Predicting evolutionary change at the DNA level in a natural Mimulus population.

Evolution by natural selection occurs when the frequencies of genetic variants change because individuals differ in Darwinian fitness components such as survival or reproductive success. Differential fitness has been demonstrated in field studies of many organisms, but it remains unclear how well we can quantitatively predict allele frequency changes from fitness measurements. Here, we characterize natural selection on millions of Single Nucleotide Polymorphisms (SNPs) across the genome of the annual plant Mimulus guttatus. We use fitness estimates to calibrate population genetic models that effectively predict allele frequency changes into the next generation. Hundreds of SNPs experienced "male selection" in 2013 with one allele at each SNP elevated in frequency among successful male gametes relative to the entire population of adults. In the following generation, allele frequencies at these SNPs consistently shifted in the predicted direction. A second year of study revealed that SNPs had effects on both viability and reproductive success with pervasive trade-offs between fitness components. SNPs favored by male selection were, on average, detrimental to survival. These trade-offs (antagonistic pleiotropy and temporal fluctuations in fitness) may be essential to the long-term maintenance of alleles. Despite the challenges of measuring selection in the wild, the strong correlation between predicted and observed allele frequency changes suggests that population genetic models have a much greater role to play in forward-time prediction of evolutionary change.

Importance of genetic architecture in marker selection decisions for genomic prediction.

KEY MESSAGE: We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.

Novelty and Convergence in Adaptation to Whole Genome Duplication.

Whole genome duplication (WGD) can promote adaptation but is disruptive to conserved processes, especially meiosis. Studies in Arabidopsis arenosa revealed a coordinated evolutionary response to WGD involving interacting proteins controlling meiotic crossovers, which are minimized in an autotetraploid (within-species polyploid) to avoid missegregation. Here, we test whether this surprising flexibility of a conserved essential process, meiosis, is recapitulated in an independent WGD system, Cardamine amara, 17 My diverged from A. arenosa. We assess meiotic stability and perform population-based scans for positive selection, contrasting the genomic response to WGD in C. amara with that of A. arenosa. We found in C. amara the strongest selection signals at genes with predicted functions thought important to adaptation to WGD: meiosis, chromosome remodeling, cell cycle, and ion transport. However, genomic responses to WGD in the two species differ: minimal ortholog-level convergence emerged, with none of the meiosis genes found in A. arenosa exhibiting strong signal in C. amara. This is consistent with our observations of lower meiotic stability and occasional clonal spreading in diploid C. amara, suggesting that nascent C. amara autotetraploid lineages were preadapted by their diploid lifestyle to survive while enduring reduced meiotic fidelity. However, in contrast to a lack of ortholog convergence, we see process-level and network convergence in DNA management, chromosome organization, stress signaling, and ion homeostasis processes. This gives the first insight into the salient adaptations required to meet the challenges of a WGD state and shows that autopolyploids can utilize multiple evolutionary trajectories to adapt to WGD.

Using multiple reference genomes to identify and resolve annotation inconsistencies.

BACKGROUND: Advances in sequencing technologies have led to the release of reference genomes and annotations for multiple individuals within more well-studied systems. While each of these new genome assemblies shares significant portions of synteny between each other, the annotated structure of gene models within these regions can differ. Of particular concern are split-gene misannotations, in which a single gene is incorrectly annotated as two distinct genes or two genes are incorrectly annotated as a single gene. These misannotations can have major impacts on functional prediction, estimates of expression, and many downstream analyses. RESULTS: We developed a high-throughput method based on pairwise comparisons of annotations that detect potential split-gene misannotations and quantifies support for whether the genes should be merged into a single gene model. We demonstrated the utility of our method using gene annotations of three reference genomes from maize (B73, PH207, and W22), a difficult system from an annotation perspective due to the size and complexity of the genome. On average, we found several hundred of these potential split-gene misannotations in each pairwise comparison, corresponding to 3-5% of gene models across annotations. To determine which state (i.e. one gene or multiple genes) is biologically supported, we utilized RNAseq data from 10 tissues throughout development along with a novel metric and simulation framework. The methods we have developed require minimal human interaction and can be applied to future assemblies to aid in annotation efforts. CONCLUSIONS: Split-gene misannotations occur at appreciable frequency in maize annotations. We have developed a method to easily identify and correct these misannotations. Importantly, this method is generic in that it can utilize any type of short-read expression data. Failure to account for split-gene misannotations has serious consequences for biological inference, particularly for expression-based analyses.

GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity.

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data. The procedure, called Genome Order Optimization by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations. We demonstrate the method using both simulated data (calibrated from experiments on Drosophila melanogaster) and real data from five distinct crosses within the flowering plant genus Mimulus. Application of GOOGA to the Mimulus data corrects numerous errors (misplaced sequences) in the M. guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.

The effect of autopolyploidy on population genetic signals of hard sweeps.

Searching for population genomic signals left behind by positive selection is a major focus of evolutionary biology, particularly as sequencing technologies develop and costs decline. The effect of the number of chromosome copies (i.e. ploidy) on the manifestation of these signals remains an outstanding question, despite a wide appreciation of ploidy being a fundamental parameter governing numerous biological processes. We clarify the principal forces governing the differential manifestation and persistence of the selection signal by separating the effects of polyploidy on the rates of fixation versus rates of diversity (i.e. mutation and recombination) using coalescent simulations. We explore the major consequences of polyploidy, finding a more localized signal, greater dependence on dominance and longer persistence of the signal following fixation, and discuss what this means for within- and across ploidy inference on the strength and prevalence of selective sweeps. As genomic advances continue to open doors for interrogating natural systems, simulations such as this aid our ability to interpret and compare data across ploidy levels.

Extreme copy number variation at a tRNA ligase gene affecting phenology and fitness in yellow monkeyflowers.

Copy number variation (CNV) is a major part of the genetic diversity segregating within populations, but remains poorly understood relative to single nucleotide variation. Here, we report on a tRNA ligase gene (Migut.N02091; RLG1a) exhibiting unprecedented, and fitness-relevant, CNV within an annual population of the yellow monkeyflower Mimulus guttatus. RLG1a variation was associated with multiple traits in pooled population sequencing (PoolSeq) scans of phenotypic and phenological cohorts. Resequencing of inbred lines revealed intermediate-frequency three-copy variants of RLG1a (trip+; 5/35 = 14%), and trip+ lines exhibited elevated RLG1a expression under multiple conditions. trip+ carriers, in addition to being over-represented in late-flowering and large-flowered PoolSeq populations, flowered later under stressful conditions in a greenhouse experiment (p < 0.05). In wild population samples, we discovered an additional rare RLG1a variant (high+) that carries 250-300 copies of RLG1a totalling ~5.7 Mb (20-40% of a chromosome). In the progeny of a high+ carrier, Mendelian segregation of diagnostic alleles and qPCR-based copy counts indicate that high+ is a single tandem array unlinked to the single-copy RLG1a locus. In the wild, high+ carriers had highest fitness in two particularly dry and/or hot years (2015 and 2017; both p < 0.01), while single-copy individuals were twice as fecund as either CNV type in a lush year (2016: p < 0.005). Our results demonstrate fluctuating selection on CNVs affecting phenological traits in a wild population, suggest that plant tRNA ligases mediate stress-responsive life-history traits, and introduce a novel system for investigating the molecular mechanisms of gene amplification.

Epistasis Is a Major Determinant of the Additive Genetic Variance in Mimulus guttatus.

The influence of genetic interactions (epistasis) on the genetic variance of quantitative traits is a major unresolved problem relevant to medical, agricultural, and evolutionary genetics. The additive genetic component is typically a high proportion of the total genetic variance in quantitative traits, despite that underlying genes must interact to determine phenotype. This study estimates direct and interaction effects for 11 pairs of Quantitative Trait Loci (QTLs) affecting floral traits within a single population of Mimulus guttatus. With estimates of all 9 genotypes for each QTL pair, we are able to map from QTL effects to variance components as a function of population allele frequencies, and thus predict changes in variance components as allele frequencies change. This mapping requires an analytical framework that properly accounts for bias introduced by estimation errors. We find that even with abundant interactions between QTLs, most of the genetic variance is likely to be additive. However, the strong dependency of allelic average effects on genetic background implies that epistasis is a major determinant of the additive genetic variance, and thus, the population's ability to respond to selection.

Gene expression plasticity resulting from parental leaf damage in Mimulus guttatus.

Leaf trichome density in Mimulus guttatus can be altered by the parental environment. In this study, we compared global gene expression patterns in progeny of damaged and control plants. Significant differences in gene expression probably explain the observed trichome response, and identify additional responsive pathways. Using whole transcriptome RNA sequencing, we estimated differential gene expression between isogenic seedlings whose parents had, or had not, been subject to leaf damage. We identified over 900 genes that were differentially expressed in response to parental wounding. These genes clustered into groups involved in cell wall and cell membrane development, stress response pathways, and secondary metabolism. Gene expression is modified as a consequence of the parental environment in a targeted way that probably alters multiple developmental pathways, and may increase progeny fitness if they experience environments similar to that of their parents.

Sun skink landscape genomics: assessing the roles of micro-evolutionary processes in shaping genetic and phenotypic diversity across a heterogeneous and fragmented landscape.

Incorporating genomic data sets into landscape genetic analyses allows for powerful insights into population genetics, explicitly geographical correlates of selection, and morphological diversification of organisms across the geographical template. Here, we utilize an integrative approach to examine gene flow and detect selection, and we relate these processes to genetic and phenotypic population differentiation across South-East Asia in the common sun skink, Eutropis multifasciata. We quantify the relative effects of geographic and ecological isolation in this system and find elevated genetic differentiation between populations from island archipelagos compared to those on the adjacent South-East Asian continent, which is consistent with expectations concerning landscape fragmentation in island archipelagos. We also identify a pattern of isolation by distance, but find no substantial effect of ecological/environmental variables on genetic differentiation. To assess whether morphological conservatism in skinks may result from stabilizing selection on morphological traits, we perform FST -PST comparisons, but observe that results are highly dependent on the method of comparison. Taken together, this work provides novel insights into the manner by which micro-evolutionary processes may impact macro-evolutionary scale biodiversity patterns across diverse landscapes, and provide genomewide confirmation of classic predictions from biogeographical and landscape ecological theory.

Naturally segregating loci exhibit epistasis for fitness.

The extent to which gene interaction or epistasis contributes to fitness variation within populations remains poorly understood, despite its importance to a myriad of evolutionary questions. Here, we report a multi-year field study estimating fitness of Mimulus guttatus genetic lines in which pairs of naturally segregating loci exist in an otherwise uniform background. An allele at QTL x5b-a locus originally mapped for its effect on flower size-positively affects survival if combined with one genotype at quantitative trait locus x10a (aa) but has negative effects when combined with the other genotypes (Aa and AA). The viability differences between genotypes parallel phenotypic differences for the time and node at which a plant flowers. Viability is negatively correlated with fecundity across genotypes, indicating antagonistic pleiotropy for fitness components. This trade-off reduces the genetic variance for total fitness relative to the individual fitness components and thus may serve to maintain variation. Additionally, we find that the effects of each locus and their interaction often vary with the environment.

Population Genomic Analysis of Diploid-Autopolyploid Species.

This chapter outlines an empirical analysis of genome-wide single-nucleotide polymorphism (SNP) variation and its underlying drivers among multiple natural populations within a diploid-autopolyploid species. The aim is to reconstruct the genetic structure among natural populations of varying ploidy and infer footprints of selection in these populations, framed around specific questions that are typically encountered when analyzing a mixed-ploidy data set,e.g., addressing the relevance of natural whole-genome duplication for speciation and adaptation. We briefly review the options for the analysis of polyploid population genomic data involving variant calling, population structure, demographic history inference, and selection scanning approaches. Further, we provide suggestions for methods and associated software, possible caveats, and examples of their application to mixed-ploidy and autopolyploid data sets.

Whole-genome variation of transposable element insertions in a maize diversity panel.

Intact transposable elements (TEs) account for 65% of the maize genome and can impact gene function and regulation. Although TEs comprise the majority of the maize genome and affect important phenotypes, genome-wide patterns of TE polymorphisms in maize have only been studied in a handful of maize genotypes, due to the challenging nature of assessing highly repetitive sequences. We implemented a method to use short-read sequencing data from 509 diverse inbred lines to classify the presence/absence of 445,418 nonredundant TEs that were previously annotated in four genome assemblies including B73, Mo17, PH207, and W22. Different orders of TEs (i.e., LTRs, Helitrons, and TIRs) had different frequency distributions within the population. LTRs with lower LTR similarity were generally more frequent in the population than LTRs with higher LTR similarity, though high-frequency insertions with very high LTR similarity were observed. LTR similarity and frequency estimates of nested elements and the outer elements in which they insert revealed that most nesting events occurred very near the timing of the outer element insertion. TEs within genes were at higher frequency than those that were outside of genes and this is particularly true for those not inserted into introns. Many TE insertional polymorphisms observed in this population were tagged by SNP markers. However, there were also 19.9% of the TE polymorphisms that were not well tagged by SNPs (R2 < 0.5) that potentially represent information that has not been well captured in previous SNP-based marker-trait association studies. This study provides a population scale genome-wide assessment of TE variation in maize and provides valuable insight on variation in TEs in maize and factors that contribute to this variation.

Genetic control of kernel compositional variation in a maize diversity panel.

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9-71.0% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole-genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality.

Individualized mating system estimation using genomic data.

The estimation of outcrossing rates in hermaphroditic species has been a major focus in the evolutionary study of reproductive strategies, and is also essential for plant breeding and conservation. Surprisingly, genomics has thus far minimally influenced outcrossing rate studies. In this article, we generalize a Bayesian inference method (BORICE) to accommodate genomic data from multiple subpopulations of a species. As an empirical demonstration, BORICE is applied to 115 maternal families of Mimulus guttatus. The analysis shows that low-level whole genome sequencing of parents and offspring is sufficient for individualized mating system estimation: 208 offspring (88.5%) were definitively called as outcrossed, 23 (9.8%) as selfed. After mating system parameters are established (each offspring as outcrossed or selfed and the inbreeding level of maternal plants), BORICE outputs posterior genotype probabilities for each SNP genomewide. Individual SNP calls are often burdened with considerable uncertainty and distilling information from closely linked sites (within genomic windows) can be a useful strategy. For the Mimulus data, principal components based on window statistics were sufficient to diagnose inversion polymorphisms and estimate their effects on spatial structure, phenotypic and fitness measures. More generally, mating system estimation with BORICE can set the stage for population and quantitative genomic analyses, particularly researchers collect phenotypic or fitness data from maternal individuals.

Interspecific introgression mediates adaptation to whole genome duplication.

Adaptive gene flow is a consequential phenomenon across all kingdoms. Although recognition is increasing, there is no study showing that bidirectional gene flow mediates adaptation at loci that manage core processes. We previously discovered concerted molecular changes among interacting members of the meiotic machinery controlling crossover number upon adaptation to whole-genome duplication (WGD) in Arabidopsis arenosa. Here we conduct a population genomic study to test the hypothesis that adaptation to WGD has been mediated by adaptive gene flow between A. arenosa and A. lyrata. We find that A. lyrata underwent WGD more recently than A. arenosa, suggesting that pre-adapted alleles have rescued nascent A. lyrata, but we also detect gene flow in the opposite direction at functionally interacting loci under the most extreme levels of selection. These data indicate that bidirectional gene flow allowed for survival after WGD, and that the merger of these species is greater than the sum of their parts.

Pervasive population genomic consequences of genome duplication in Arabidopsis arenosa.

Ploidy-variable species allow direct inference of the effects of chromosome copy number on fundamental evolutionary processes. While an abundance of theoretical work suggests polyploidy should leave distinct population genomic signatures, empirical data remains sparse. We sequenced ~300 individuals from 39 populations of Arabidopsis arenosa, a naturally diploid-autotetraploid species. We find that the impacts of polyploidy on population genomic processes are subtle yet pervasive, such as reduced efficiency of purifying selection, differences in linked selection and rampant gene flow from diploids. Initial masking of deleterious mutations, faster rates of nucleotide substitution and interploidy introgression likely conspire to shape the evolutionary potential of polyploids.

The Genomic Architecture of Flowering Time Varies Across Space and Time in Mimulus guttatus.

The degree to which genomic architecture varies across space and time is central to the evolution of genomes in response to natural selection. Bulked-segregant mapping combined with pooled sequencing provides an efficient means to estimate the effect of genetic variants on quantitative traits. We develop a novel likelihood framework to identify segregating variation within multiple populations and generations while accommodating estimation error on a sample- and SNP-specific basis. We use this method to map loci for flowering time within natural populations of Mimulus guttatus, collecting the early- and late-flowering plants from each of three neighboring populations and two consecutive generations. Structural variants, such as inversions, and genes from multiple flowering-time pathways exhibit the strongest associations with flowering time. We find appreciable variation in genetic effects on flowering time across both time and space; the greatest differences evident between populations, where numerous factors (environmental variation, genomic background, and private polymorphisms) likely contribute to heterogeneity. However, the changes across years within populations clearly identify genotype-by-environment interactions as an important influence on flowering time variation.

A genomic selection component analysis characterizes migration-selection balance.

The genetic differentiation of populations in response to local selection pressures has long been studied by evolutionary biologists, but key details about the process remain obscure. How rapidly can local adaptation evolve, how extensive is the process across the genome, and how strong are the opposing forces of natural selection and gene flow? Here, we combine direct measurement of survival and reproduction with whole-genome genotyping of a plant species (Mimulus guttatus) that has recently invaded a novel habitat (the Quarry population). We renovate the classic selection component method to accommodate genomic data and observe selection at SNPs throughout the genome. SNPs showing viability selection in Quarry exhibit elevated divergence from neighboring populations relative to neutral SNPs. We also find that nonsignificant SNPs exhibit a subtle, but still significant, change in allele frequency toward neighboring populations, a predicted effect of gene flow. Given that the Quarry population is most probably only 30-40 generations old, the alleles conferring local advantage are almost certainly older than the population itself. Thus, local adaptation owes to the recruitment of standing genetic variation.

A high-resolution genetic map of yellow monkeyflower identifies chemical defense QTLs and recombination rate variation.

Genotyping-by-sequencing methods have vastly improved the resolution and accuracy of genetic linkage maps by increasing both the number of marker loci as well as the number of individuals genotyped at these loci. Using restriction-associated DNA sequencing, we construct a dense linkage map for a panel of recombinant inbred lines derived from a cross between divergent ecotypes of Mimulus guttatus. We used this map to estimate recombination rate across the genome and to identify quantitative trait loci for the production of several secondary compounds (PPGs) of the phenylpropanoid pathway implicated in defense against herbivores. Levels of different PPGs are correlated across recombinant inbred lines suggesting joint regulation of the phenylpropanoid pathway. However, the three quantitative trait loci identified in this study each act on a distinct PPG. Finally, we map three putative genomic inversions differentiating the two parental populations, including a previously characterized inversion that contributes to life-history differences between the annual/perennial ecotypes.