Genomic Basis of Adaptation to a Novel Precipitation Regime.

Energy production and metabolism are intimately linked to ecological and environmental constraints across the tree of life. In plants, which depend on sunlight to produce energy, the link between primary metabolism and the environment is especially strong. By governing CO2 uptake for photosynthesis and transpiration, leaf pores, or stomata, couple energy metabolism to the environment and determine productivity and water-use efficiency (WUE). Although evolution is known to tune physiological traits to the local environment, we lack knowledge of the specific links between molecular and evolutionary mechanisms that shape this process in nature. Here, we investigate the evolution of stomatal conductance and WUE in an Arabidopsis population that colonized an island with a montane cloud scrubland ecosystem characterized by seasonal drought and fog-based precipitation. We find that stomatal conductance increases and WUE decreases in the colonizing population relative to its closest outgroup population from temperate North Africa. Genome-wide association mapping reveals a polygenic basis of trait variation, with a substantial contribution from a nonsynonymous single-nucleotide polymorphism in MAP KINASE 12 (MPK12 G53R), which explains 35% of the phenotypic variance in WUE in the island population. We reconstruct the spatially explicit evolutionary history of MPK12 53R on the island and find that this allele increased in frequency in the population due to positive selection as Arabidopsis expanded into the harsher regions of the island. Overall, these findings show how adaptation shaped quantitative eco-physiological traits in a new precipitation regime defined by low rainfall and high humidity.

A two-step adaptive walk rewires nutrient transport in a challenging edaphic environment.

Most well-characterized cases of adaptation involve single genetic loci. Theory suggests that multilocus adaptive walks should be common, but these are challenging to identify in natural populations. Here, we combine trait mapping with population genetic modeling to show that a two-step process rewired nutrient homeostasis in a population of Arabidopsis as it colonized the base of an active stratovolcano characterized by extremely low soil manganese (Mn). First, a variant that disrupted the primary iron (Fe) uptake transporter gene (IRT1) swept quickly to fixation in a hard selective sweep, increasing Mn but limiting Fe in the leaves. Second, multiple independent tandem duplications occurred at NRAMP1 and together rose to near fixation in the island population, compensating the loss of IRT1 by improving Fe homeostasis. This study provides a clear case of a multilocus adaptive walk and reveals how genetic variants reshaped a phenotype and spread over space and time.

Parallel reduction in flowering time from de novo mutations enable evolutionary rescue in colonizing lineages.

Understanding how populations adapt to abrupt environmental change is necessary to predict responses to future challenges, but identifying specific adaptive variants, quantifying their responses to selection and reconstructing their detailed histories is challenging in natural populations. Here, we use Arabidopsis from the Cape Verde Islands as a model to investigate the mechanisms of adaptation after a sudden shift to a more arid climate. We find genome-wide evidence of adaptation after a multivariate change in selection pressures. In particular, time to flowering is reduced in parallel across islands, substantially increasing fitness. This change is mediated by convergent de novo loss of function of two core flowering time genes: FRI on one island and FLC on the other. Evolutionary reconstructions reveal a case where expansion of the new populations coincided with the emergence and proliferation of these variants, consistent with models of rapid adaptation and evolutionary rescue.

A New Catalog of Structural Variants in 1,301 A. thaliana Lines from Africa, Eurasia, and North America Reveals a Signature of Balancing Selection at Defense Response Genes.

Genomic variation in the model plant Arabidopsis thaliana has been extensively used to understand evolutionary processes in natural populations, mainly focusing on single-nucleotide polymorphisms. Conversely, structural variation has been largely ignored in spite of its potential to dramatically affect phenotype. Here, we identify 155,440 indels and structural variants ranging in size from 1 bp to 10 kb, including presence/absence variants (PAVs), inversions, and tandem duplications in 1,301 A. thaliana natural accessions from Morocco, Madeira, Europe, Asia, and North America. We show evidence for strong purifying selection on PAVs in genes, in particular for housekeeping genes and homeobox genes, and we find that PAVs are concentrated in defense-related genes (R-genes, secondary metabolites) and F-box genes. This implies the presence of a "core" genome underlying basic cellular processes and a "flexible" genome that includes genes that may be important in spatially or temporally varying selection. Further, we find an excess of intermediate frequency PAVs in defense response genes in nearly all populations studied, consistent with a history of balancing selection on this class of genes. Finally, we find that PAVs in genes involved in the cold requirement for flowering (vernalization) and drought response are strongly associated with temperature at the sites of origin.

Linked-read sequencing of gametes allows efficient genome-wide analysis of meiotic recombination.

Meiotic crossovers (COs) ensure proper chromosome segregation and redistribute the genetic variation that is transmitted to the next generation. Large populations and the demand for genome-wide, fine-scale resolution challenge existing methods for CO identification. Taking advantage of linked-read sequencing, we develop a highly efficient method for genome-wide identification of COs at kilobase resolution in pooled recombinants. We first test this method using a pool of Arabidopsis F2 recombinants, and recapitulate results obtained from the same plants using individual whole-genome sequencing. By applying this method to a pool of pollen DNA from an F1 plant, we establish a highly accurate CO landscape without generating or sequencing a single recombinant plant. The simplicity of this approach enables the simultaneous generation and analysis of multiple CO landscapes, accelerating the pace at which mechanisms for the regulation of recombination can be elucidated through efficient comparisons of genotypic and environmental effects on recombination.

Gene expression drives the evolution of dominance.

Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance in natural populations is poorly quantified. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arises as a consequence of the functional importance of genes and their optimal expression levels.

Archaic lineages broaden our view on the history of Arabidopsis thaliana.

Contents Summary 1194 I. Introduction 1194 II. Origin of the A. thaliana species 1194 III. The classic model of the history of A. thaliana 1195 IV. New genomic data from outside Eurasia challenge our view of A. thaliana history 1195 V. Conclusions 1197 Acknowledgements 1197 References 1197 SUMMARY: Natural variation in Arabidopsis thaliana has contributed to discoveries in diverse areas of plant biology. While A. thaliana has typically been considered a weed associated primarily with human-mediated environments, including agricultural and urban sites and railways, it has recently been shown that it is also native in remote natural areas, including high altitude sites in Eurasia and Africa, from the Atlas mountains in Morocco to the afro-alpine regions in Eastern and South Africa to Yunnan in China, the Himalayas and the Tibetan Plateau. This finding suggests that while A. thaliana has been extensively studied in Europe and Western Asia there are still many open questions about its population history, genotype-phenotype relationships and mechanisms of adaptation.

Madeiran Arabidopsis thaliana Reveals Ancient Long-Range Colonization and Clarifies Demography in Eurasia.

The study of model organisms on islands may shed light on rare long-range dispersal events, uncover signatures of local evolutionary processes, and inform demographic inference on the mainland. Here, we sequenced the genomes of Arabidopsis thaliana samples from the oceanic island of Madeira. These samples include the most diverged worldwide, likely a result of long isolation on the island. We infer that colonization of Madeira happened between 70 and 85 ka, consistent with a propagule dispersal model (of size ≥10), or with an ecological window of opportunity. This represents a clear example of a natural long-range dispersal event in A. thaliana. Long-term effective population size on the island, rather than the founder effect, had the greatest impact on levels of diversity, and rates of coalescence. Our results uncover a selective sweep signature on the ancestral haplotype of a known translocation in Eurasia, as well as the possible importance of the low phosphorous availability in volcanic soils, and altitude, in shaping early adaptations to the island conditions. Madeiran genomes, sheltered from the complexities of continental demography, help illuminate ancient demographic events in Eurasia. Our data support a model in which two separate lineages of A. thaliana, one originating in Africa and the other from the Caucasus expanded and met in Iberia, resulting in a secondary contact zone there. Although previous studies inferred that the westward expansion of A. thaliana coincided with the spread of human agriculture, our results suggest that it happened much earlier (20-40 ka).

African genomes illuminate the early history and transition to selfing in Arabidopsis thaliana.

Over the past 20 y, many studies have examined the history of the plant ecological and molecular model, Arabidopsis thaliana, in Europe and North America. Although these studies informed us about the recent history of the species, the early history has remained elusive. In a large-scale genomic analysis of African A. thaliana, we sequenced the genomes of 78 modern and herbarium samples from Africa and analyzed these together with over 1,000 previously sequenced Eurasian samples. In striking contrast to expectations, we find that all African individuals sampled are native to this continent, including those from sub-Saharan Africa. Moreover, we show that Africa harbors the greatest variation and represents the deepest history in the A. thaliana lineage. Our results also reveal evidence that selfing, a major defining characteristic of the species, evolved in a single geographic region, best represented today within Africa. Demographic inference supports a model in which the ancestral A. thaliana population began to split by 120-90 kya, during the last interglacial and Abbassia pluvial, and Eurasian populations subsequently separated from one another at around 40 kya. This bears striking similarities to the patterns observed for diverse species, including humans, implying a key role for climatic events during interglacial and pluvial periods in shaping the histories and current distributions of a wide range of species.

The genomic basis of adaptation in plants.

Plants are powerful models for the study of adaptive evolution. Since they are rooted in place, they must directly face environmental insults, making adaptation to local conditions vital. In addition to adaptation to natural conditions, some plant species have held a central role in human subsistence over the past several thousand years. In these species, humans exerted strong selective pressures on traits of agricultural importance. Recently, an increasing number of studies have aimed to identify the genomic basis of adaptation. These studies have provided insights into the mechanisms through which the raw materials of adaptation were introduced as well as the modes of adaptation in wild and domesticated species.

Rapid adaptation to climate change.

In recent years, amid growing concerns that changing climate is affecting species distributions and ecosystems, predicting responses to rapid environmental change has become a major goal. In this issue, Franks and colleagues take a first step towards this objective (Franks et al. 2016). They examine genomewide signatures of selection in populations of Brassica rapa after a severe multiyear drought. Together with other authors, Franks had previously shown that flowering time was reduced after this particular drought and that the reduction was genetically encoded. Now, the authors have sequenced previously stored samples to compare allele frequencies before and after the drought and identify the loci with the most extreme shifts in frequencies. The loci they identify largely differ between populations, suggesting that different genetic variants may be responsible for reduction in flowering time in the two populations.

A practical guide to environmental association analysis in landscape genomics.

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next-generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced-representation sequencing, candidate-gene approaches, linearity of allele-environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.

Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel.

Arabidopsis thaliana is native to Eurasia and is naturalized across the world. Its ability to be easily propagated and its high phenotypic variability make it an ideal model system for functional, ecological and evolutionary genetics. To date, analyses of the natural genetic variation of A. thaliana have involved small numbers of individual plants or genetic markers. Here we genotype 1,307 worldwide accessions, including several regional samples, using a 250K SNP chip. This allowed us to produce a high-resolution description of the global pattern of genetic variation. We applied three complementary selection tests and identified new targets of selection. Further, we characterized the pattern of historical recombination in A. thaliana and observed an enrichment of hotspots in its intergenic regions and repetitive DNA, which is consistent with the pattern that is observed for humans but which is strikingly different from that observed in other plant species. We have made the seeds we used to produce this Regional Mapping (RegMap) panel publicly available. This panel comprises one of the largest genomic mapping resources currently available for global natural isolates of a non-human species.

Adaptation to climate across the Arabidopsis thaliana genome.

Understanding the genetic bases and modes of adaptation to current climatic conditions is essential to accurately predict responses to future environmental change. We conducted a genome-wide scan to identify climate-adaptive genetic loci and pathways in the plant Arabidopsis thaliana. Amino acid-changing variants were significantly enriched among the loci strongly correlated with climate, suggesting that our scan effectively detects adaptive alleles. Moreover, from our results, we successfully predicted relative fitness among a set of geographically diverse A. thaliana accessions when grown together in a common environment. Our results provide a set of candidates for dissecting the molecular bases of climate adaptations, as well as insights about the prevalence of selective sweeps, which has implications for predicting the rate of adaptation.

Adaptations to climate-mediated selective pressures in humans.

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans.

Population genetic analysis of the uncoupling proteins supports a role for UCP3 in human cold resistance.

Production of heat via nonshivering thermogenesis (NST) is critical for temperature homeostasis in mammals. Uncoupling protein UCP1 plays a central role in NST by uncoupling the proton gradients produced in the inner membranes of mitochondria to produce heat; however, the extent to which UCP1 homologues, UCP2 and UCP3, are involved in NST is the subject of an ongoing debate. We used an evolutionary approach to test the hypotheses that variants that are associated with increased expression of these genes (UCP1 -3826A, UCP2 -866A, and UCP3 -55T) show evidence of adaptation with winter climate. To that end, we calculated correlations between allele frequencies and winter climate variables for these single-nucleotide polymorphisms (SNPs), which we genotyped in a panel of 52 worldwide populations. We found significant correlations with winter climate for UCP1 -3826G/A and UCP3 -55C/T. Further, by analyzing previously published genotype data for these SNPs, we found that the peak of the correlation for the UCP1 region occurred at the disease-associated -3826A/G variant and that the UCP3 region has a striking signal overall, with several individual SNPs showing interesting patterns, including the -55C/T variant. Resequencing of the regions in a set of three diverse population samples helped to clarify the signals that we found with the genotype data. At UCP1, the resequencing data revealed modest evidence that the haplotype carrying the -3826A variant was driven to high frequency by selection. In the UCP3 region, combining results from the climate analysis and resequencing survey suggest a more complex model in which variants on multiple haplotypes may independently be correlated with temperature. This is further supported by an excess of intermediate frequency variants in the UCP3 region in the Han Chinese population. Taken together, our results suggest that adaptation to climate influenced the global distribution of allele frequencies in UCP1 and UCP3 and provide an independent source of evidence for a role in cold resistance for UCP3.

Adaptations to new environments in humans: the role of subtle allele frequency shifts.

Humans show tremendous phenotypic diversity across geographically distributed populations, and much of this diversity undoubtedly results from genetic adaptations to different environmental pressures. The availability of genome-wide genetic variation data from densely sampled populations offers unprecedented opportunities for identifying the loci responsible for these adaptations and for elucidating the genetic architecture of human adaptive traits. Several approaches have been used to detect signals of selection in human populations, and these approaches differ in the assumptions they make about the underlying mode of selection. We contrast the results of approaches based on haplotype structure and differentiation of allele frequencies to those from a method for identifying single nucleotide polymorphisms strongly correlated with environmental variables. Although the first group of approaches tends to detect new beneficial alleles that were driven to high frequencies by selection, the environmental correlation approach has power to identify alleles that experienced small shifts in frequency owing to selection. We suggest that the first group of approaches tends to identify only variants with relatively strong phenotypic effects, whereas the environmental correlation methods can detect variants that make smaller contributions to an adaptive trait.

Colloquium paper: human adaptations to diet, subsistence, and ecoregion are due to subtle shifts in allele frequency.

Human populations use a variety of subsistence strategies to exploit an exceptionally broad range of ecoregions and dietary components. These aspects of human environments have changed dramatically during human evolution, giving rise to new selective pressures. To understand the genetic basis of human adaptations, we combine population genetics data with ecological information to detect variants that increased in frequency in response to new selective pressures. Our approach detects SNPs that show concordant differences in allele frequencies across populations with respect to specific aspects of the environment. Genic and especially nonsynonymous SNPs are overrepresented among those most strongly correlated with environmental variables. This provides genome-wide evidence for selection due to changes in ecoregion, diet, and subsistence. We find particularly strong signals associated with polar ecoregions, with foraging, and with a diet rich in roots and tubers. Interestingly, several of the strongest signals overlap with those implicated in energy metabolism phenotypes from genome-wide association studies, including SNPs influencing glucose levels and susceptibility to type 2 diabetes. Furthermore, several pathways, including those of starch and sucrose metabolism, are enriched for strong signals of adaptations to a diet rich in roots and tubers, whereas signals associated with polar ecoregions are overrepresented in genes associated with energy metabolism pathways.

Adaptations to climate in candidate genes for common metabolic disorders.

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.