In plants, like in humans, DNA is arranged into sections known as genes that are in turn organised into structures called chromosomes. Mutations that modify the activity of these genes can help plant species to adapt to a new environment or to extreme conditions such as drought. However, successful adaptation often requires changes in many different genes. If these sets of genes are located close to each other on the same chromosome, any mutations will likely be passed onto the next generation together. If the genes are located further away, or even on different chromosomes, they may instead be inherited separately so that the next generation does not benefit as much from the adaptation.

A chromosome inversion – when a segment of chromosome breaks off and reattaches the other way around – can increase the likelihood that sets of mutations on the same chromosome will be inherited together. Many previous studies have found that chromosome inversions tend to drive the ability of species to adapt to different environments by keeping together mutations that affect the same characteristics. However, it is not clear how inversions affect the repeatability of the adaptation, that is, if another group of closely related plants faced the same challenge in their environment would they evolve in the same way, or would they evolve a new response?

To address this question, Soudi, Jahani et al. used a genetics approach known as a genome wide association study to explore how three closely related species of sunflower have adapted to their respective environments. Two of the species grow in various environments across the centre and west of the USA that are often hot and dry, whereas the third species is restricted to the more humid coastal plain of Texas, USA.

The experiments found that a few key genes had changed in all three sunflower species. However, each species also had mutations in a larger set of unique genes that were not changed in the other species. Regions of chromosomes harbouring inversions in one of the species tended to have more of the key genes within them, compared to other genomic regions. This was also true for species that did not have inversions in those regions. This demonstrates that genes in regions affected by chromosome inversions can still help plants adapt to changes in the environment even in the absence of inversions.

Sunflowers are widely grown for their edible oily seeds. In the future, some of the key genes identified in this work may be useful candidates for plant breeding to improve the resilience of sunflowers to drought, high temperatures and other environmental challenges.

The genetic basis of local adaptation is sometimes highly repeatable, with examples of large effect genes driving responses in multiple species, such as FT affecting flowering time in numerous plants (Izawa, 2007; Auge et al., 2019) or Mc1r driving colour polymorphisms in vertebrates (Manceau et al., 2010; Rosenblum et al., 2014). Local adaptation can also be repeated for polygenic traits, with significant patterns of similar association found across many loci for comparisons of conifers (Yeaman et al., 2016), maize and its wild relative teosinte (Tittes et al., 2021; Wang et al., 2021), and Brassicaceae (Bohutínská et al., 2021), to name a few. While we have a growing number of examples of repeatability in the basis of adaptation, it is also interesting to know if species use different genes to adapt to the same selection pressure. Genotypic redundancy – the potential for many genotypes to yield a given phenotype – is one critical factor affecting the repeatability of adaptation, as high redundancy would be expected to result in lower repeatability (Yeaman et al., 2018). Another critical factor affecting repeatability is shared standing variation, whether present due to introgression or incomplete lineage sorting. In either case, variants shared among lineages are much more likely to contribute to a repeated response than new mutations (MacPherson and Nuismer 2018; Ralph and Coop 2015). Consistent with this, Bohutínská et al., 2021 found that repeatability was negatively related to phylogenetic distance. While we are now accumulating more studies about the repeatability of adaptation, we still have very few examples and much remains unknown about the relative importance of these factors (Yeaman, 2022).

Inversions have been implicated in local adaptation in many species (Wellenreuther and Bernatchez, 2018), likely due to their effect to suppress recombination among inverted and non-inverted haplotypes, and thereby maintain linkage disequilibrium (LD) among beneficial combinations of locally adapted alleles (Rieseberg, 2001; Noor et al. 2001; Kirkpatrick and Barton, 2006). This has been approached by models studying the establishment of inversions that capture combinations of locally adapted alleles present as standing variation (e.g. Kirkpatrick and Barton, 2006), as well as models examining the accumulation of locally adapted mutations within inversions (e.g. Schaal et al., 2022). If there is variation in the density of loci that can potentially contribute to local adaptation, inversions would be expected to preferentially establish and be retained in regions harbouring a high density of such loci (and this expectation would hold for both the capture and accumulation models). We would also expect to see stronger signatures of repeated local adaptation in such high-density regions. Despite mounting evidence of their importance in adaptation, it is unclear how inversions may covary with repeatability of adaptation among species. A fundamental parameter of importance in these models is the relationship between migration rate and strength of selection on individual alleles, which may not make persistent contributions to local adaptation without the suppressing effects of recombination if selection is too weak (Yeaman and Whitlock, 2011; Bürger and Akerman, 2011). If most alleles have small effects relative to migration rate and can only contribute to local adaptation via the benefit of the recombination-suppressing effect of an inversion, then we would expect little repeatability at the site of an inversion – other species lacking the inversion would not tend to use that same region for adaptation because selection would be too weak for alleles to persist. On the other hand, if some loci are particularly important for local adaptation and regularly yield mutations of large effect, with these patterns being conserved among species, repeatability within regions harbouring inversions may be substantial. Thus, studying whether adaptation at the same genomic region harbouring an inversion is observed in other species lacking the inversion can give insights about the underlying architecture of adaptation, and the evolution and maintenance of inversions.

Here, we explore the repeatability of local adaptation in three species of sunflowers, Helianthus annuus, Helianthus argophyllus, and Helianthus petiolaris (Figure 1), which harbour large regions of suppressed recombination (‘haploblocks’), most of which are inversions, and are often associated with adaptive traits (Todesco et al., 2020). H. annuus, the common sunflower, is the closest wild relative of cultivated sunflower, which was domesticated from it around 4000 years ago (Blackman et al., 2011). Populations of H. annuus are distributed throughout the central and western United States and generally found on mesic soils, but can grow in a variety of disturbed or extreme habitats, such as semi-desertic or frequently flooded areas, as well as salt marshes. H. petiolaris, the prairie sunflower, prefers sandier soils, and ecotypes of this species are adapted to sand sheets and sand dunes (Ostevik et al., 2016). Here we include samples from two subspecies: H. petiolaris ssp. petiolaris, which is commonly found in the southern Great Plains, and H. petiolaris ssp. fallax, which is limited to more arid regions in Colorado, Utah, New Mexico, and Arizona (Heiser et al., 1969). H. petiolaris and H. annuus have broad and overlapping distributions throughout the central and western United States and appear to have adapted to similar changes in temperature, moisture, and photoperiod regimes. There is also evidence indicating H. annuus and H. petiolaris have likely been exchanging genes during much of their history of divergence (Strasburg and Rieseberg, 2008), although partially isolated by strong pre- and post-zygotic barriers (Sambatti et al., 2012). The third species, H. argophyllus, the silverleaf sunflower, is found exclusively in the southeast coast of Texas and includes both an early flowering ecotype on the coastal barrier islands and a late flowering ecotype inland (Moyers and Rieseberg, 2016; Todesco et al., 2020). H. argophyllus is thought to have undergone cycles of sympatry and allopatry with H. annuus and H. petiolaris over time, but currently only overlaps with H. annuus, with thus overlap likely being a recent event (Heiser, 1951).

Figure 1

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Using broad sampling across the ranges of these species (Figure 1) and sequencing data first published by Todesco et al., 2020, we study the genomic basis of local adaptation by conducting genome-wide scans for associations with climatic and soil environmental variables and phenotypes measured in a common garden. An inherent problem in studying the basis of local adaptation is accounting for the covariance between genome-wide population genetic structure and environment. When the selection pressure driving local adaptation tends to parallel a main axis of demographic expansion or isolation by distance, neutral alleles will have spatial patterns resembling those of causal alleles. Methods that do not correct for population structure have large numbers of false positives because allele frequencies at neutral loci tend to correlate with the environment more than expected by chance (Lotterhos and Whitlock, 2014). By contrast, methods that use structure correction tend to suffer from false negatives because the causal loci have similar patterns of allele frequency variation as the genomic background, and so their statistical signatures are decreased (DeRaad et al., 2021; Booker et al., 2023a). When a non-corrected approach is applied to multiple species, the false-positive problem can be mitigated when testing for loci that contribute to repeated adaptation as the same gene should not tend to be a false positive in multiple species more often than expected by chance (Yeaman et al., 2016). Here we use a conventional genome-wide association study (GWAS) approach with structure correction to study the basis of phenotypes, which vary both within and among populations, and use an uncorrected approach to test association to environment, which only varies among populations (as all individuals within the same population have the same value of environmental variable). For the environmental association portions of the study, we also explore the effect of structure correction in some analyses to contrast the false-negative vs. false-positive problem inherent in each approach. We apply these approaches individually to H. annuus, H. argophyllus, and the two H. petiolaris subspecies, and make pairwise and higher-order comparisons among combinations of these four lineages to study repeatability.

Our overall aim is to characterize the extent of repeatability of local adaptation for different traits and environments and explore the importance of inversions and other factors that may drive differences in repeatability. This analysis builds upon the work of Todesco et al., 2020, which focused on the evolution of haploblocks within each species, but did not systematically study patterns of repeatability in signatures of association among species. We begin by using the strength of correlations between environment and phenotypes measured in a common garden to identify which environmental variables are likely driving local adaptation within each species, and use an index to quantify the relative similarity in these patterns among pairs of species (see Appendix 1—figure 1 for a schematic overview of methods and research questions). We then conduct genome-wide association tests to identify regions most strongly associated with phenotypes and environments within each species. To assess the similarity in these association statistics among species, we use methods similar to Yeaman et al., 2016 to identify regions of the genome that exhibit greater similarity in signatures of association among pairs of lineages than expected by chance, which are likely driven by repeated adaptation using the same genes (but not necessarily the same alleles). We then explore how the number and total size of these regions of repeated association covary with the index of similarity in environmental importance. We also explore whether these regions of repeated association tend to covary with patterns of shared standing variation and test if they are enriched within previously identified haploblocks to explore the role of inversions in adaptation. We then identify candidate genes that show particularly strong signatures of repeated association across multiple lineages. Finally, based on the number of signatures of association that are shared vs. non-shared among lineages, we estimate the number of regions genome-wide that could potentially contribute to local adaptation for each variable and phenotype, which is related to the genotypic redundancy. Taken together, our results show that local adaptation in sunflowers tends to involve both strong repeatability at a small number of genes, often associated with inversions, coupled with high redundancy and non-repeated responses across a much larger number of loci.

Despite its critical importance in shaping the architecture of adaptation, little is known about the extent of genotypic redundancy underlying different traits (Barghi et al., 2020; Láruson et al., 2020; Yeaman, 2022). Here, we have shown evidence of significant repeatability in the basis of local adaptation (Figures 4 and 5), but also an abundance of species-specific, non-repeated signatures (Figure 3; Appendix 1—figure 14). In particular, we find that regions of the genome that harbour inversions in one species also tend to be strongly enriched for signatures of association in other species lacking the inversion (Figure 5). Taken together, this suggests that local adaptation in these species is highly flexible – different species apparently use quite different sets of loci to adapt to the same environment – yet still involves some component that has minimal redundancy, with inversions playing a particularly important role. Some of the ‘usual suspects’ show up in the set of significantly repeated loci identified by PicMin: in addition to the homologs of the FT (FLOWERING LOCUS T) gene reported by Todesco et al., 2020, we also found hits for several other genes involved in circadian regulation and flowering time, including PRR3 (Para et al., 2007), TOE1 (Aukerman and Sakai, 2003), and PHYC (Takano et al., 2005; Chen et al., 2014), which is known to regulate photoperiodic responses in Arabidopsis accessions (Balasubramanian et al., 2006). Other top hits included genes involved in plant development and auxin transport (PIN3 [Keuskamp et al., 2010], ARF4 [Pekker et al., 2005], and MN [Bhatia et al., 2016]), and plant immunity (CRK13 [Acharya et al., 2007] and PRR2 [Cheval et al., 2017]).

The increased repeatability found in regions of the genome that harbour inversions in only one species is particularly interesting. Inversions are commonly associated with local adaptation (Wellenreuther and Bernatchez, 2018), likely because they reduce the rate at which recombination breaks up combinations of co-selected alleles (Kirkpatrick and Barton, 2006), which perhaps facilitates contributions by alleles that would be individually too weakly selected to overcome swamping by migration (Bürger and Akerman, 2011; Yeaman and Whitlock, 2011; Schaal et al., 2022). Evidence here suggests these regions still tend to contribute to adaptation in the species lacking the recombination-suppressing effect of an inversion, consistent with a strong effect of selection relative to migration on at least one locus in the region (rather than adaptation exclusively via many alleles of small effect; Yeaman, 2022). As an example, chromosome 15 harbours a large (72 Mbp) haploblock in H. annuus that is strongly associated with NFFD, and also shows some signatures of association in the other taxa, with particularly strong signatures of association on either side of the haploblock in H. argophyllus (Figure 3A). Interestingly, loci of repeated association identified by PicMin within this region include two genes whose homologs are known to regulate responses to cold: COLD-RESPONSIVE PROTEIN KINASE 1,CRPK1 (Liu et al., 2017) and LATE ELONGATED HYPOCOTYL, LHY (Mizoguchi et al., 2002; Dong et al., 2011). It seems likely that strong selection relative to migration is therefore acting upon several loci in this region, and in many others harbouring inversions.

The observed repeatability associated with inversions further supports the local adaptation model as an explanation for the long-term persistence of segregating inversions (at least in sunflower), rather than mechanisms based on dominance or meiotic drive (Rieseberg, 2001). If there is variation across the genome in the density of loci with the potential to be involved in local adaptation, then the establishment and maintenance of inversions would be biased towards regions harbouring a high density of such loci under this model. If the genomic basis for local adaptation is conserved amongst species, then these same regions are more likely to have high repeatability. Thus, our observation of genomic regions harbouring inversions also being enriched for WRAs is consistent with this general model for inversion evolution. Unfortunately, our observations do not provide much insight into whether inversions evolve through the capture (e.g. Kirkpatrick and Barton, 2006) or accumulation (e.g. Schaal et al., 2022) type of model as either model would be consistent with our results. Most of the sunflower inversions are >1 My old, and therefore predate any current local adaptation patterns, but likely do not predate the genes underlying local adaptation (which appear to be shared among the species we studied). As for the alleles underlying local adaptation, they may be younger than the inversions, but as our work suggests, these regions are prone to harbouring locally adaptive alleles, so it is possible that they also harboured other ancestral locally adaptive alleles. While many studies have demonstrated the importance of inversions for adaptation (Wellenreuther and Bernatchez, 2018; Hager et al., 2022), to our knowledge only two other studies have documented the involvement of the same loci making contributions in the absence of the recombination-suppressing effect of the inversion (Lee et al., 2017; Coughlan and Willis, 2019). This also highlights how comparative studies of a species lacking an inversion may help identify which genes are driving adaptation in another species with an inversion, as segregating inversions tend to have extensive LD that prevents identification of any potential targets of natural selection within them.

While our results suggest a large number of loci can potentially contribute to adaptation, implying high redundancy (Figure 3B; Appendix 1—figure 14), there are several factors that complicate inference. Separating the effects of drift and selection to detect signatures of local adaptation is notoriously difficult because population structure often covaries with features of the environment that drive adaptation (Lotterhos and Whitlock, 2014; Hoban et al., 2016; DeRaad et al., 2021). As found by other analyses (Yeaman et al., 2016; DeRaad et al., 2021), when we use structure correction in our genotype–environment association tests, we find many fewer signatures of repeated association (Appendix 1—figure 7 vs. Appendix 1—figure 11), likely due to the reduced power of BayPass to detect true positives when the environment covaries with population structure (Booker et al., 2023a). Here, we have side-stepped the issues involved in correction for population structure by instead relying on comparisons among species to identify loci with associations more extreme than expected by chance in multiple species, using the null-W and PicMin tests. This assumes that most loci in the genome are not involved in local adaptation, so that the relatively small proportion that are driving adaptation can therefore be picked out due to their tendency to fall into the tail of the distribution of association statistics in at least one other species (which would only occur at the rate of random chance under a purely neutral model). While this approach should be relatively robust for identifying loci with repeated patterns of adaptation, there is no way to formally estimate significance of associations found in only a single species, many of which may be false positives due to covariation of population structure and environment. However, even if we assume that 80% of the observed non-repeated loci are false positives, we still find that estimates of the effective number of loci contributing to adaptation (L_eff) are in the hundreds (Appendix 1—figure 15C). As we are unable to detect loci of small effect due to the limited power, but these loci still likely make important contributions to heritability, our estimates of L_eff will also be biased downwards by excluding these potentially important drivers. Finally, it is also difficult to exclude the contribution of introgression or incomplete lineage sorting to the observed signatures of repeated association. If a locus tended to be highly introgressed between two species in a restricted region of their range, it is possible it could also covary with environment and therefore result in a signature of repeated association that would be mistakenly interpreted as adaptation. While it is difficult to preclude this from our analysis, regions of the genome with signatures of repeated association do not tend to have higher levels of shared standing variation than background regions (Appendix 1—figure 6), suggesting that this is not a broad explanation for our observations. If anything, shared standing variation would be expected to increase repeatability of adaptation (MacPherson and Nuismer, 2017), adding further weight to the inference that there is high genotypic redundancy in these species.

In general, we find that temperature is the strongest driver of repeated adaptation at both the phenotypic and genomic levels. We quantified local adaptation at the phenotypic level using correlations between traits measured in common gardens and the home environment of the population they were sampled from. Across all phenotypes, these correlations tended to be strongest and most similar among species for temperature variables, particularly in comparisons among H. annuus and H. petiolaris subspecies (Appendix 1—figure 3). We see similar patterns of repeatability reflected in the genome, where temperature variables also tend to have the greatest repeatability (Figure 4). The similarity in these phenotypic and genomic signatures is consistent with an effect of strong selection as other artefactual or drift-based explanations for repeatability would not be expected to reflect patterns found at the phenotypic level. It should be noted that the reduced importance of soil variables in the SIPEC index might be partly driven by the fact that all traits were measured on above-ground features due to the difficulty of getting non-disruptive phenotypic measurements for roots.

Taken together, these results suggest that some fraction of the genome contributes to adaptation with low redundancy and high repeatability (which tends to be enriched within genomic regions where there exist inversions in at least one species), while the remainder of the adaptive response is driven by loci with high redundancy and species-specific contributions. Theoretical models of adaptive evolution necessarily involve simplified representations of genetics: population genetic approaches explore cases where strong selection deterministically drives a change in allele frequency at one or a few loci (usually without epistasis), whereas quantitative genetic approaches make the infinitesimal assumption that phenotypic change can be realized through small frequency changes at many loci (Barton et al., 2017; Barghi et al., 2020; Yeaman, 2022). Such models implicitly make quite different assumptions about redundancy: if a population genetic model of directional selection includes no epistasis, then it is implicitly assuming there is no redundancy as every locus can increase fitness and no locus can affect the potential of another locus to increase fitness. On the other hand, quantitative genetic models implicitly assume complete redundancy as the loci driving trait variation have interchangeable effects. Given that differences in redundancy result in quite different evolutionary dynamics (Hollinger et al., 2019; Yeaman, 2022), it is important to re-evaluate the behaviour of theoretical models in light of what we can learn about redundancy from empirical data. Our results suggest that adaptation is a complex process that does not map cleanly onto the assumptions of either approach. At least some component of trait variation experiences sufficiently strong selection and has low enough redundancy to drive the significant signatures of repeatability above the random null that we observe here. On the other hand, most of the strongest signatures of association are specific to a single species, implying high redundancy for most of the genome, even if there are still some detectable signatures of repeatability. Such observations may be consistent with classifying loci into ‘core’ and ‘peripheral’ sets similar to the omnigenic model, but would involve classification based on redundancy rather than gene expression, as advocated by Boyle et al. (2017). Theoretical models adopting a two-class representation of the genetic basis of trait variation may provide more realism than models assuming a simpler distribution, and perhaps yield new insights about qualitatively different dynamical behaviour. Given the difficulty of rigorously quantifying the contribution of alleles of small effect and loci contributing to local adaptation in only one species, it remains an open question what proportion of adaptive response is governed by loci with dynamical behaviour that follow the infinitesimal assumption. This could perhaps be quantified explicitly by decomposing heritabilities for traits driving local adaptation, as has been done for standing variation in humans (Visscher et al., 2017), but this would require a considerable increase in sample size as methods such as GCTA (Yang et al., 2011) do not yield accurate estimates at the sample sizes used here. Future work could perhaps resolve this question using multi-generational crossing designs to break up the LD that tends to accompany local adaptation, allowing a more accurate parsing of the effect sizes of genomic regions and the contribution by repeated vs. redundant components of adaptive trait variation.

All scripts used for analysis and figures are deposited in a Dryad Digital Repository. Raw sequence data are deposited under NCBI BioProject accessions PRJNA532579, PRJNA398560, and PRJNA564337, as described in Todesco et al., 2020.

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Author details

1. Shaghayegh Soudi

   Department of Biological Sciences, University of Calgary, Calgary, Canada

   Contribution

   Resources, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Mojtaba Jahani

   Competing interests

   No competing interests declared

2. Mojtaba Jahani

   1. Department of Biological Sciences, University of Calgary, Calgary, Canada
   2. Department of Botany, University of British Columbia, Vancouver, Canada

   Contribution

   Resources, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Shaghayegh Soudi

   Competing interests

   No competing interests declared

3. Marco Todesco

   1. Department of Botany, University of British Columbia, Vancouver, Canada
   2. Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
   3. Irving K. Barber Faculty of Science, University of British Columbia Okanagan, Kelowna, Canada

   Contribution

   Visualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Gregory L Owens

   Department of Biology, University of Victoria, Victoria, Canada

   Contribution

   Visualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Natalia Bercovich

   Department of Botany, University of British Columbia, Vancouver, Canada

   Contribution

   Resources, Visualization, Methodology

   Competing interests

   No competing interests declared

6. Loren H Rieseberg

   Department of Botany, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Supervision, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

7. Sam Yeaman

   Department of Biological Sciences, University of Calgary, Calgary, Canada

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   samuel.yeaman@ucalgary.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1706-8699

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