What can genome-wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

The Roles of Drift and Selection on Short Stamen Loss in Arabidopsis thaliana along an Elevational Gradient in the Spanish Pyrenees

Traits that have lost function sometimes persist through evolutionary time. These traits may be maintained by a lack of standing genetic variation for the trait, if selection against the trait is weak relative to drift, or if they have a residual function. To determine the evolutionary processes shaping whether nonfunctional traits are retained or lost, we investigated short stamens in 16 populations of Arabidopsis thaliana along an elevational cline in the Spanish Pyrenees. We found a cline in short stamen number from retention of short stamens in high elevation populations to incomplete loss in low elevation populations. We did not find evidence that limited genetic variation constrains the loss of short stamens at high elevations nor evidence for divergent selection on short stamens between high and low elevations. Finally, we identified loci associated with short stamens in the Spanish Pyrenees that are different from loci associated with variation in short stamen number across latitudes from a previous study. Overall, we did not identify the evolutionary mechanisms maintaining an elevational cline in short stamen number but did identify different genetic loci underlying the variation in short stamen along similar phenotypic clines.

Defining fitness in evolutionary ecology

An understanding of biological fitness is central to theory and practice in ecology and evolution, yet fitness remains an elusive concept to define and challenging to measure accurately. Fitness reflects an individual's ability to pass its alleles on to subsequent generations. Researchers often quantify proxies for fitness, such as survival, growth or reproductive success. However, it can be difficult to determine lifetime fitness, especially for species with long lifespans. The abiotic and biotic environment strongly affects the expression of fitness, which means that fitness components can vary through both space and time. This spatial and temporal heterogeneity results in the impressive range of adaptations that we see in nature. Here, we review definitions of fitness and approaches to measuring fitness at the level of genes, individuals, genotypes, and populations and highlight that fitness is a key concept linking ecological and evolutionary thought.

Genomic wide association study and selective sweep analysis identify genes associated with improved yield under drought in Turkish winter wheat germplasm

A panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

The quantitative genetics of gene expression in Mimulus guttatus

Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population of Mimulus guttatus and scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05). cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTLs make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of "gene expression modules," which has important implications for the evolution of gene expression and how it is studied by geneticists.

Genome-Wide Association Study for Agronomic Traits in Gamma-Ray-Derived Mutant Kenaf (Hibiscus cannabinus L.)

Kenaf (Hibiscus cannabinus L.), in the Malvaceae family, is an important crop for not only fiber production, but also various other industrial materials. We performed phylogenetic analysis and a genome-wide association study (GWAS) of seven agronomic traits: days to flowering, plant height, fresh weight, dry weight, flower color, stem color, and leaf shape, using 96 kenaf genotypes, including gamma-irradiation-derived mutant lines. Genotypes were determined by genotyping-by-sequencing (GBS) and a total of 49,241 single-nucleotide polymorphisms (SNPs) were used in the analysis. Days to flowering, plant height, fresh weight, and dry weight were positively correlated with each other, and stem color was also correlated with fresh weight and dry weight. The phylogenetic analysis divided the 96 lines into nine related groups within two independent groups, and the GWAS analysis detected a total of 49 SNPs for days to flowering, plant height, fresh weight, dry weight, flower color, stem color, and leaf shape with -log10(P) ≥ 4, of which 22 were located in genic regions. The detected SNPs were located in genes with homology ranging from 45% to 96% to plants of the Malvaceae and Betulaceae, and these genes were found to be involved in plant growth and development via various pathways. Our identification of SNP markers related to agronomic traits is expected to help improve the quality of selective breeding programs for kenaf.

Genomic and transcriptomic analyses reveal polygenic architecture for ecologically important traits in aspen (Populus tremuloides Michx.)

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this "missing heritability" phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees

Analysis of natural diversity in wild/cultivated plants can be used to understand the genetic basis for plant breeding programs. Recent advancements in DNA sequencing have expanded the possibilities for genetically altering essential features. There have been several recently disclosed statistical genetic methods for discovering the genes impacting target qualities. One of these useful methods is the genome-wide association study (GWAS), which effectively identifies candidate genes for a variety of plant properties by examining the relationship between a molecular marker (such as SNP) and a target trait. Conventional QTL mapping with highly structured populations has major limitations. The limited number of recombination events results in poor resolution for quantitative traits. Only two alleles at any given locus can be studied simultaneously. Conventional mapping approach fails to work in perennial plants and vegetatively propagated crops. These limitations are sidestepped by association mapping or GWAS. The flexibility of GWAS comes from the fact that the individuals being examined need not be linked to one another, allowing for the use of all meiotic and recombination events to increase resolution. Phenotyping, genotyping, population structure analysis, kinship analysis, and marker-trait association analysis are the fundamental phases of GWAS. With the rapid development of sequencing technologies and computational methods, GWAS is becoming a potent tool for identifying the natural variations that underlie complex characteristics in crops. The use of high-throughput sequencing technologies along with genotyping approaches like genotyping-by-sequencing (GBS) and restriction site associated DNA (RAD) sequencing may be highly useful in fast-forward mapping approach like GWAS. Breeders may use GWAS to quickly unravel the genomes through QTL and association mapping by taking advantage of natural variances. The drawbacks of conventional linkage mapping can be successfully overcome with the use of high-resolution mapping and the inclusion of multiple alleles in GWAS.

Genetic Architecture of Flowering Time Differs Between Populations With Contrasting Demographic and Selective Histories

Understanding the evolutionary factors that impact the genetic architecture of traits is a central goal of evolutionary genetics. Here, we investigate how quantitative trait variation accumulated over time in populations that colonized a novel environment. We compare the genetic architecture of flowering time in Arabidopsis populations from the drought-prone Cape Verde Islands and their closest outgroup population from North Africa. We find that trait polygenicity is severely reduced in the island populations compared to the continental North African population. Further, trait architectures and reconstructed allelic histories best fit a model of strong directional selection in the islands in accord with a Fisher-Orr adaptive walk. Consistent with this, we find that large-effect variants that disrupt major flowering time genes (FRI and FLC) arose first, followed by smaller effect variants, including ATX2 L125F, which is associated with a 4-day reduction in flowering time. The most recently arising flowering time-associated loci are not known to be directly involved in flowering time, consistent with an omnigenic signature developing as the population approaches its trait optimum. Surprisingly, we find no effect in the natural population of EDI-Cvi-0 (CRY2 V367M), an allele for which an effect was previously validated by introgression into a Eurasian line. Instead, our results suggest the previously observed effect of the EDI-Cvi-0 allele on flowering time likely depends on genetic background, due to an epistatic interaction. Altogether, our results provide an empirical example of the effects demographic history and selection has on trait architecture.

A polygenic basis for birth weight in a wild population of red deer (Cervus elaphus)

The genetic architecture of traits under selection has important consequences for the response to selection and potentially for population viability. Early QTL mapping studies in wild populations have reported loci with large effect on trait variation. However, these results are contradicted by more recent genome-wide association analyses, which strongly support the idea that most quantitative traits have a polygenic basis. This study aims to re-evaluate the genetic architecture of a key morphological trait, birth weight, in a wild population of red deer (Cervus elaphus), using genomic approaches. A previous study using 93 microsatellite and allozyme markers and linkage mapping on a kindred of 364 deer detected a pronounced QTL on chromosome 21 explaining 29% of the variance in birth weight, suggesting that this trait is partly controlled by genes with large effects. Here, we used data for more than 2,300 calves genotyped at >39,000 SNP markers and two approaches to characterise the genetic architecture of birth weight. First, we performed a genome-wide association (GWA) analysis, using a genomic relatedness matrix to account for population structure. We found no SNPs significantly associated with birth weight. Second, we used genomic prediction to estimate the proportion of variance explained by each SNP and chromosome. This analysis confirmed that most genetic variance in birth weight was explained by loci with very small effect sizes. Third, we found that the proportion of variance explained by each chromosome was slightly positively correlated with its size. These three findings highlight a highly polygenic architecture for birth weight, which contradicts the previous QTL study. These results are probably explained by the differences in how associations are modelled between QTL mapping and GWA. Our study suggests that models of polygenic adaptation are the most appropriate to study the evolutionary trajectory of this trait.

Selective sweeps linked to the colonization of novel habitats and climatic changes in a wild tomato species

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

From common gardens to candidate genes: exploring local adaptation to climate in red spruce

Local adaptation to climate is common in plant species and has been studied in a range of contexts, from improving crop yields to predicting population maladaptation to future conditions. The genomic era has brought new tools to study this process, which was historically explored through common garden experiments. In this study, we combine genomic methods and common gardens to investigate local adaptation in red spruce and identify environmental gradients and loci involved in climate adaptation. We first use climate transfer functions to estimate the impact of climate change on seedling performance in three common gardens. We then explore the use of multivariate gene-environment association methods to identify genes underlying climate adaptation, with particular attention to the implications of conducting genome scans with and without correction for neutral population structure. This integrative approach uncovered phenotypic evidence of local adaptation to climate and identified a set of putatively adaptive genes, some of which are involved in three main adaptive pathways found in other temperate and boreal coniferous species: drought tolerance, cold hardiness, and phenology. These putatively adaptive genes segregated into two 'modules' associated with different environmental gradients. This study nicely exemplifies the multivariate dimension of adaptation to climate in trees.

Interpreting population and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.

Neutral and adaptive genetic diversity in plants: An overview

Genetic diversity is a prerequisite for evolutionary change in all kinds of organisms. It is generally acknowledged that populations lacking genetic variation are unable to evolve in response to new environmental conditions (e.g., climate change) and thus may face an increased risk of extinction. Although the importance of incorporating genetic diversity into the design of conservation measures is now well understood, less attention has been paid to the distinction between neutral (NGV) and adaptive (AGV) genetic variation. In this review, we first focus on the utility of NGV by examining the ways to quantify it, reviewing applications of NGV to infer ecological and evolutionary processes, and by exploring its utility in designing conservation measures for plant populations and species. Against this background, we then summarize the ways to identify and estimate AGV and discuss its potential use in plant conservation. After comparing NGV and AGV and considering their pros and cons in a conservation context, we conclude that there is an urgent need for a better understanding of AGV and its role in climate change adaptation. To date, however, there are only a few AGV studies on non-model plant species aimed at deciphering the genetic and genomic basis of complex trait variation. Therefore, conservation researchers and practitioners should keep utilizing NGV to develop relevant strategies for rare and endangered plant species until more estimates of AGV are available.

Taking quantitative genomics into the wild

We organized this special issue to highlight new work and review recent advances at the cutting edge of 'wild quantitative genomics'. In this editorial, we will present some history of wild quantitative genetic and genomic studies, before discussing the main themes in the papers published in this special issue and highlighting the future outlook of this dynamic field.

Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future.

Combining GWAS and population genomic analyses to characterize coevolution in a legume-rhizobia symbiosis

The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two-species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host Medicago truncatula with a community of 88 Sinorhizobia (Ensifer) meliloti strains. Multistrain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single-strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, that is, genotype-by-genotype, effects on rhizobial fitness and some annotated rhizobial genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation-selection balance. Consistent with the lack of positive selection acting on host genes, we found that among-host variation in growth was similar whether plants were grown with rhizobia or N-fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multistrain contexts.

Genetic mapping and genomic prediction of sclerotinia stem rot resistance to rapeseed/canola (Brassica napus L.) at seedling stage

GWAS detected ninety-eight significant SNPs associated with Sclerotinia sclerotiorum resistance. Six statistical models resulted in medium to high predictive ability, depending on trait, indicating potential of genomic prediction for disease resistance breeding. The lack of complete host resistance and a complex resistance inheritance nature between rapeseed/canola and Sclerotinia sclerotiorum often limits the development of functional molecular markers that enable breeding for sclerotinia stem rot (SSR) resistance. However, genomics-assisted selection has the potential to accelerate the breeding for SSR resistance. Therefore, genome-wide association (GWA) mapping and genomic prediction (GP) were performed using a diverse panel of 337 rapeseed/canola genotypes. Three-week-old seedlings were screened using the petiole inoculation technique (PIT). Days to wilt (DW) up to 2 weeks and lesion phenotypes (LP) at 3, 4, and 7 days post-inoculation (dpi) were recorded. A strong correlation (r = - 0.90) between DW and LP_4dpi implied that a single time point scoring at four days could be used as a proxy trait. GWA analyses using single-locus (SL) and multi-locus (ML) models identified a total of 41, and 208 significantly associated SNPs, respectively. Out of these, ninety-eight SNPs were identified by a combination of the SL model and any of the ML models, at least two ML models, or two traits. These SNPs explained 1.25-12.22% of the phenotypic variance and considered as significant, could be associated with SSR resistance. Eighty-three candidate genes with a function in disease resistance were associated with the significant SNPs. Six GP models resulted in moderate to high (0.42-0.67) predictive ability depending on SSR resistance traits. The resistant genotypes and significant SNPs will serve as valuable resources for future SSR resistance breeding. Our results also highlight the potential of genomic selection to improve rapeseed/canola breeding for SSR resistance.

Genome Wide Association Study Identifies Candidate Genes Related to the Earlywood Tracheid Properties in Picea crassifolia Kom

Picea crassifolia Kom. is one of the timber and ecological conifers in China and its wood tracheid traits directly affect wood formation and adaptability under harsh environment. Molecular studies on P. crassifolia remain inadequate because relatively few genes have been associated with these traits. To identify markers and candidate genes that can potentially be used for genetic improvement of wood tracheid traits, we examined 106 clones of P. crassifolia, and investigated phenotypic data for 14 wood tracheid traits before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome wide association study (GWAS). Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the studied traits. We developed 4,058,883 SLAF-tags and 12,275,765 SNP loci, and our analyses identified a total of 96 SNP loci that showed significant correlations with three earlywood tracheid traits using a mixed linear model (MLM). Next, candidate genes were screened in the 100 kb zone (50 kb upstream, 50 kb downstream) of each of the SNP loci, whereby 67 candidate genes were obtained in earlywood tracheid traits, including 34 genes of known function and 33 genes of unknown function. We provide the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in wood traits improvement and candidate genes for the development of earlywood tracheid in P. crassifolia.

Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances

Growing virus resistant varieties is a highly effective means to avoid yield loss due to infection by many types of virus. The challenge is to be able to detect resistance donors within plant species diversity and then quickly introduce alleles conferring resistance into elite genetic backgrounds. Until now, mainly monogenic forms of resistance with major effects have been introduced in crops. Polygenic resistance is harder to map and introduce in susceptible genetic backgrounds, but it is likely more durable. Genome wide association studies (GWAS) offer an opportunity to accelerate mapping of both monogenic and polygenic resistance, but have seldom been implemented and described in the plant-virus interaction context. Yet, all of the 48 plant-virus GWAS published so far have successfully mapped QTLs involved in plant virus resistance. In this review, we analyzed general and specific GWAS issues regarding plant virus resistance. We have identified and described several key steps throughout the GWAS pipeline, from diversity panel assembly to GWAS result analyses. Based on the 48 published articles, we analyzed the impact of each key step on the GWAS power and showcase several GWAS methods tailored to all types of viruses.

Genome-wide association mapping and genomic prediction for adult stage sclerotinia stem rot resistance in Brassica napus (L) under field environments

Sclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6 to 12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.41-0.64 predictive ability depending on the model specifications. The highest predictive ability for PM_21D with three models was about 0.64. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes.

Genetic Architecture and Genome-Wide Adaptive Signatures Underlying Stem Lenticel Traits in Populus tomentosa

The stem lenticel is a highly specialized tissue of woody plants that has evolved to balance stem water retention and gas exchange as an adaptation to local environments. In this study, we applied genome-wide association studies and selective sweeping analysis to characterize the genetic architecture and genome-wide adaptive signatures underlying stem lenticel traits among 303 unrelated accessions of P. tomentosa, which has significant phenotypic and genetic variations according to climate region across its natural distribution. In total, we detected 108 significant single-nucleotide polymorphisms, annotated to 88 candidate genes for lenticel, of which 9 causative genes showed significantly different selection signatures among climate regions. Furthermore, PtoNAC083 and PtoMYB46 showed significant association signals and abiotic stress response, so we overexpressed these two genes in Arabidopsis thaliana and found that the number of stem cells in all three overexpression lines was significantly reduced by PtoNAC083 overexpression but slightly increased by PtoMYB46 overexpression, suggesting that both genes are involved in cell division and expansion during lenticel formation. The findings of this study demonstrate the successful application of an integrated strategy for dissecting the genetic basis and landscape genetics of complex adaptive traits, which will facilitate the molecular design of tree ideotypes that may adapt to future climate and environmental changes.

The genetic basis of adaptation in phenology in an introduced population of Black Cottonwood (Populus trichocarpa, Torr. & Gray)

BACKGROUND

Entering and exiting winter dormancy present important trade-offs between growth and survival at northern latitudes. Many forest trees display local adaptation across latitude in traits associated with these phenology transitions. Transfers of a species outside its native range introduce the species to novel combinations of environmental conditions potentially requiring different combinations of alleles to optimize growth and survival. In this study, we performed genome wide association analyses and a selection scan in a P. trichocarpa mapping population derived from crossings between clones collected across the native range and introduced into Sweden. GWAS analyses were performed using phenotypic data collected across two field seasons and in a controlled phytotron experiment.

RESULTS

We uncovered 584 putative candidate genes associated with spring and autumn phenology traits as well as with growth. Many regions harboring variation significantly associated with the initiation of leaf shed and leaf autumn coloring appeared to have been evolving under positive selection in the native environments of P. trichocarpa. A comparison between the candidate genes identified with results from earlier GWAS analyses performed in the native environment found a smaller overlap for spring phenology traits than for autumn phenology traits, aligning well with earlier observations that spring phenology transitions have a more complex genetic basis than autumn phenology transitions.

CONCLUSIONS

In a small and structured introduced population of P. trichocarpa, we find complex genetic architectures underlying all phenology and growth traits, and identify multiple putative candidate genes despite the limitations of the study population.

Genetic Determinants of Intraocular Pressure

Intraocular pressure (IOP) is the cardinal and only modifiable risk factor for glaucoma, the leading cause of irreparable blindness worldwide. Twin and family studies estimate the heritability of IOP to be 40-70%, and linkage studies for IOP have identified numerous loci. Mutations in MYOC can cause markedly elevated IOP and aggressive glaucoma often requiring surgical intervention. However, the majority of the genetic basis for raised IOP and glaucoma in populations is complex, and recent large genome-wide association studies (GWASs) have identified over 100 common variants that contribute to IOP variation. In combination, these loci are predictive for primary open-angle glaucoma in independent populations, achieving an area under the receiver operating characteristic curve of 76% for high-pressure primary open-angle glaucoma; this suggests the possibility of targeted screening in the future. Additionally, GWAS findings have identified important biological pathways underlying IOP regulation, including lymphangiogenesis and lipid metabolism, providing novel targets for new therapies. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genetic architecture and adaptation of flowering time among environments

The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.

A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on polygenic traits from population genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modelled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. The performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Callosobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect the causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open-source computer software (fsabc) implementing this method are available from github (https://github.com/zgompert/fsabc.git).

Population genomics of selectively neutral genetic structure and herbicide resistance in UK populations of Alopecurus myosuroides

BACKGROUND

Alopecurus myosuroides (blackgrass) is a major weed in Europe with known resistance to multiple herbicide modes of action. In the UK, there is evidence that blackgrass has undergone a range expansion. In this paper, genotyping-by-sequencing and population-level herbicide resistance phenotypes are used to explore spatial patterns of selectively neutral genetic variation and resistance. We also perform a preliminary genome-wide association study (GWAS) and genomic prediction analysis to evaluate the potential of these approaches for investigating nontarget site herbicide resistance.

RESULTS

Blackgrass was collected from 47 fields across the British Isles and up to eight plants per field population (n = 369) were genotyped by Restriction site-associated DNA (RAD)-sequencing. A total of 20 426 polymorphic loci were identified and used for population genetic analyses. Phenotypic assays revealed significant variation in herbicide resistance between populations. Population structure was weak (F_ST = 0.024-0.048), but spatial patterns were consistent with an ongoing westward and northward range expansion. We detected strong and consistent Wahlund effects (F_IS = 0.30). There were no spatial patterns of herbicide resistance or evidence for confounding with population structure. Using a combination of population-level GWAS and genomic prediction we found that the top 20, 200, and 2000 GWAS loci had higher predictive abilities for fenoxaprop resistance compared to all markers.

CONCLUSION

There is likely extensive human-mediated gene flow between field populations of the weed blackgrass at a national scale. The lack of confounding of adaptive and neutral genetic variation can enable future, more extensive GWAS analyses to identify the genetic architecture of evolved herbicide resistance. © 2020 Society of Chemical Industry.

Whose trait is it anyways? Coevolution of joint phenotypes and genetic architecture in mutualisms

Evolutionary biologists typically envision a trait's genetic basis and fitness effects occurring within a single species. However, traits can be determined by and have fitness consequences for interacting species, thus evolving in multiple genomes. This is especially likely in mutualisms, where species exchange fitness benefits and can associate over long periods of time. Partners may experience evolutionary conflict over the value of a multi-genomic trait, but such conflicts may be ameliorated by mutualism's positive fitness feedbacks. Here, we develop a simulation model of a host-microbe mutualism to explore the evolution of a multi-genomic trait. Coevolutionary outcomes depend on whether hosts and microbes have similar or different optimal trait values, strengths of selection and fitness feedbacks. We show that genome-wide association studies can map joint traits to loci in multiple genomes and describe how fitness conflict and fitness feedback generate different multi-genomic architectures with distinct signals around segregating loci. Partner fitnesses can be positively correlated even when partners are in conflict over the value of a multi-genomic trait, and conflict can generate strong mutualistic dependency. While fitness alignment facilitates rapid adaptation to a new optimum, conflict maintains genetic variation and evolvability, with implications for applied microbiome science.

Genome-Wide Association Study Reveals Complex Genetic Architecture of Cadmium and Mercury Accumulation and Tolerance Traits in Medicago truncatula

Heavy metals are an increasing problem due to contamination from human sources that and can enter the food chain by being taken up by plants. Understanding the genetic basis of accumulation and tolerance in plants is important for reducing the uptake of toxic metals in crops and crop relatives, as well as for removing heavy metals from soils by means of phytoremediation. Following exposure of Medicago truncatula seedlings to cadmium (Cd) and mercury (Hg), we conducted a genome-wide association study using relative root growth (RRG) and leaf accumulation measurements. Cd and Hg accumulation and RRG had heritability ranging 0.44 - 0.72 indicating high genetic diversity for these traits. The Cd and Hg trait associations were broadly distributed throughout the genome, indicated the traits are polygenic and involve several quantitative loci. For all traits, candidate genes included several membrane associated ATP-binding cassette transporters, P-type ATPase transporters, oxidative stress response genes, and stress related UDP-glycosyltransferases. The P-type ATPase transporters and ATP-binding cassette protein-families have roles in vacuole transport of heavy metals, and our findings support their wide use in physiological plant responses to heavy metals and abiotic stresses. We also found associations between Cd RRG with the genes CAX3 and PDR3, two linked adjacent genes, and leaf accumulation of Hg associated with the genes NRAMP6 and CAX9. When plant genotypes with the most extreme phenotypes were compared, we found significant divergence in genomic regions using population genomics methods that contained metal transport and stress response gene ontologies. Several of these genomic regions show high linkage disequilibrium (LD) among candidate genes suggesting they have evolved together. Minor allele frequency (MAF) and effect size of the most significant SNPs was negatively correlated with large effect alleles being most rare. This is consistent with purifying selection against alleles that increase toxicity and abiotic stress. Conversely, the alleles with large affect that had higher frequencies that were associated with the exclusion of Cd and Hg. Overall, macroevolutionary conservation of heavy metal and stress response genes is important for improvement of forage crops by harnessing wild genetic variants in gene banks such as the Medicago HapMap collection.

Loci harboring genes with important role in drought and related abiotic stress responses in flax revealed by multiple GWAS models

QTNs associated with drought tolerance traits and indices were identified in a flax mini-core collection through multiple GWAS models and phenotyping at multiple locations under irrigated and non-irrigated field conditions. Drought is a critical phenomenon challenging today's agricultural sector. Crop varieties adapted to moisture deficit are becoming vital. Flax can be greatly affected by limiting moisture conditions, especially during the early development and reproductive stages. Here, a mini-core collection comprising genotypes from more than 20 major growing countries was evaluated for 11 drought-related traits in irrigated and non-irrigated fields for 3 years. Heritability of the traits ranged from 44.7 to 86%. Six of the 11 traits showed significant phenotypic difference between irrigated and non-irrigated conditions. A genome-wide association study (GWAS) was performed for these six traits and their corresponding stress indices based on 106 genotypes and 12,316 single nucleotide polymorphisms (SNPs) using six multi-locus and one single-locus models. The SNPs were then assigned to 8050 linkage disequilibrium (LD) blocks to which a restricted two-stage multi-locus multi-allele GWAS was applied. A total of 144 quantitative trait nucleotides (QTNs) and 13 LD blocks were associated with at least one trait or stress index. Of these, 16 explained more than 15% of the genetic variance. Most large-effect QTN loci harbored gene(s) previously predicted to play role(s) in the associated traits. Genes mediating responses to abiotic stresses resided at loci associated with stress indices. Flax genes Lus10009480 and Lus10030150 that are predicted to encode WAX INDUCER1 and STRESS-ASSOCIATED PROTEIN (SAP), respectively, are among the important candidates detected. Accessions with multiple favorable alleles outperformed others for grain yield, thousand seed weight and fiber/biomass in non-irrigated conditions, suggesting their potential usefulness in breeding and genomic selection.

Whole genome resequencing reveals genomic regions associated with thermal adaptation in redband trout

Adaptation to local environments involves evolution of ecologically important traits and underlying physiological processes. Here, we used low coverage whole-genome resequencing (lcWGR) on individuals to identify genome regions involved in thermal adaptation in wild redband trout Oncorhynchus mykiss gairdneri, a subspecies of rainbow trout that inhabits ecosystems ranging from cold montane forests to high elevation deserts. This study includes allele frequency-based analyses for selective sweeps among populations, followed by multiple association tests for specific sets of phenotypes measured under thermal stress (acute and chronic survival/mortality; high or low cardiac performance groups). Depending on the groups in each set of analyses, sequencing reads covered 43%-75% of the genome at ≥15× and each analysis included millions of SNPs across the genome. In tests for selective sweeps among populations, a total of six chromosomal regions were significant. The further association tests for specific phenotypes revealed that the region on chromosome 4 was consistently the most significant and contains the cerk gene (ceramide kinase). This study provides insight into a potential genetic mechanism of local thermal adaptation and suggests cerk may be an important candidate gene. However, further validation of this cerk gene is necessary to determine if the association with cardiac performance results in a functional role to influence thermal performance when exposed to high water temperatures and hypoxic conditions.

Evolutionary Genomics of Plant Gametophytic Selection

It has long been recognized that natural selection during the haploid gametophytic phase of the plant life cycle may have widespread importance for rates of evolution and the maintenance of genetic variation. Recent theoretical advances have further highlighted the significance of gametophytic selection for diverse evolutionary processes. Genomic approaches offer exciting opportunities to address key questions about the extent and effects of gametophytic selection on plant evolution and adaptation. Here, we review the progress and prospects for integrating functional and evolutionary genomics to test theoretical predictions, and to examine the importance of gametophytic selection on genetic diversity and rates of evolution. There is growing evidence that selection during the gametophyte phase of the plant life cycle has important effects on both gene and genome evolution and is likely to have important pleiotropic effects on the sporophyte. We discuss the opportunities to integrate comparative population genomics, genome-wide association studies, and experimental approaches to further distinguish how differential selection in the two phases of the plant life cycle contributes to genetic diversity and adaptive evolution.

Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study

BACKGROUND

Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals.

RESULTS

The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield.

CONCLUSION

Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations

Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype-phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.

Identification of Age-Specific and Common Key Regulatory Mechanisms Governing Eggshell Strength in Chicken Using Random Forests

In today's chicken egg industry, maintaining the strength of eggshells in longer laying cycles is pivotal for improving the persistency of egg laying. Eggshell development and mineralization underlie a complex regulatory interplay of various proteins and signaling cascades involving multiple organ systems. Understanding the regulatory mechanisms influencing this dynamic trait over time is imperative, yet scarce. To investigate the temporal changes in the signaling cascades, we considered eggshell strength at two different time points during the egg production cycle and studied the genotype-phenotype associations by employing the Random Forests algorithm on chicken genotypic data. For the analysis of corresponding genes, we adopted a well established systems biology approach to delineate gene regulatory pathways and master regulators underlying this important trait. Our results indicate that, while some of the master regulators (Slc22a1 and Sox11) and pathways are common at different laying stages of chicken, others (e.g., Scn11a, St8sia2, or the TGF- β pathway) represent age-specific functions. Overall, our results provide: (i) significant insights into age-specific and common molecular mechanisms underlying the regulation of eggshell strength; and (ii) new breeding targets to improve the eggshell quality during the later stages of the chicken production cycle.

The Evolutionary Forces Shaping Cis- and Trans-Regulation of Gene Expression within a Population of Outcrossing Plants

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route toward achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. Gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (local or cis-eQTLs) are under negative selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of genes in trans. Here, we identify loci that affect gene expression in trans using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. The allele frequencies of trans-eQTLs are consistent with stronger negative selection acting on trans-eQTLs than cis-eQTLs, and stronger negative selection acting on trans-eQTLs associated with the expression of multiple genes. However, despite this general pattern, we still observe the presence of a trans-eQTL at intermediate frequency that affects the expression of a large number of genes in the same coexpression module. Overall, our work highlights the different selective pressures shaping variation in cis- and trans-regulation.

Evolution of asexual Daphnia pulex in Japan: variations and covariations of the digestive, morphological and life history traits

Background

Several genetic lineages of obligate parthenogenetic Daphnia pulex, a common zooplankton species, have invaded Japan from North America. Among these, a lineage named JPN1 is thought to have started colonization as a single genotype several hundred to thousand years ago and subsequently produced many genotypes in Japan. To examine the phenotypic variations due to ecological drivers diverging the genotypes in new habitats, we measured heritability and variation in 17 traits, including life history, morphology and digestive traits, and the genetic distance among the D. pulex JPN1 genotypes in Japan.

Results

We found that most of the traits measured varied significantly among the genotypes and that heritability was highest in the morphological traits, followed by the digestive and life history traits. In addition, 93% of the variation in these traits was explained by the first three components in the principal component analysis, implying that variation of these heritable traits is not random but rather converged into a few directions. These relations among traits revealed the potential importance of predation pressures and food conditions as factors for diverging and selecting different genotypes. However, the magnitude of the difference in any single trait group did not correlate with the genetic distance.

Conclusions

Our findings show that the divergent traits evolved within D. pulex JPN1 lineage without genetic recombination, since their ancestral clone invaded Japan. Large variations and covariations of the phenotypic traits, irrespective of the genetic distance among the genotypes, support the view that the invasive success of D. pulex JPN1 was promoted by a genetic architecture that allowed for large phenotypic variations with a limited number of functionally important mutations without recombination.

Electronic supplementary material

The online version of this article (10.1186/s12862-019-1453-9) contains supplementary material, which is available to authorized users.

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants

The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method's prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.

Evaluating the genetic architecture of quantitative traits via selection followed by inbreeding

The deleterious mutation model proposes that quantitative trait variation should be dominated by rare, partially recessive, deleterious mutations. Following artificial selection on a focal trait, the ratio of the difference in inbreeding effects between control and selected populations (ΔB), to the difference in trait means caused by directional selection (ΔM), can inform the extent to which deleterious mutations cause quantitative trait variation. Here, we apply the ΔB/ΔM ratio test to two quantitative traits (male mating success and body size) in Drosophila melanogaster. For both traits, ΔB/ΔM ratios suggested that intermediate-frequency alleles, rather than rare, partially recessive alleles (i.e. deleterious mutations), caused quantitative trait variation. We discuss these results in relation to viability data, exploring how differences between regimens in segregating (measured through inbreeding) and fixed (measured through population crosses) mutational load could affect the ratio test. Finally, we present simulations that test the statistical power of the ratio test, providing guidelines for future research.

Molecular evidence supports a genic capture resolution of the lek paradox

The genic capture hypothesis, where sexually selected traits capture genetic variation in condition and the condition reflects genome-wide mutation load, stands to explain the presence of abundant genetic variation underlying sexually selected traits. Here we test this hypothesis by applying bidirectional selection to male mating success for 14 generations in replicate populations of Drosophila melanogaster. We then resequenced the genomes of flies from each population. Consistent with the central predictions of the genic capture hypothesis, we show that genetic variance decreased with success selection and increased with failure selection, providing evidence for purifying sexual selection. This pattern was distributed across the genome and no consistent molecular pathways were associated with divergence, consistent with condition being the target of selection. Together, our results provide molecular evidence suggesting that strong sexual selection erodes genetic variation, and that genome-wide mutation-selection balance contributes to its maintenance.

Genomic architecture of complex traits in loblolly pine

Dissecting the genetic and genomic architecture of complex traits is essential to understand the forces maintaining the variation in phenotypic traits of ecological and economical importance. Whole-genome resequencing data were used to generate high-resolution polymorphic single nucleotide polymorphism (SNP) markers and genotype individuals from common gardens across the loblolly pine (Pinus taeda) natural range. Genome-wide associations were tested with a large phenotypic dataset comprising 409 variables including morphological traits (height, diameter, carbon isotope discrimination, pitch canker resistance), and molecular traits such as metabolites and expression of xylem development genes. Our study identified 2335 new SNP × trait associations for the species, with many SNPs located in physical clusters in the genome of the species; and the genomic location of hotspots for metabolic × genotype associations. We found a highly polygenic basis of quantitative inheritance, with significant differences in number, effects size, genomic location and frequency of alleles contributing to variation in phenotypes in the different traits. While mutation-selection balance might be shaping the genetic variation in metabolic traits, balancing selection is more likely to shape the variation in expression of xylem development genes. Our work contributes to the study of complex traits in nonmodel plant species by identifying associations at a whole-genome level.

Genome-Wide Association Analyses in the Model Rhizobium Ensifer meliloti

Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association.IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.

Quantitative epigenetics and evolution

Epigenetics refers to chemical modifications of chromatin or transcribed DNA that can influence gene activity and expression without changes in DNA sequence. The last 20 years have yielded breakthroughs in our understanding of epigenetic processes that impact many fields of biology. In this review, we discuss how epigenetics relates to quantitative genetics and evolution. We argue that epigenetics is important for quantitative genetics because: (1) quantitative genetics is increasingly being combined with genomics, and therefore we should expand our thinking to include cellular-level mechanisms that can account for phenotypic variance and heritability besides just those that are hard-coded in the DNA sequence; and (2) epigenetic mechanisms change how phenotypic variance is partitioned, and can thereby change the heritability of traits and how those traits are inherited. To explicate these points, we show that epigenetics can influence all aspects of the phenotypic variance formula: VP (total phenotypic variance) = VG (genetic variance) + VE (environmental variance) + VGxE (genotype-by-environment interaction) + 2COVGE (the genotype-environment covariance) + Vɛ (residual variance), requiring new strategies to account for different potential sources of epigenetic effects on phenotypic variance. We also demonstrate how each of the components of phenotypic variance not only can be influenced by epigenetics, but can also have evolutionary consequences. We argue that no sources of epigenetic effects on phenotypic variance can be easily cast aside in a quantitative genetic research program that seeks to understand evolutionary processes.

Determining the evolutionary forces shaping G × E

Contents Summary 31 I. Introduction 31 II. The maintenance of genetic variation for plasticity 32 III. Why is there environmental variation for genetic effects? 33 IV. Conclusions 35 Acknowledgements 35 References 35 SUMMARY: Phenotypic plasticity is common in nature, yet we lack a comprehensive understanding of the evolutionary forces that shape genetic variation for plasticity. This endeavor is especially important because variation for plasticity will result in genotype-by-environment interactions (G × E), a crucial component of variation in quantitative traits. Here, I review our understanding of the evolutionary forces shaping G × E, focusing specifically on: what evolutionary forces maintain variation for plasticity; and what forces maintain different genetic architectures across environments. My specific goal is to show that genomic data can be leveraged to explain the maintenance of G × E by contrasting patterns of genetic variation for plasticity with neutral expectations.