Beyond cyanogenesis: Temperature gradients drive environmental adaptation in North American white clover (Trifolium repens L.)

Species that repeatedly evolve phenotypic clines across environmental gradients have been highlighted as ideal systems for characterizing the genomic basis of local environmental adaptation. However, few studies have assessed the importance of observed phenotypic clines for local adaptation: conspicuous traits that vary clinally may not necessarily be the most critical in determining local fitness. The present study was designed to fill this gap, using a plant species characterized by repeatedly evolved adaptive phenotypic clines. White clover is naturally polymorphic for its chemical defence cyanogenesis (HCN release with tissue damage); climate-associated cyanogenesis clines have evolved throughout its native and introduced range worldwide. We performed landscape genomic analyses on 415 wild genotypes from 43 locations spanning much of the North American species range to assess the relative importance of cyanogenesis loci vs. other genomic factors in local climatic adaptation. We find clear evidence of local adaptation, with temperature-related climatic variables best describing genome-wide differentiation between sampling locations. The same climatic variables are also strongly correlated with cyanogenesis frequencies and gene copy number variations (CNVs) at cyanogenesis loci. However, landscape genomic analyses indicate no significant contribution of cyanogenesis loci to local adaptation. Instead, several genomic regions containing promising candidate genes for plant response to seasonal cues are identified - some of which are shared with previously identified QTLs for locally adaptive fitness traits in North American white clover. Our findings suggest that local adaptation in white clover is likely determined primarily by genes controlling the timing of growth and flowering in response to local seasonal cues. More generally, this work suggests that caution is warranted when considering the importance of conspicuous phenotypic clines as primary determinants of local adaptation.

Landscape genomics reveals adaptive genetic differentiation driven by multiple environmental variables in naked barley on the Qinghai-Tibetan Plateau

Understanding the local adaptation of crops has long been a concern of evolutionary biologists and molecular ecologists. Identifying the adaptive genetic variability in the genome is crucial not only to provide insights into the genetic mechanism of local adaptation but also to explore the adaptation potential of crops. This study aimed to identify the climatic drivers of naked barley landraces and putative adaptive loci driving local adaptation on the Qinghai-Tibetan Plateau (QTP). To this end, a total of 157 diverse naked barley accessions were genotyped using the genotyping-by-sequencing approach, which yielded 3123 high-quality SNPs for population structure analysis and partial redundancy analysis, and 37,636 SNPs for outlier analysis. The population structure analysis indicated that naked barley landraces could be divided into four groups. We found that the genomic diversity of naked barley landraces could be partly traced back to the geographical and environmental diversity of the landscape. In total, 136 signatures associated with temperature, precipitation, and ultraviolet radiation were identified, of which 13 had pleiotropic effects. We mapped 447 genes, including a known gene HvSs1. Some genes involved in cold stress and regulation of flowering time were detected near eight signatures. Taken together, these results highlight the existence of putative adaptive loci in naked barley on QTP and thus improve our current understanding of the genetic basis of local adaptation.

Genotype-environment associations to reveal the molecular basis of environmental adaptation

A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.

Genetic trade-offs underlie divergent life history strategies for local adaptation in white clover

Local adaptation is common in plants, yet characterization of its underlying genetic basis is rare in herbaceous perennials. Moreover, while many plant species exhibit intraspecific chemical defence polymorphisms, their importance for local adaptation remains poorly understood. We examined the genetic architecture of local adaptation in a perennial, obligately-outcrossing herbaceous legume, white clover (Trifolium repens). This widespread species displays a well-studied chemical defence polymorphism for cyanogenesis (HCN release following tissue damage) and has evolved climate-associated cyanogenesis clines throughout its range. Two biparental F₂  mapping populations, derived from three parents collected in environments spanning the U.S. latitudinal species range (Duluth, MN, St. Louis, MO and Gainesville, FL), were grown in triplicate for two years in reciprocal common garden experiments in the parental environments (6,012 total plants). Vegetative growth and reproductive fitness traits displayed trade-offs across reciprocal environments, indicating local adaptation. Genetic mapping of fitness traits revealed a genetic architecture characterized by allelic trade-offs between environments, with 100% and 80% of fitness QTL in the two mapping populations showing significant QTL×E interactions, consistent with antagonistic pleiotropy. Across the genome there were three hotspots of QTL colocalization. Unexpectedly, we found little evidence that the cyanogenesis polymorphism contributes to local adaptation. Instead, divergent life history strategies in reciprocal environments were major fitness determinants: selection favoured early investment in flowering at the cost of multiyear survival in the southernmost site versus delayed flowering and multiyear persistence in the northern environments. Our findings demonstrate that multilocus genetic trade-offs contribute to contrasting life history characteristics that allow for local adaptation in this outcrossing herbaceous perennial.

Harnessing landscape genomics to identify future climate resilient genotypes in a desert annual

Local adaptation features critically in shaping species responses to changing environments, complicating efforts to revegetate degraded areas. Rapid climate change poses an additional challenge that could reduce fitness of even locally sourced seeds in restoration. Predictive restoration strategies that apply seeds with favourable adaptations to future climate may promote long-term resilience. Landscape genomics is increasingly used to assess spatial patterns in local adaption and may represent a cost-efficient approach for identifying future-adapted genotypes. To demonstrate such an approach, we genotyped 760 plants from 64 Mojave Desert populations of the desert annual Plantago ovata. Genome scans on 5,960 SNPs identified 184 potentially adaptive loci related to climate and satellite vegetation metrics. Causal modelling indicated that variation in potentially adaptive loci was not confounded by isolation by distance or isolation by habitat resistance. A generalized dissimilarity model (GDM) attributed spatial turnover in potentially adaptive loci to temperature, precipitation and NDVI amplitude, a measure of vegetation green-up potential. By integrating a species distribution model (SDM), we find evidence that summer maximum temperature may both constrain the range of P. ovata and drive adaptive divergence in populations exposed to higher temperatures. Within the species' current range, warm-adapted genotypes are predicted to experience a fivefold expansion in climate niche by midcentury and could harbour key adaptations to cope with future climate. We recommend eight seed transfer zones and project each zone into its relative position in future climate. Prioritizing seed collection efforts on genotypes with expanding future habitat represents a promising strategy for restoration practitioners to address rapidly changing climates.

In the presence of population structure: From genomics to candidate genes underlying local adaptation

Understanding the genomic signatures, genes, and traits underlying local adaptation of organisms to heterogeneous environments is of central importance to the field evolutionary biology. To identify loci underlying local adaptation, models that combine allelic and environmental variation while controlling for the effects of population structure have emerged as the method of choice. Despite being evaluated in simulation studies, there has not been a thorough investigation of empirical evidence supporting local adaptation across these alleles. To evaluate these methods, we use 875 Arabidopsis thaliana Eurasian accessions and two mixed models (GEMMA and LFMM) to identify candidate SNPs underlying local adaptation to climate. Subsequently, to assess evidence of local adaptation and function among significant SNPs, we examine allele frequency differentiation and recent selection across Eurasian populations, in addition to their distribution along quantitative trait loci (QTL) explaining fitness variation between Italy and Sweden populations and cis-regulatory/nonsynonymous sites showing significant selective constraint. Our results indicate that significant LFMM/GEMMA SNPs show low allele frequency differentiation and linkage disequilibrium across locally adapted Italy and Sweden populations, in addition to a poor association with fitness QTL peaks (highest logarithm of odds score). Furthermore, when examining derived allele frequencies across the Eurasian range, we find that these SNPs are enriched in low-frequency variants that show very large climatic differentiation but low levels of linkage disequilibrium. These results suggest that their enrichment along putative functional sites most likely represents deleterious variation that is independent of local adaptation. Among all the genomic signatures examined, only SNPs showing high absolute allele frequency differentiation (AFD) and linkage disequilibrium (LD) between Italy and Sweden populations showed a strong association with fitness QTL peaks and were enriched along selectively constrained cis-regulatory/nonsynonymous sites. Using these SNPs, we find strong evidence linking flowering time, freezing tolerance, and the abscisic-acid pathway to local adaptation.

Landscape genetics reveals that adaptive genetic divergence in Pinus bungeana (Pinaceae) is driven by environmental variables relating to ecological habitats

Background

Understanding the genetic basis of local adaptation has long been the concern of biologists. Identifying these adaptive genetic variabilities is crucial not only to improve our knowledge of the genetic mechanism of local adaptation but also to explore the adaptation potential of species.

Results

Using 10 natural populations and 12 start codon targeted (SCoT) markers, a total of 430 unambiguous loci were yielded. The Bayesian analysis of population structure clearly demonstrated that the 10 populations of P. bungeana could be subdivided into three groups. Redundancy analysis showed that this genetic divergence was caused by divergence selection from environmental variables related to the ecological habitats of “avoidance of flooding” and “avoidance of high temperature and humidity.” LFMM results indicated that Bio1, Bio5, Bio8, Bio12, Bio14, and Bio16, which are related to the ecological habitat of P. bungeana, were correlated with the highest numbers of environment-associated loci (EAL).

Conclusions

The results of EAL characterization in P. bungeana clearly supported the hypothesis that environmental variations related to the ecological habitat of species are the key drivers of species adaptive divergence. Moreover, a method to calculate the species landscape adaptation index and quantify the adaptation potential of species was proposed and verified using ecological niche modeling. This model could estimate climatically suitable areas of species spatial distribution. Taking the results together, this study improves the current understanding on the genetic basis of local adaptation.

Electronic supplementary material

The online version of this article (10.1186/s12862-019-1489-x) contains supplementary material, which is available to authorized users.

Genomic Patterns of Local Adaptation under Gene Flow in Arabidopsis lyrata

Short-scale local adaptation is a complex process involving selection, migration, and drift. The expected effects on the genome are well grounded in theory but examining these on an empirical level has proven difficult, as it requires information about local selection, demographic history, and recombination rate variation. Here, we use locally adapted and phenotypically differentiated Arabidopsis lyrata populations from two altitudinal gradients in Norway to test these expectations at the whole-genome level. Demography modeling indicates that populations within the gradients diverged <2 kya and that the sites are connected by gene flow. The gene flow estimates are, however, highly asymmetric with migration from high to low altitudes being several times more frequent than vice versa. To detect signatures of selection for local adaptation, we estimate patterns of lineage-specific differentiation among these populations. Theory predicts that gene flow leads to concentration of adaptive loci in areas of low recombination; a pattern we observe in both lowland-alpine comparisons. Although most selected loci display patterns of conditional neutrality, we found indications of genetic trade-offs, with one locus particularly showing high differentiation and signs of selection in both populations. Our results further suggest that resistance to solar radiation is an important adaptation to alpine environments, while vegetative growth and bacterial defense are indicated as selected traits in the lowland habitats. These results provide insights into genetic architectures and evolutionary processes driving local adaptation under gene flow. We also contribute to understanding of traits and biological processes underlying alpine adaptation in northern latitudes.

Unpacking Conditional Neutrality: Genomic Signatures of Selection on Conditionally Beneficial and Conditionally Deleterious Mutations

It is common to look for signatures of local adaptation in genomes by identifying loci with extreme levels of allele frequency divergence among populations. This approach to finding genes associated with local adaptation often assumes antagonistic pleiotropy, wherein alternative alleles are strongly favored in alternative environments. Conditional neutrality has been proposed as an alternative to antagonistic pleiotropy, but conditionally neutral polymorphisms are transient, and it is unclear how much outlier signal would be maintained under different forms of conditional neutrality. Here, we use individual-based simulations and a simple analytical heuristic to show that a pattern that mimics local adaptation at the phenotypic level, where each genotype has the highest fitness in its home environment, can be produced by the accumulation of mutations that are neutral in their home environment and deleterious in nonlocal environments. Because conditionally deleterious mutations likely arise at a rate many times higher than conditionally beneficial mutations, they can have a significant cumulative effect on fitness even when individual effect sizes are small. We show that conditionally deleterious mutations driving nonlocal maladaptation may be undetectable by even the most powerful genome scans, as differences in allele frequency between populations are typically small. We also explore the evolutionary effects of conditionally beneficial mutations and find that they can maintain significant signals of local adaptation, and they would be more readily detectable than conditionally deleterious mutations using conventional genome scan approaches. We discuss implications for interpreting outcomes of transplant experiments and genome scans that are used to study the genetic basis of local adaptation.

The Effects of Epistasis and Pleiotropy on Genome-Wide Scans for Adaptive Outlier Loci

With the advent of next-generation sequencing approaches, the search for individual loci underlying local adaptation has become a major enterprise in evolutionary biology. One promising method to identify such loci is to examine genome-wide patterns of differentiation, using an FST-outlier approach. The effects of pleiotropy and epistasis on this approach are not yet known. Here, we model 2 populations of a sexually reproducing, diploid organism with 2 quantitative traits, one of which is involved in local adaptation. We consider genetic architectures with and without pleiotropy and epistasis. We also model neutral marker loci on an explicit genetic map as the 2 populations diverge and apply FST outlier approaches to determine the extent to which quantitative trait loci (QTL) are detectable. Our results show, under a wide range of conditions, that only a small number of QTL are typically responsible for most of the trait divergence between populations, even when inheritance is highly polygenic. We find that the loci making the largest contributions to trait divergence tend to be detectable outliers. These loci also make the largest contributions to within-population genetic variance. The addition of pleiotropy reduces the extent to which quantitative traits can evolve independently but does not reduce the efficacy of outlier scans. The addition of epistasis, however, reduces the mean FST values for causative QTL, making these loci more difficult, but not impossible, to detect in outlier scans.

Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana

Local adaptation can occur due to individual genetic variants that increase the fitness of individuals in their home environments but decrease fitness in other environments [genetic trade-offs (GTs)] or genetic variants that increase fitness in one environment but have no effect in other environments [conditional neutrality (CN)]. Here, we show that GT quantitative trait loci (QTLs) for fitness between Italian and Swedish Arabidopsis thaliana exhibit strong population genomic signatures of local adaptation, including elevated levels of allele frequency differentiation, correlations to climatic variables, and recent sweeps. Highly divergent genes between Italy and Sweden populations show evidence of more recent selection in Sweden than Italy, and the biological annotations of these genes suggest interesting mechanisms underlying local adaptation.

Evidence for adaptation to different climates in the model species Arabidopsis thaliana is seen in reciprocal transplant experiments, but the genetic basis of this adaptation remains poorly understood. Field-based quantitative trait locus (QTL) studies provide direct but low-resolution evidence for the genetic basis of local adaptation. Using high-resolution population genomic approaches, we examine local adaptation along previously identified genetic trade-off (GT) and conditionally neutral (CN) QTLs for fitness between locally adapted Italian and Swedish A. thaliana populations [Ågren J, et al. (2013) Proc Natl Acad Sci USA 110:21077–21082]. We find that genomic regions enriched in high F_ST SNPs colocalize with GT QTL peaks. Many of these high F_ST regions also colocalize with regions enriched for SNPs significantly correlated to climate in Eurasia and evidence of recent selective sweeps in Sweden. Examining unfolded site frequency spectra across genes containing high F_ST SNPs suggests GTs may be due to more recent adaptation in Sweden than Italy. Finally, we collapse a list of thousands of genes spanning GT QTLs to 42 genes that likely underlie the observed GTs and explore potential biological processes driving these trade-offs, from protein phosphorylation, to seed dormancy and longevity. Our analyses link population genomic analyses and field-based QTL studies of local adaptation, and emphasize that GTs play an important role in the process of local adaptation.