Population Genomic Evidence for a Repeated Introduction and Rapid Expansion of the Fungal Maize Pathogen Setosphaeria turcica in Europe.

Modern agricultural practices, climate change, and globalization foster the rapid spread of plant pathogens, such as the maize fungal pathogen Setosphaeria turcica, which causes Northern corn leaf blight and expanded into Central Europe during the twentieth century. To investigate the rapid expansion of S. turcica, we sequenced 121 isolates from Europe and Kenya. Population genomic inference revealed a single genetically diverse cluster in Kenya and three clonal lineages with low diversity, as well as one cluster of multiple clonal sublineages in Europe. Phylogenetic dating suggests that all European lineages originated through sexual reproduction outside Europe and were subsequently introgressed multiple times. Unlike isolates from Kenya, European isolates did not show sexual recombination, despite the presence of both MAT1-1 and MAT1-2 mating types. For the clonal lineages, coalescent model selection supported a selectively neutral model with strong exponential population growth, rather than models with pervasive positive selection caused by host defense resistance or environmental adaptation. Within clonal lineages, phenotypic variation in virulence to different monogenic resistances, which defines the pathogen races, suggests that these races may originate from repeated mutations in virulence genes. Association testing based on k-mers did not identify genomic regions linked to pathogen races, but it did uncover strongly differentiated genomic regions between clonal lineages, which harbor genes with putative roles in pathogenicity. In conclusion, the expansion and population growth of S. turcica in Europe are mainly driven by an expansion of the maize cultivation area and not by rapid adaptation.

Standing genetic variation fuels rapid evolution of herbicide resistance in blackgrass.

Repeated herbicide applications in agricultural fields exert strong selection on weeds such as blackgrass (Alopecurus myosuroides), which is a major threat for temperate climate cereal crops. This inadvertent selection pressure provides an opportunity for investigating the underlying genetic mechanisms and evolutionary processes of rapid adaptation, which can occur both through mutations in the direct targets of herbicides and through changes in other, often metabolic, pathways, known as non-target-site resistance. How much target-site resistance (TSR) relies on de novo mutations vs. standing variation is important for developing strategies to manage herbicide resistance. We first generated a chromosome-level reference genome for A. myosuroides for population genomic studies of herbicide resistance and genome-wide diversity across Europe in this species. Next, through empirical data in the form of highly accurate long-read amplicons of alleles encoding acetyl-CoA carboxylase (ACCase) and acetolactate synthase (ALS) variants, we showed that most populations with resistance due to TSR mutations-23 out of 27 and six out of nine populations for ACCase and ALS, respectively-contained at least two TSR haplotypes, indicating that soft sweeps are the norm. Finally, through forward-in-time simulations, we inferred that TSR is likely to mainly result from standing genetic variation, with only a minor role for de novo mutations.

Deep haplotype analyses of target-site resistance locus ACCase in blackgrass enabled by pool-based amplicon sequencing.

Rapid adaptation of weeds to herbicide applications in agriculture through resistance development is a widespread phenomenon. In particular, the grass Alopecurus myosuroides is an extremely problematic weed in cereal crops with the potential to manifest resistance in only a few generations. Target-site resistances (TSRs), with their strong phenotypic response, play an important role in this rapid adaptive response. Recently, using PacBio's long-read amplicon sequencing technology in hundreds of individuals, we were able to decipher the genomic context in which TSR mutations occur. However, sequencing individual amplicons are costly and time-consuming, thus impractical to implement for other resistance loci or applications. Alternatively, pool-based approaches overcome these limitations and provide reliable allele frequencies, although at the expense of not preserving haplotype information. In this proof-of-concept study, we sequenced with PacBio High Fidelity (HiFi) reads long-range amplicons (13.2 kb), encompassing the entire ACCase gene in pools of over 100 individuals, and resolved them into haplotypes using the clustering algorithm PacBio amplicon analysis (pbaa), a new application for pools in plants and other organisms. From these amplicon pools, we were able to recover most haplotypes from previously sequenced individuals of the same population. In addition, we analysed new pools from a Germany-wide collection of A. myosuroides populations and found that TSR mutations originating from soft sweeps of independent origin were common. Forward-in-time simulations indicate that TSR haplotypes will persist for decades even at relatively low frequencies and without selection, highlighting the importance of accurate measurement of TSR haplotype prevalence for weed management.

Parental environmental effects are common and strong, but unpredictable, in Arabidopsis thaliana.

The phenotypes of plants can be influenced by the environmental conditions experienced by their parents. However, there is still much uncertainty about how common and how predictable such parental environmental effects really are. We carried out a comprehensive experimental test for parental effects, subjecting plants of multiple Arabidopsis thaliana genotypes to 24 different biotic or abiotic stresses, or combinations thereof, and comparing their offspring phenotypes in a common environment. The majority of environmental stresses caused significant parental effects, with -35% to +38% changes in offspring fitness. The expression of parental effects was strongly genotype-dependent, and multiple environmental stresses often acted nonadditively when combined. The direction and magnitude of parental effects were unrelated to the direct effects on the parents: Some environmental stresses did not affect the parents but caused substantial effects on offspring, while for others, the situation was reversed. Our study demonstrates that parental environmental effects are common and often strong in A. thaliana, but they are genotype-dependent, act nonadditively, and are difficult to predict. We should thus be cautious with generalizing from simple studies with single plant genotypes and/or only few individual environmental stresses. A thorough and general understanding of parental effects requires large multifactorial experiments.

Genome-wide association study in quinoa reveals selection pattern typical for crops with a short breeding history.

Quinoa germplasm preserves useful and substantial genetic variation, yet it remains untapped due to a lack of implementation of modern breeding tools. We have integrated field and sequence data to characterize a large diversity panel of quinoa. Whole-genome sequencing of 310 accessions revealed 2.9 million polymorphic high confidence single nucleotide polymorphism (SNP) loci. Highland and Lowland quinoa were clustered into two main groups, with FST divergence of 0.36 and linkage disequilibrium (LD) decay of 6.5 and 49.8 kb, respectively. A genome-wide association study using multi-year phenotyping trials uncovered 600 SNPs stably associated with 17 traits. Two candidate genes are associated with thousand seed weight, and a resistance gene analog is associated with downy mildew resistance. We also identified pleiotropically acting loci for four agronomic traits important for adaptation. This work demonstrates the use of re-sequencing data of an orphan crop, which is partially domesticated to rapidly identify marker-trait association and provides the underpinning elements for genomics-enabled quinoa breeding.

As human populations grow and climate change tightens its grip, developing nutritious crops which can thrive on poor soil and under difficult conditions will become a priority. Quinoa, a harvest currently overlooked by agricultural research, could be an interesting candidate in this effort. With its high nutritional value and its ability to tolerate drought, frost and high concentrations of salt in the soil, this hardy crop has been cultivated in the Andes for the last 5,000 to 7,000 years. Today its commercial production is mainly limited to Peru, Bolivia, and Ecuador. Pinpointing the genetic regions that control traits such as yields or flowering time would help agronomists to create new varieties better suited to life under northern latitudes and mechanical farming. To identify these genes, Patiranage et al. grew 310 varieties of quinoa from all over the world under the same conditions; the genomes of these plants were also examined in great detail. Analyses were then performed to link specific genetic variations with traits relevant to agriculture, helping to pinpoint changes in the genetic code linked to differences in how the plants grew, resisted disease, or produced seeds of varying quality. Candidate genes likely to control these traits were then put forward. The study by Patiranage et al. provides a genetic map where genes of agronomical importance have been precisely located and their effects measured. This resource will help to select genetic profiles which could be used to create new quinoa breeds better adapted to a changing world.

High-Density Mapping of Quantitative Trait Loci Controlling Agronomically Important Traits in Quinoa (Chenopodium quinoa Willd.).

Quinoa is a pseudocereal originating from the Andean regions. Despite quinoa's long cultivation history, genetic analysis of this crop is still in its infancy. We aimed to localize quantitative trait loci (QTL) contributing to the phenotypic variation of agronomically important traits. We crossed the Chilean accession PI-614889 and the Peruvian accession CHEN-109, which depicted significant differences in days to flowering, days to maturity, plant height, panicle length, and thousand kernel weight (TKW), saponin content, and mildew susceptibility. We observed sizeable phenotypic variation across F2 plants and F3 families grown in the greenhouse and the field, respectively. We used Skim-seq to genotype the F2 population and constructed a high-density genetic map with 133,923 single nucleotide polymorphism (SNPs). Fifteen QTL were found for ten traits. Two significant QTL, common in F2 and F3 generations, depicted pleiotropy for days to flowering, plant height, and TKW. The pleiotropic QTL harbored several putative candidate genes involved in photoperiod response and flowering time regulation. This study presents the first high-density genetic map of quinoa that incorporates QTL for several important agronomical traits. The pleiotropic loci can facilitate marker-assisted selection in quinoa breeding programs.

Physical geography, isolation by distance and environmental variables shape genomic variation of wild barley (Hordeum vulgare L. ssp. spontaneum) in the Southern Levant.

Determining the extent of genetic variation that reflects local adaptation in crop-wild relatives is of interest for the purpose of identifying useful genetic diversity for plant breeding. We investigated the association of genomic variation with geographical and environmental factors in wild barley (Hordeum vulgare L. ssp. spontaneum) populations of the Southern Levant using genotyping by sequencing (GBS) of 244 accessions in the Barley 1K+ collection. The inference of population structure resulted in four genetic clusters that corresponded to eco-geographical habitats and a significant association between lower gene flow rates and geographical barriers, e.g. the Judaean Mountains and the Sea of Galilee. Redundancy analysis (RDA) revealed that spatial autocorrelation explained 45% and environmental variables explained 15% of total genomic variation. Only 4.5% of genomic variation was solely attributed to environmental variation if the component confounded with spatial autocorrelation was excluded. A synthetic environmental variable combining latitude, solar radiation, and accumulated precipitation explained the highest proportion of genomic variation (3.9%). When conditioned on population structure, soil water capacity was the most important environmental variable explaining 1.18% of genomic variation. Genome scans with outlier analysis and genome-environment association studies were conducted to identify adaptation signatures. RDA and outlier methods jointly detected selection signatures in the pericentromeric regions, which have reduced recombination, of the chromosomes 3H, 4H, and 5H. However, selection signatures mostly disappeared after correction for population structure. In conclusion, adaptation to the highly diverse environments of the Southern Levant over short geographical ranges had a limited effect on the genomic diversity of wild barley. This highlighted the importance of nonselective forces in genetic differentiation.

DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics.

BACKGROUND: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. RESULTS: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ([Formula: see text]). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. CONCLUSIONS: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.

Genetic variation for tolerance to the downy mildew pathogen Peronospora variabilis in genetic resources of quinoa (Chenopodium quinoa).

BACKGROUND: Quinoa (Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding. RESULTS: We infected 132 South American genotypes, three Danish cultivars and the weedy relative C. album with a single isolate of P. variabilis under greenhouse conditions and observed a large variation in disease traits like severity of infection, which ranged from 5 to 83%. Linear mixed models revealed a significant effect of genotypes on disease traits with high heritabilities (0.72 to 0.81). Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance. CONCLUSIONS: The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding.

Hybrid transcriptome sequencing approach improved assembly and gene annotation in Cynara cardunculus (L.).

BACKGROUND: The investigation of transcriptome profiles using short reads in non-model organisms, which lack of well-annotated genomes, is limited by partial gene reconstruction and isoform detection. In contrast, long-reads sequencing techniques revealed their potential to generate complete transcript assemblies even when a reference genome is lacking. Cynara cardunculus var. altilis (DC) (cultivated cardoon) is a perennial hardy crop adapted to dry environments with many industrial and nutraceutical applications due to the richness of secondary metabolites mostly produced in flower heads. The investigation of this species benefited from the recent release of a draft genome, but the transcriptome profile during the capitula formation still remains unexplored. In the present study we show a transcriptome analysis of vegetative and inflorescence organs of cultivated cardoon through a novel hybrid RNA-seq assembly approach utilizing both long and short RNA-seq reads. RESULTS: The inclusion of a single Nanopore flow-cell output in a hybrid sequencing approach determined an increase of 15% complete assembled genes and 18% transcript isoforms respect to short reads alone. Among 25,463 assembled unigenes, we identified 578 new genes and updated 13,039 gene models, 11,169 of which were alternatively spliced isoforms. During capitulum development, 3424 genes were differentially expressed and approximately two-thirds were identified as transcription factors including bHLH, MYB, NAC, C2H2 and MADS-box which were highly expressed especially after capitulum opening. We also show the expression dynamics of key genes involved in the production of valuable secondary metabolites of which capitulum is rich such as phenylpropanoids, flavonoids and sesquiterpene lactones. Most of their biosynthetic genes were strongly transcribed in the flower heads with alternative isoforms exhibiting differentially expression levels across the tissues. CONCLUSIONS: This novel hybrid sequencing approach allowed to improve the transcriptome assembly, to update more than half of annotated genes and to identify many novel genes and different alternatively spliced isoforms. This study provides new insights on the flowering cycle in an Asteraceae plant, a valuable resource for plant biology and breeding in Cynara and an effective method for improving gene annotation.

Combining focused identification of germplasm and core collection strategies to identify genebank accessions for central European soybean breeding.

Environmental adaptation of crops is essential for reliable agricultural production and an important breeding objective. Genebanks provide genetic variation for the improvement of modern varieties, but the selection of suitable germplasm is frequently impeded by incomplete phenotypic data. We address this bottleneck by combining a Focused Identification of Germplasm Strategy (FIGS) with core collection methodology to select soybean (Glycine max) germplasm for Central European breeding from a collection of >17,000 accessions. By focussing on adaptation to high-latitude cold regions, we selected an "environmental precore" of 3,663 accessions using environmental data and compared the Donor opulation of Environments (DPE) in Asia and the Target Population of Environments (TPE) in Central Europe in the present and 2070. Using single nucleotide polymorphisms, we reduced the precore into two diverse core collections of 183 and 366 accessions to serve as diversity panels for evaluation in the TPE. Genetic differentiation between precore and non-precore accessions revealed genomic regions that control maturity, and novel candidate loci for environmental adaptation, demonstrating the potential of diversity panels for studying adaptation. Objective-driven core collections have the potential to increase germplasm utilization for abiotic adaptation by breeding for a rapidly changing climate, or de novo adaptation of crops to expand cultivation ranges.

Evolutionary genomics can improve prediction of species' responses to climate change.

Global climate change (GCC) increasingly threatens biodiversity through the loss of species, and the transformation of entire ecosystems. Many species are challenged by the pace of GCC because they might not be able to respond fast enough to changing biotic and abiotic conditions. Species can respond either by shifting their range, or by persisting in their local habitat. If populations persist, they can tolerate climatic changes through phenotypic plasticity, or genetically adapt to changing conditions depending on their genetic variability and census population size to allow for de novo mutations. Otherwise, populations will experience demographic collapses and species may go extinct. Current approaches to predicting species responses to GCC begin to combine ecological and evolutionary information for species distribution modelling. Including an evolutionary dimension will substantially improve species distribution projections which have not accounted for key processes such as dispersal, adaptive genetic change, demography, or species interactions. However, eco-evolutionary models require new data and methods for the estimation of a species' adaptive potential, which have so far only been available for a small number of model species. To represent global biodiversity, we need to devise large-scale data collection strategies to define the ecology and evolutionary potential of a broad range of species, especially of keystone species of ecosystems. We also need standardized and replicable modelling approaches that integrate these new data to account for eco-evolutionary processes when predicting the impact of GCC on species' survival. Here, we discuss different genomic approaches that can be used to investigate and predict species responses to GCC. This can serve as guidance for researchers looking for the appropriate experimental setup for their particular system. We furthermore highlight future directions for moving forward in the field and allocating available resources more effectively, to implement mitigation measures before species go extinct and ecosystems lose important functions.

Parallel Seed Color Adaptation during Multiple Domestication Attempts of an Ancient New World Grain.

Thousands of plants have been selected as crops; yet, only a few are fully domesticated. The lack of adaptation to agroecological environments of many crop plants with few characteristic domestication traits potentially has genetic causes. Here, we investigate the incomplete domestication of an ancient grain from the Americas, amaranth. Although three grain amaranth species have been cultivated as crop for millennia, all three lack key domestication traits. We sequenced 121 crop and wild individuals to investigate the genomic signature of repeated incomplete adaptation. Our analysis shows that grain amaranth has been domesticated three times from a single wild ancestor. One trait that has been selected during domestication in all three grain species is the seed color, which changed from dark seeds to white seeds. We were able to map the genetic control of the seed color adaptation to two genomic regions on chromosomes 3 and 9, employing three independent mapping populations. Within the locus on chromosome 9, we identify an MYB-like transcription factor gene, a known regulator for seed color variation in other plant species. We identify a soft selective sweep in this genomic region in one of the crop species but not in the other two species. The demographic analysis of wild and domesticated amaranths revealed a population bottleneck predating the domestication of grain amaranth. Our results indicate that a reduced level of ancestral genetic variation did not prevent the selection of traits with a simple genetic architecture but may have limited the adaptation of complex domestication traits.

Microclimate predicts frost hardiness of alpine Arabidopsis thaliana populations better than elevation.

In mountain regions, topological differences on the microscale can strongly affect microclimate and may counteract the average effects of elevation, such as decreasing temperatures. While these interactions are well understood, their effect on plant adaptation is understudied. We investigated winter frost hardiness of Arabidopsis thaliana accessions originating from 13 sites along altitudinal gradients in the Southern Alps during three winters on an experimental field station on the Swabian Jura and compared levels of frost damage with the observed number of frost days and the lowest temperature in eight collection sites. We found that frost hardiness increased with elevation in a log-linear fashion. This is consistent with adaptation to a higher frequency of frost conditions, but also indicates a decreasing rate of change in frost hardiness with increasing elevation. Moreover, the number of frost days measured with temperature loggers at the collection sites correlated much better with frost hardiness than the elevation of collection sites, suggesting that populations were adapted to their local microclimate. Notably, the variance in frost days across sites increased exponentially with elevation. Together, our results suggest that strong microclimate heterogeneity of high alpine environments can preserve functional genetic diversity among small populations. Synthesis: Here, we tested how plant populations differed in their adaptation to frost exposure along an elevation gradient and whether microsite temperatures improve the prediction of frost hardiness. We found that local temperatures, particularly the number of frost days, are a better predictor of the frost hardiness of plants than elevation. This reflects a substantial variance in frost frequency between sites at similar high elevations. We conclude that high mountain regions harbor microsites that differ in their local microclimate and thereby can preserve a high functional genetic diversity among them. Therefore, high mountain regions have the potential to function as a refugium in times of global change.

Thermal plasticity of the circadian clock is under nuclear and cytoplasmic control in wild barley.

Temperature compensation, expressed as the ability to maintain clock characteristics (mainly period) in face of temperature changes, that is, robustness, is considered a key feature of circadian clock systems. In this study, we explore the genetic basis for lack of robustness, that is, plasticity, of circadian clock as reflected by photosynthesis rhythmicity. The clock rhythmicity of a new wild barley reciprocal doubled haploid population was analysed with a high temporal resolution of pulsed amplitude modulation of chlorophyll fluorescence under optimal (22°C) and high (32°C) temperature. This comparison between two environments pointed to the prevalence of clock acceleration under heat. Genotyping by sequencing of doubled haploid lines indicated a rich recombination landscape with minor fixation (less than 8%) for one of the parental alleles. Quantitative genetic analysis included genotype by environment interactions and binary-threshold models. Variation in the circadian rhythm plasticity phenotypes, expressed as change (delta) of period and amplitude under two temperatures, was associated with maternal organelle genome (the plasmotype), as well as with several nuclear loci. This first reported rhythmicity driven by nuclear loci and plasmotype with few identified variants, paves the way for studying impact of cytonuclear variations on clock robustness and on plant adaptation to changing environments.

Comparative analysis of genetic diversity and differentiation of cauliflower (Brassica oleracea var. botrytis) accessions from two ex situ genebanks.

Cauliflower (Brassica oleracea var. botrytis) is an important vegetable crop for human nutrition. We characterized 192 cauliflower accessions from the USDA and IPK genebanks with genotyping by sequencing (GBS). They originated from 26 different countries and represent about 44% of all cauliflower accessions in both genebanks. The analysis of genetic diversity revealed that accessions formed two major groups that represented the two genebanks and were not related to the country of origin. This differentiation was robust with respect to the analysis methods that included principal component analysis, ADMIXTURE and neighbor-joining trees. Genetic diversity was higher in the USDA collection and significant phenotypic differences between the two genebanks were found in three out of six traits investigated. GBS data have a high proportion of missing data, but we observed that the exclusion of single nucleotide polymorphisms (SNPs) with missing data or the imputation of missing SNP alleles produced very similar results. The results indicate that the composition and type of accessions have a strong effect on the structure of genetic diversity of ex situ collections, although regeneration procedures and local adaptation to regeneration conditions may also contribute to a divergence. Fst-based outlier tests of genetic differentiation identified only a small proportion (<1%) of SNPs that are highly differentiated between the two genebanks, which indicates that selection during seed regeneration is not a major cause of differentiation between genebanks. Seed regeneration procedures of both genebanks do not result in different levels of genetic drift and loss of genetic variation. We therefore conclude that the composition and type of accessions mainly influence the level of genetic diversity and explain the strong genetic differentiation between the two ex situ collections. In summary, GBS is a useful method for characterizing genetic diversity in cauliflower genebank material and our results suggest that it may be useful to incorporate routine genotyping into accession management and seed regeneration to monitor the diversity present in ex situ collections and to reduce the loss of genetic diversity during seed regeneration.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding.

Transgenerational effects of mild heat in Arabidopsis thaliana show strong genotype specificity that is explained by climate at origin.

Transgenerational environmental effects can trigger strong phenotypic variation. However, it is unclear how cues from different preceding generations interact. Also, little is known about the genetic variation for these life history traits. Here, we present the effects of grandparental and parental mild heat, and their combination, on four traits of the third-generation phenotype of 14 Arabidopsis thaliana genotypes. We tested for correlations of these effects with climate and constructed a conceptual model to identify the environmental conditions that favour the parental effect on flowering time. We observed strong evidence for genotype-specific transgenerational effects. On average, A. thaliana accustomed to mild heat produced more seeds after two generations. Parental effects overruled grandparental effects in all traits except reproductive biomass. Flowering was generally accelerated by all transgenerational effects. Notably, the parental effect triggered earliest flowering in genotypes adapted to dry summers. Accordingly, this parental effect was favoured in the model when early summer heat terminated the growing season and environments were correlated across generations. Our results suggest that A. thaliana can partly accustom to mild heat over two generations and genotype-specific parental effects show non-random evolutionary divergence across populations that may support climate change adaptation in the Mediterranean.

Genomic prediction ability for yield-related traits in German winter barley elite material.

KEY MESSAGE: Genomic prediction was evaluated in German winter barley breeding lines. In this material, prediction ability is strongly influenced by population structure and main determinant of prediction ability is the close genetic relatedness of the breeding material. To ensure breeding progress under changing environmental conditions the implementation and evaluation of new breeding methods is of crucial importance. Modern breeding approaches like genomic selection may significantly accelerate breeding progress. We assessed the potential of genomic prediction in a training population of 750 genotypes, consisting of multiple six-rowed winter barley (Hordeum vulgare L.) elite material families and old cultivars, which reflect the breeding history of barley in Germany. Crosses of parents selected from the training set were used to create a set of double-haploid families consisting of 750 genotypes. Those were used to confirm prediction ability estimates based on a cross-validation with the training set material using 11 different genomic prediction models. Population structure was inferred with dimensionality reduction methods like discriminant analysis of principle components and the influence of population structure on prediction ability was investigated. In addition to the size of the training set, marker density is of crucial importance for genomic prediction. We used genome-wide linkage disequilibrium and persistence of linkage phase as indicators to estimate that 11,203 evenly spaced markers are required to capture all QTL effects. Although a 9k SNP array does not contain a sufficient number of polymorphic markers for long-term genomic selection, we obtained fairly high prediction accuracies ranging from 0.31 to 0.71 for the traits earing, hectoliter weight, spikes per square meter, thousand kernel weight and yield and show that they result from the close genetic relatedness of the material. Our work contributes to designing long-term genetic prediction programs for barley breeding.

Analysis of phylogenetic relationships and genome size evolution of the Amaranthus genus using GBS indicates the ancestors of an ancient crop.

The genus Amaranthus consists of 50-70 species and harbors several cultivated and weedy species of great economic importance. A small number of suitable traits, phenotypic plasticity, gene flow and hybridization made it difficult to establish the taxonomy and phylogeny of the whole genus despite various studies using molecular markers. We inferred the phylogeny of the Amaranthus genus using genotyping by sequencing (GBS) of 94 genebank accessions representing 35 Amaranthus species and measured their genome sizes. SNPs were called by de novo and reference-based methods, for which we used the distant sugarbeet Beta vulgaris and the closely related Amaranthus hypochondriacus as references. SNP counts and proportions of missing data differed between methods, but the resulting phylogenetic trees were highly similar. A distance-based neighbor joining tree of individual accessions and a species tree calculated with the multispecies coalescent supported a previous taxonomic classification into three subgenera although the subgenus A. Acnida consists of two highly differentiated clades. The analysis of the Hybridus complex within the A. Amaranthus subgenus revealed insights on the history of cultivated grain amaranths. The complex includes the three cultivated grain amaranths and their wild relatives and was well separated from other species in the subgenus. Wild and cultivated amaranth accessions did not differentiate according to the species assignment but clustered by their geographic origin from South and Central America. Different geographically separated populations of Amaranthus hybridus appear to be the common ancestors of the three cultivated grain species and A. quitensis might be additionally be involved in the evolution of South American grain amaranth (A. caudatus). We also measured genome sizes of the species and observed little variation with the exception of two lineages that showed evidence for a recent polyploidization. With the exception of two lineages, genome sizes are quite similar and indicate that polyploidization did not play a major role in the history of the genus.