Figure S7. Permutation test comparing between-species allele frequency differences among positive- and negative-effect alleles, based on model-averaged effect size estimates Embedded Image.

Figure S8. Correlations among simulated effects and raw and model-averaged estimates of effect sizes (Embedded Image and Embedded Image).

Table S1. Description of source populations and transplant sites.

Table S2. Crossing scheme.

Table S3. Individuals used for crosses.

Table S4. Sparse-effect loci with posterior inclusion probabilities over 0.01.

Acknowledgements

We thank to Rasmus Janson and Karin Steffen for providing help in the molecular lab. We are grateful for support from the Science for Life Laboratory (SciLifeLab) and the National Genomics Infrastructure, NGI, for massive parallel sequencing. Computations were performed on resources provided by SNIC through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under SNIC projects 2017/7-406 and 2019/8-21. This work was funded by a project grants of the Swiss National Science Foundation (SNF, no. 3100A-118221), the Swedish Research Council (Vetenskapsrådet, no. 2012-03622) and the Carl Tryggers foundation to SK, as well as by a grant of the German Science Foundation (Deutsche Forschungsgemeinschaft, project no. FA1117/1-2) to AF.

References

  1. ↵
    Ågren J, Oakley CG, Lundemo S, Schemske DW. 2017. Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana:Estimates of selection and QTL mapping. Evolution 71(3): 550–564.
    OpenUrl
  2. ↵
    Anderson JT, Lee CR, Mitchell-Olds T. 2014. Strong selection genome-wide enhances fitness trade-offs across environments and episodes of selection. Evolution 68(1): 16–31.
    OpenUrlCrossRefPubMed
  3. ↵
    Anderson JT, Willis JH, Mitchell-Olds T. 2011. Evolutionary genetics of plant adaptation. Trends in Genetics 27(7): 258–266.
    OpenUrlCrossRefPubMedWeb of Science
  4. ↵
    Baena□Díaz F, Zemp N, Widmer A. 2019. Insights into the genetic architecture of sexual dimorphism from an interspecific cross between two diverging Silene(Caryophyllaceae) species. Molecular Ecology 28(23): 5052–5067.
    OpenUrl
  5. ↵
    Barghi N, Tobler R, Nolte V, Jaksic AM, Mallard F, Otte KA, Dolezal M, Taus T, Kofler R, Schlotterer C. 2019. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS Biology 17(2): e3000128.
    OpenUrlCrossRef
  6. ↵
    Bates D, Maechler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67: 1–48.
    OpenUrl
  7. ↵
    Bono LM, Smith LB, Jr.., Pfennig DW, Burch CL. 2017. The emergence of performance trade-offs during local adaptation: insights from experimental evolution. Molecular Ecology 26(7): 1720–1733.
    OpenUrlCrossRef
  8. ↵
    Booker TR, Yeaman S, Whitlock MC. 2021. Global adaptation complicates the interpretation of genome scans for local adaptation. Evolution Letters 5(1): 4–15.
    OpenUrl
  9. ↵
    Boyle EA, Li YI, Pritchard JK. 2017. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell 169(7): 1177–1186.
    OpenUrlCrossRefPubMed
  10. ↵
    Bresadola L, Caseys C, Castiglione S, Buerkle CA, Wegmann D, Lexer C. 2019. Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits. New Phytologist 223(4): 2076–2089.
    OpenUrl
  11. ↵
    Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. 2013. Stacks: an analysis tool set for population genomics. Molecular Ecology 22: 3124–3140.
    OpenUrlCrossRefPubMedWeb of Science
  12. ↵
    Chen S, Zhou Y, Chen Y, Gu J. 2018. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17): i884–i890.
    OpenUrlCrossRefPubMed
  13. ↵
    Comeault AA, Soria-Carrasco V, Gompert Z, Farkas TE, Buerkle CA, Parchman TL, Nosil P. 2014. Genome-wide association mapping of phenotypic traits subject to a range of intensities of natural selection in Timema cristinae. The American Naturalist 183(5): 711–727.
    OpenUrlCrossRefPubMed
  14. ↵
    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, et al. 2011. The variant call format and VCFtools. Bioinformatics 27(15): 2156–2158.
    OpenUrlCrossRefPubMedWeb of Science
  15. ↵
    Exposito-Alonso M, Burbano HA, Bossdorf O, Nielsen R, Weigel D. 2019. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573(7772): 126–129.
    OpenUrlCrossRef
  16. ↵
    Favre A, Widmer A, Karrenberg S. 2017. Differential adaptation drives ecological speciation in campions (Silene): evidence from a multi-site transplant experiment. New Phytologist 213(3): 1487–1499.
    OpenUrl
  17. ↵
    Fournier-Level A, Wilczek AM, Cooper MD, Roe JL, Anderson J, Eaton D, Moyers BT, Petipas RH, Schaeffer RN, Pieper B, et al. 2013. Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana. Molecular Ecology 22(13): 3552–3566.
    OpenUrlCrossRefWeb of Science
  18. ↵
    1. Rechinger KH
    Friedrich HC 1979. Caryophyllaceae. In: Rechinger KH ed. Illustrierte Flora von Mitteleuropa. Hamburg: Parey.
  19. ↵
    Garrison E, Marth G. 2012. Haplotype-based variant detection from short-read sequencing. 1–9.
  20. ↵
    Gompert Z, Egan SP, Barrett RD, Feder JL, Nosil P. 2017. Multilocus approaches for the measurement of selection on correlated genetic loci. Molecular Ecology 26(1): 365–382.
    OpenUrlCrossRef
  21. ↵
    Gompert Z, Jahner JP, Scholl CF, Wilson JS, Lucas LK, Soria-Carrasco V, Fordyce JA, Nice CC, Buerkle CA, Forister ML. 2015. The evolution of novel host use is unlikely to be constrained by trade-offs or a lack of genetic variation. Molecular Ecology 24(11): 2777–2793.
    OpenUrlCrossRefPubMed
  22. ↵
    Gompert Z, Lucas LK, Nice CC, Buerkle CA. 2013. Genome divergence and the genetic architecture of barriers to gene flow between Lycaeides idas and L.melissa. Evolution 67(9): 2498–2514.
    OpenUrlCrossRefPubMed
  23. ↵
    Gompert Z, Messina FJ. 2016. Genomic evidence that resource-based trade-offs limit host-range expansion in a seed beetle. Evolution 70(6): 1249–1264.
    OpenUrlCrossRefPubMed
  24. ↵
    Goulson D, Jerrim K. 1997. Maintenance of the species boundary between Silene dioica and S. latifolia (red and white campion). Oikos 79(1): 115–126.
    OpenUrlCrossRefWeb of Science
  25. ↵
    Hall MC, Lowry DB, Willis JH. 2010. Is local adaptation in Mimulus guttatus caused by trade-offs at individual loci? Molecular Ecology 19(13): 2739–2753.
    OpenUrlCrossRefPubMedWeb of Science
  26. ↵
    Hothorn T, Hornik K, van de Wiel MA, Zeileis A. 2008. Implementing a class of permutation tests: the coin package. Journal of Statistical Software 28(8): 1–23.
    OpenUrl
  27. ↵
    Hu XS, Filatov DA. 2015. The large-X effect in plants: Increased species divergence and reduced gene flow on the Silene X-chromosome. Molecular Ecology 25: 2609–2619.
    OpenUrl
  28. ↵
    Karrenberg S, Favre A. 2008. Genetic and ecological differentiation in the hybridizing campions Silene dioica and S. latifolia. Evolution 62(4): 763–773.
    OpenUrlCrossRefPubMedWeb of Science
  29. ↵
    Karrenberg S, Liu X, Hallander E, Favre A, Herforth□Rahmé J, Widmer A. 2019. Ecological divergence plays an important role in strong but complex reproductive isolation in campions (Silene). Evolution 73(2): 245–261.
    OpenUrl
  30. ↵
    Kawecki TJ, Ebert D. 2004. Conceptual issues in local adaptation. Ecology Letters 7(12): 1225–1241.
    OpenUrlCrossRefWeb of Science
  31. ↵
    Kokko H, Chaturvedi A, Croll D, Fischer MC, Guillaume F, Karrenberg S, Kerr B, Rolshausen G, Stapley J. 2017. Can evolution supply what ecology demands? Trends in Ecology and Evolution 32(3): 187–197.
    OpenUrl
  32. ↵
    Krasovec M, Chester M, Ridout K, Filatov DA. 2018. The mutation rate and the age of the sex chromosomes in Silene latifolia. Current Biology 28(11): 1832–1838.
    OpenUrlCrossRef
  33. ↵
    Leimu R, Fischer M. 2008. A meta-analysis of local adaptation in plants. PloS One 3(12): e4010. doi:4010.1371/journal.pone.0004010.
    OpenUrlCrossRefPubMed
  34. ↵
    Leinonen PH, Remington DL, Leppälä J, Savolainen O. 2013. Genetic basis of local adaptation and flowering time variation in Arabidopsis lyrata. Molecular Ecology 22(3): 709–723.
    OpenUrlCrossRefWeb of Science
  35. ↵
    Lenth RV 2020. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.3. https://CRAN.R-project.org/package=emmeans.
  36. ↵
    Li H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 1303.3997.
  37. ↵
    Lind BM, Friedline CJ, Wegrzyn JL, Maloney PE, Vogler DR, Neale DB, Eckert AJ. 2017. Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA. Molecular Ecology 26(12): 3168–3185.
    OpenUrlCrossRef
  38. ↵
    Liu X, Glémin S, Karrenberg S. 2020. Evolution of putative barrier loci at an intermediate stage of speciation with gene flow in campions (Silene). Molecular Ecology 29(18): 3511–3525.
    OpenUrl
  39. ↵
    Liu X, Karrenberg S. 2018. Genetic architecture of traits associated with reproductive barriers in Silene: Coupling, sex chromosomes and variation. Molecular Ecology 27(19): 3889–3904.
    OpenUrl
  40. ↵
    Lowry DB, Lovell JT, Zhang L, Bonnette J, Fay PA, Mitchell RB, Lloyd-Reilley J, Boe AR, Wu Y, Rouquette FM, Jr.., et al. 2019. QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proceeding of the National Academy of Sciences, U S A 116(26): 12933–12941.
    OpenUrl
  41. ↵
    Mee JA, Yeaman S. 2019. Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations. American Naturalist 194(4): 529–540.
    OpenUrlCrossRef
  42. ↵
    Minder AM, Rothenbuehler C, Widmer A. 2007. Genetic structure of hybrid zones between Silene latifolia and Silene dioica (Caryophyllaceae): evidence for introgressive hybridization. Molecular Ecology 16(12): 2504–2516.
    OpenUrlCrossRefPubMedWeb of Science
  43. ↵
    Mitchell-Olds T, Willis JH, Goldstein DB. 2007. Which evolutionary processes influence natural genetic variation for phenotypic traits? Nature Reviews: Genetics 8(11): 845–856.
    OpenUrlCrossRefPubMedWeb of Science
  44. ↵
    Nosil P. 2012. Ecological Speciation. Oxford: Oxford University Press.
  45. ↵
    O’Leary SJ, Puritz JB, Willis SC, Hollenbeck CM, Portnoy DS. 2018. These aren’t the loci you’e looking for: Principles of effective SNP filtering for molecular ecologists. Molecular Ecology 27(16): 3193–3206.
    OpenUrlCrossRef
  46. ↵
    Orr HA. 1998. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52(4): 935–949.
    OpenUrlCrossRefWeb of Science
  47. ↵
    Orr HA. 2005. The genetic theory of adaptation: a brief history. Nature Reviews Genetics 6(2): 119–127.
    OpenUrlCrossRefPubMedWeb of Science
  48. ↵
    Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. 2012. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PloS One 7(5): e37135.
    OpenUrlCrossRefPubMed
  49. ↵
    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, et al. 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81(3): 559–575.
    OpenUrlCrossRefPubMed
  50. ↵
    Puritz JB, Hollenbeck CM, Gold JR. 2014. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2: e431.
    OpenUrlCrossRefPubMed
  51. ↵
    R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  52. ↵
    Rockman MV. 2012. The QTN program and the alleles that matter for evolution: All that’s gold does not glitter. Evolution 66(1): 1–17.
    OpenUrlCrossRefPubMedWeb of Science
  53. ↵
    Savolainen O, Lascoux M, Merila J. 2013. Ecological genomics of local adaptation. Nature Reviews: Genetics 14(11): 807–820.
    OpenUrlCrossRefPubMed
  54. ↵
    Selby JP, Willis JH. 2018. Major QTL controls adaptation to serpentine soils in Mimulus guttatus. Molecular Ecology 27(24): 5073–5087.
    OpenUrlCrossRef
  55. ↵
    Tiffin P, Ross-Ibarra J. 2014. Advances and limits of using population genetics to understand local adaptation. Trends in Ecology and Evolution 29(12): 673–680.
    OpenUrl
  56. ↵
    Tigano A, Friesen VL. 2016. Genomics of local adaptation with gene flow. Molecular Ecology 25(10): 2144–2164.
    OpenUrlCrossRef
  57. ↵
    Tusso S, Nieuwenhuis BPS, Weissensteiner B, Immler S, Wolf JBW. 2021. Experimental evolution of adaptive divergence under varying degrees of gene flow. Nature Ecology and Evolution 5(3): 338–349.
    OpenUrl
  58. ↵
    Wadgymar SM, Lowry DB, Gould BA, Byron CN, Mactavish RM, Anderson JT. 2017. Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation. Methods in Ecology and Evolution 8(6): 738–749.
    OpenUrl
  59. ↵
    Yeaman S. 2015. Local adaptation by alleles of small effect. The American Naturalist 186(S1): S74–S89.
    OpenUrlCrossRefPubMed
  60. ↵
    Yeaman S, Whitlock MC. 2011. The genetic architecture of adaptation under migration-selection balance. Evolution 65(7): 1897–1911.
    OpenUrlCrossRefPubMedWeb of Science
  61. ↵
    Zhou X, Carbonetto P, Stephens M. 2013. Polygenic modeling with bayesian sparse linear mixed models. Plos Genetics 9(2): e1003264.
    OpenUrl
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A polygenic architecture with conditionally neutral effects underlies ecological differentiation in Silene
Susanne Gramlich, Xiaodong Liu, Adrien Favre, C. Alex Buerkle, Sophie Karrenberg
bioRxiv 2021.07.06.451304; doi: https://doi.org/10.1101/2021.07.06.451304
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