LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms

Accounting for population structure in genomic predictions of Eucalyptus globulus

Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP (“HBLUP”) analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the “Green Triangle” region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype × environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection.

Genotyping by Sequencing Advancements in Barley

Barley is considered an ideal crop to study cereal genetics due to its close relationship with wheat and diploid ancestral genome. It plays a crucial role in reducing risks to global food security posed by climate change. Genetic variations in the traits of interest in crops are vital for their improvement. DNA markers have been widely used to estimate these variations in populations. With the advancements in next-generation sequencing, breeders could access different types of genetic variations within different lines, with single-nucleotide polymorphisms (SNPs) being the most common type. However, genotyping barley with whole genome sequencing (WGS) is challenged by the higher cost and computational demand caused by the large genome size (5.5GB) and a high proportion of repetitive sequences (80%). Genotyping-by-sequencing (GBS) protocols based on restriction enzymes and target enrichment allow a cost-effective SNP discovery by reducing the genome complexity. In general, GBS has opened up new horizons for plant breeding and genetics. Though considered a reliable alternative to WGS, GBS also presents various computational difficulties, but GBS-specific pipelines are designed to overcome these challenges. Moreover, a robust design for GBS can facilitate the imputation to the WGS level of crops with high linkage disequilibrium. The complete exploitation of GBS advancements will pave the way to a better understanding of crop genetics and offer opportunities for the successful improvement of barley and its close relatives.

Linkage analysis and residual heterozygotes derived near isogenic lines reveals a novel protein quantitative trait loci from a Glycine soja accession

Modern soybean [Glycine max (L.) Merr] cultivars have low overall genetic variation due to repeated bottleneck events that arose during domestication and from selection strategies typical of many soybean breeding programs. In both public and private soybean breeding programs, the introgression of wild soybean (Glycine soja Siebold and Zucc.) alleles is a viable option to increase genetic diversity and identify new sources for traits of value. The objectives of our study were to examine the genetic architecture responsible for seed protein and oil using a recombinant inbred line (RIL) population derived from hybridizing a G. max line ('Osage') with a G. soja accession (PI 593983). Linkage mapping identified a total of seven significant quantitative trait loci on chromosomes 14 and 20 for seed protein and on chromosome 8 for seed oil with LOD scores ranging from 5.3 to 31.7 for seed protein content and from 9.8 to 25.9 for seed oil content. We analyzed 3,015 single F4:9 soybean plants to develop two residual heterozygotes derived near isogenic lines (RHD-NIL) populations by targeting nine SNP markers from genotype-by-sequencing, which corresponded to two novel quantitative trait loci (QTL) derived from G. soja: one for a novel seed oil QTL on chromosome 8 and another for a novel protein QTL on chromosome 14. Single marker analysis and linkage analysis using 50 RHD-NILs validated the chromosome 14 protein QTL, and whole genome sequencing of RHD-NILs allowed us to reduce the QTL interval from ∼16.5 to ∼4.6 Mbp. We identified two genomic regions based on recombination events which had significant increases of 0.65 and 0.72% in seed protein content without a significant decrease in seed oil content. A new Kompetitive allele-specific polymerase chain reaction (KASP) assay, which will be useful for introgression of this trait into modern elite G. max cultivars, was developed in one region. Within the significantly associated genomic regions, a total of eight genes are considered as candidate genes, based on the presence of gene annotations associated with the protein or amino acid metabolism/movement. Our results provide better insights into utilizing wild soybean as a source of genetic diversity for soybean cultivar improvement utilizing native traits.

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

Partial Least Squares Enhances Genomic Prediction of New Environments

In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as “leave one environment out,” is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.

Contrasting Water Withholding Responses of Young Maize Plants Reveal Link Between Lipid Peroxidation and Osmotic Regulation Corroborated by Genetic Analysis

Linking biochemistry and genetics of tolerance to osmotic stress is of interest for understanding plant adaptations to unfavorable conditions. The aims of this study were to investigate the variability in responses of panel of elite maize inbred lines to water withholding for stress-related traits through association study and to identify pathways linked to detected associations for better understanding of maize stress responses. Densely genotyped public and expired Plant Variety Protection Certificate (ex-PVP) inbred lines were planted in controlled conditions (16-h/8-h day/night, 25°C, 50% RH) in control (CO) and exposed to 10-day water withholding (WW). Traits analyzed were guaiacol peroxidase activity (GPOD), total protein content (PROT), lipid peroxidation (TBARS), hydrogen peroxide accumulation (H₂O₂), proline accumulation (proline), and current water content (CWC). Proline accumulation was found to be influenced by H₂O₂ and TBARS signaling pathways acting as an accumulation-switching mechanism. Most of the associations detected were for proline (29.4%) and TBARS (44.1%). Gene ontology (GO) enrichment analysis showed significant enrichment in regulation of integral membrane parts and peroxisomes along with regulation of transcription and polysaccharide catabolism. Dynamic studies involving inbreds with extreme phenotypes are needed to elucidate the role of this signaling mechanism in regulation of response to water deficit.

Genome-Wide Association Mapping Identifies Key Genomic Regions for Grain Zinc and Iron Biofortification in Bread Wheat

Accelerating breeding efforts for developing biofortified bread wheat varieties necessitates understanding the genetic control of grain zinc concentration (GZnC) and grain iron concentration (GFeC). Hence, the major objective of this study was to perform genome-wide association mapping to identify consistently significant genotyping-by-sequencing markers associated with GZnC and GFeC using a large panel of 5,585 breeding lines from the International Maize and Wheat Improvement Center. These lines were grown between 2018 and 2021 in an optimally irrigated environment at Obregon, Mexico, while some of them were also grown in a water-limiting drought-stressed environment and a space-limiting small plot environment and evaluated for GZnC and GFeC. The lines showed a large and continuous variation for GZnC ranging from 27 to 74.5 ppm and GFeC ranging from 27 to 53.4 ppm. We performed 742,113 marker-traits association tests in 73 datasets and identified 141 markers consistently associated with GZnC and GFeC in three or more datasets, which were located on all wheat chromosomes except 3A and 7D. Among them, 29 markers were associated with both GZnC and GFeC, indicating a shared genetic basis for these micronutrients and the possibility of simultaneously improving both. In addition, several significant GZnC and GFeC associated markers were common across the irrigated, water-limiting drought-stressed, and space-limiting small plots environments, thereby indicating the feasibility of indirect selection for these micronutrients in either of these environments. Moreover, the many significant markers identified had minor effects on GZnC and GFeC, suggesting a quantitative genetic control of these traits. Our findings provide important insights into the complex genetic basis of GZnC and GFeC in bread wheat while implying limited prospects for marker-assisted selection and the need for using genomic selection.

Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes

We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.

Population structure and genetic diversity of U.S. wheat varieties

The United States is a major wheat producer with more than a century of wheat (Triticum aestivum L.) research and breeding. Using a panel of 753 historical and modern wheat varieties grown in the United States from the early 1800s to present day, we examined population structure and changes in genetic diversity. We used previously mapped high-quality single-nucleotide polymorphism (SNP) markers from the wheat 90K SNP array for genotyping. The wheat varieties had a slight hierarchical population structure based on growth habit and then by kernel color within spring varieties and by kernel hardness within winter varieties, which corresponds with geographical distribution of the varieties. Classifying varieties by market class, which is a combination of habit, hardness, and color, accounted for the greatest amount of variation (13.3%). We did not find evidence of decreased genetic diversity of either spring or winter varieties after the release of the first semidwarf wheat variety in 1961. On the contrary, northern and Pacific spring varieties, hard red spring (HRS), hard white spring (HWS), and soft white winter (SWW) had increases in both SNP and haplotype genetic diversity after 1961. The soft white spring (SWS) and soft red winter (SRW) market classes already had high genetic diversity in varieties before 1961 and showed some evidence of decreased diversity after 1961. Examination of temporal trends in genetic diversity also did not indicate long-term decline in diversity despite occasional fluctuations.

Genetics, not environment, contributed to winter wheat yield gains in Ontario, Canada

Changes in entries' market classes and genetic improvements within classes-not environmental changes-enhanced yields over thirty-one years of wheat trials. Correlations between yields and ancestries drove genomic prediction accuracies. Increasing crop yields is important for enhancing farmers' livelihoods, meeting market demands, and reducing the environmental impact of agriculture. We analyzed the yield trends of Ontario winter wheat variety trials between 1988 and 2018. Over this period, wheat yields steadily increased by 38 kg ha^-1 yr^-1, or 0.68% yr^-1 relative to the mean. While fungicide treatment of trials contributed a one-time 670 kg ha^-1 yield increase, yields were otherwise unaffected by long-term changes in agronomic practice, climate, or other non-genetic factors. Genetic improvement entirely accounted for yield improvement. Market class changes over the 31 year span accounted for some yield improvement. More importantly, genetic improvement occurred within each market class. Entry yield estimates calculated from genomic prediction models strongly correlated with field estimated yields with a mean r of 0.68. Genomic prediction accuracies were high because yields differed across genetically distinct subpopulations. Despite environmental changes, genetic improvement will likely increase Ontario winter wheat yields into the future.

Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs

KEY MESSAGE

Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs. Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.

Atlas of phenotypic, genotypic and geographical diversity present in the European traditional tomato

The Mediterranean basin countries are considered secondary centres of tomato diversification. However, information on phenotypic and allelic variation of local tomato materials is still limited. Here we report on the evaluation of the largest traditional tomato collection, which includes 1499 accessions from Southern Europe. Analyses of 70 traits revealed a broad range of phenotypic variability with different distributions among countries, with the culinary end use within each country being the main driver of tomato diversification. Furthermore, eight main tomato types (phenoclusters) were defined by integrating phenotypic data, country of origin, and end use. Genome-wide association study (GWAS) meta-analyses identified associations in 211 loci, 159 of which were novel. The multidimensional integration of phenoclusters and the GWAS meta-analysis identified the molecular signatures for each traditional tomato type and indicated that signatures originated from differential combinations of loci, which in some cases converged in the same tomato phenotype. Our results provide a roadmap for studying and exploiting this untapped tomato diversity.

Genome-wide association mapping and genomic prediction for kernel color traits in intermediate wheatgrass (Thinopyrum intermedium)

BACKGROUND

Intermediate wheatgrass (IWG) is a novel perennial grain crop currently undergoing domestication. It offers important ecosystem benefits while producing grain suitable for human consumption. Several aspects of plant biology and genetic control are yet to be studied in this new crop. To understand trait behavior and genetic characterization of kernel color in IWG breeding germplasm from the University of Minnesota was evaluated for the CIELAB components (L*, a*, b*) and visual differences. Trait values were used in a genome-wide association scan to reveal genomic regions controlling IWG's kernel color. The usability of genomic prediction in predicting kernel color traits was also evaluated using a four-fold cross validation method.

RESULTS

A wide phenotypic variation was observed for all four kernel color traits with pairwise trait correlations ranging from - 0.85 to 0.27. Medium to high estimates of broad sense trait heritabilities were observed and ranged from 0.41 to 0.78. A genome-wide association scan with single SNP markers detected 20 significant marker-trait associations in 9 chromosomes and 23 associations in 10 chromosomes using multi-allelic haplotype blocks. Four of the 20 significant SNP markers and six of the 23 significant haplotype blocks were common between two or more traits. Evaluation of genomic prediction of kernel color traits revealed the visual score to have highest mean predictive ability (r² = 0.53); r² for the CIELAB traits ranged from 0.29-0.33. A search for candidate genes led to detection of seven IWG genes in strong alignment with MYB36 transcription factors from other cereal crops of the Triticeae tribe. Three of these seven IWG genes had moderate similarities with R-A1, R-B1, and R-D1, the three genes that control grain color in wheat.

CONCLUSIONS

We characterized the distribution of kernel color in IWG for the first time, which revealed a broad phenotypic diversity in an elite breeding germplasm. Identification of genetic loci controlling the trait and a proof-of-concept that genomic selection might be useful in selecting genotypes of interest could help accelerate the breeding of this novel crop towards specific end-use.

Influence of Pollen Dispersal and Mating Pattern in Domestication of Intermediate Wheatgrass, a Novel Perennial Food Crop

Intermediate wheatgrass (IWG) is a perennial forage grass that is currently being domesticated as a grain crop. It is a primarily wind-pollinated outcrossing species and expresses severe inbreeding depression when self-pollinated. Characterization of pollen dispersal, mating parameters, and change in genetic diversity due to pollen movement is currently lacking in IWG. In this study, we examined pollen dispersal in an IWG selection nursery by evaluating 846 progeny from 15 mother plants and traced their parentage to 374 fathers. A set of 2,500 genomic loci was used to characterize the population. We assigned paternity to 769 (91%) progeny and the average number of fathers per mother plant was 37, from an average of 56 progeny examined per mother. An extensive number (80%) of pollination events occurred within 10 m of the mother plants. Pollination success was not correlated with trait attributes of the paternal genotypes. Mating system analysis confirmed that IWG is highly outcrossing and inbreeding was virtually absent. Neither genetic diversity nor the genome-estimated trait values of progeny were significantly affected by pollinator distance. The distance of pollinator in an IWG breeding nursery therefore was not found to be a major contributor in maintaining genetic diversity. These findings reveal the pollen dispersal model in IWG for the first time and its effect on genetic diversity, which will be valuable in designing future IWG breeding populations. Information generated and discussed in this study could be applied in understanding gene flow and genetic diversity of other open-pollinated species.

Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools

Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

Background

Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2.

Results

In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model.

Conclusions

The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08487-8.

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.)

Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01295-8.

Genetic Diversity and Selection Footprints in the Genome of Brazilian Soybean Cultivars

Although Brazil is currently the largest soybean producer in the world, only a small number of studies have analyzed the genetic diversity of Brazilian soybean. These studies have shown the existence of a narrow genetic base. The objectives of this work were to analyze the population structure and genetic diversity, and to identify selection signatures in the genome of soybean germplasms from different companies in Brazil. A panel consisting of 343 soybean lines from Brazil, North America, and Asia was genotyped using genotyping by sequencing (GBS). Population structure was assessed by Bayesian and multivariate approaches. Genetic diversity was analyzed using metrics such as the fixation index, nucleotide diversity, genetic dissimilarity, and linkage disequilibrium. The software BayeScan was used to detect selection signatures between Brazilian and Asian accessions as well as among Brazilian germplasms. Region of origin, company of origin, and relative maturity group (RMG) all had a significant influence on population structure. Varieties belonging to the same company and especially to the same RMG exhibited a high level of genetic similarity. This result was exacerbated among early maturing accessions. Brazilian soybean showed significantly lower genetic diversity when compared to Asian accessions. This was expected, because the crop's region of origin is its main genetic diversity reserve. We identified 7 genomic regions under selection between the Brazilian and Asian accessions, and 27 among Brazilian varieties developed by different companies. Associated with these genomic regions, we found 96 quantitative trait loci (QTLs) for important soybean breeding traits such as flowering, maturity, plant architecture, productivity components, pathogen resistance, and seed composition. Some of the QTLs associated with the markers under selection have genes of great importance to soybean's regional adaptation. The results reported herein allowed to expand the knowledge about the organization of the genetic variability of the Brazilian soybean germplasm. Furthermore, it was possible to identify genomic regions under selection possibly associated with the adaptation of soybean to Brazilian environments.

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize

BACKGROUND

Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.

RESULTS

Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.

CONCLUSIONS

We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.

Genome Wide Association Study Uncovers the QTLome for Osmotic Adjustment and Related Drought Adaptive Traits in Durum Wheat

Osmotic adjustment (OA) is a major component of drought resistance in crops. The genetic basis of OA in wheat and other crops remains largely unknown. In this study, 248 field-grown durum wheat elite accessions grown under well-watered conditions, underwent a progressively severe drought treatment started at heading. Leaf samples were collected at heading and 17 days later. The following traits were considered: flowering time (FT), leaf relative water content (RWC), osmotic potential (ψs), OA, chlorophyll content (SPAD), and leaf rolling (LR). The high variability (3.89-fold) in OA among drought-stressed accessions resulted in high repeatability of the trait (h² = 72.3%). Notably, a high positive correlation (r = 0.78) between OA and RWC was found under severe drought conditions. A genome-wide association study (GWAS) revealed 15 significant QTLs (Quantitative Trait Loci) for OA (global R² = 63.6%), as well as eight major QTL hotspots/clusters on chromosome arms 1BL, 2BL, 4AL, 5AL, 6AL, 6BL, and 7BS, where a higher OA capacity was positively associated with RWC and/or SPAD, and negatively with LR, indicating a beneficial effect of OA on the water status of the plant. The comparative analysis with the results of 15 previous field trials conducted under varying water regimes showed concurrent effects of five OA QTL cluster hotspots on normalized difference vegetation index (NDVI), thousand-kernel weight (TKW), and/or grain yield (GY). Gene content analysis of the cluster regions revealed the presence of several candidate genes, including bidirectional sugar transporter SWEET, rhomboid-like protein, and S-adenosyl-L-methionine-dependent methyltransferases superfamily protein, as well as DREB1. Our results support OA as a valuable proxy for marker-assisted selection (MAS) aimed at enhancing drought resistance in wheat.

Population genomics and geographic dispersal in Chagas disease vectors: Landscape drivers and evidence of possible adaptation to the domestic setting

Accurate prediction of vectors dispersal, as well as identification of adaptations that allow blood-feeding vectors to thrive in built environments, are a basis for effective disease control. Here we adopted a landscape genomics approach to assay gene flow, possible local adaptation, and drivers of population structure in Rhodnius ecuadoriensis, an important vector of Chagas disease. We used a reduced-representation sequencing technique (2b-RADseq) to obtain 2,552 SNP markers across 272 R. ecuadoriensis samples from 25 collection sites in southern Ecuador. Evidence of high and directional gene flow between seven wild and domestic population pairs across our study site indicates insecticide-based control will be hindered by repeated re-infestation of houses from the forest. Preliminary genome scans across multiple population pairs revealed shared outlier loci potentially consistent with local adaptation to the domestic setting, which we mapped to genes involved with embryogenesis and saliva production. Landscape genomic models showed elevation is a key barrier to R. ecuadoriensis dispersal. Together our results shed early light on the genomic adaptation in triatomine vectors and facilitate vector control by predicting that spatially-targeted, proactive interventions would be more efficacious than current, reactive approaches.

Re-infestation of recently insecticide-treated houses by wild/secondary triatomine, their potential adaptation to this new environment and capabilities to geographically disperse across multiple human communities jeopardise sustainable Chagas disease control. This is the first study in Chagas disease vectors that identifies genomic regions possibly linked to adaptations to the built environment and describes landscape drivers for accurate prediction of geographic dispersal. We sampled multiple domestic and wild Rhodnius ecuadoriensis population pairs across a mountainous terrain in southern Ecuador. We evidenced that triatomine movement from forest to built enviroments does occur at a high rate. In these highly connected population pairs we detected loci possibly linked to local adaptation among the genomic makers we evaluated and in doing so we pave the way for future triatomine genomic research. We highlighted that current haphazardous vector control in the zone will be hindered by reinfestation of triatomines from the forest. Instead, we recommend frequent and spatially-targeted vector control and provided a landacape genomic model that identifies highly connected and isolated triatomine populations to facilitate efficient vector control.

The performance of phenomic selection depends on the genetic architecture of the target trait

The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

Phenotyping and Quantitative Trait Locus Analysis for the Limited Transpiration Trait in an Upper-Mid South Soybean Recombinant Inbred Line Population ("Jackson" × "KS4895"): High Throughput Aquaporin Inhibitor Screening

Soybean is most often grown under rainfed conditions and negatively impacted by drought stress in the upper mid-south of the United States. Therefore, identification of drought-tolerance traits and their corresponding genetic components are required to minimize drought impacts on productivity. Limited transpiration (TRlim) under high vapor pressure deficit (VPD) is one trait that can help conserve soybean water-use during late-season drought. The main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO3, for the TRlim trait. A secondary objective was to undertake a genetic marker/quantitative trait locus (QTL) genetic analysis using the AgNO3 phenotyping results. A set of 122 soybean genotypes (120-RILs and parents) were grown in controlled environments (32/25-d/n °C). The transpiration rate (TR) responses of derooted soybean shoots before and after application of AgNO3 were measured under 37°C and >3.0 kPa VPD. Then, the decrease in transpiration rate (DTR) for each genotype was determined. Based on DTR rate, a diverse group (slow, moderate, and high wilting) of 26 RILs were selected and tested for the whole plant TRs under varying levels of VPD (0.0-4.0 kPa) at 32 and 37°C. The phenotyping results showed that 88% of slow, 50% of moderate, and 11% of high wilting genotypes expressed the TRlim trait at 32°C and 43, 10, and 0% at 37°C, respectively. Genetic mapping with the phenotypic data we collected revealed three QTL across two chromosomes, two associated with TRlim traits and one associated with leaf temperature. Analysis of Gene Ontologies of genes within QTL regions identified several intriguing candidate genes, including one gene that when overexpressed had previously been shown to confer enhanced tolerance to abiotic stress. Collectively these results will inform and guide ongoing efforts to understand how to deploy genetic tolerance for drought stress.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut (Arachis hypogaea L.)

The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here, we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect-increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanuts, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (P-value < 1.0E-16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization

Array-based single nucleotide polymorphism (SNP) genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Illumina Infinium Wheat Barley 40K SNP array Version 1.0. We show that the approach delivers high quality and high resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimizing ascertainment bias. Comprising mostly biallelic markers that were designed to be species-specific and single-copy, the array permits highly accurate imputation in diverse germplasm to improve the statistical power of genome-wide association studies (GWAS) and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and Aegilops tauschii Coss. (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheat, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley, as well as direct connections to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool, Pretzel (https://plantinformatics.io/) which enables the content of the array to be visualized and interrogated interactively in the context of numerous genetic and genomic resources to be connected more seamlessly to research and breeding. The array is available for use by the international wheat and barley community.

Genomic regions and candidate genes linked with Phytophthora capsici root rot resistance in chile pepper (Capsicum annuum L.)

BACKGROUND

Phytophthora root rot, caused by Phytophthora capsici, is a major disease affecting Capsicum production worldwide. A recombinant inbred line (RIL) population derived from the hybridization between 'Criollo de Morellos-334' (CM-334), a resistant landrace from Mexico, and 'Early Jalapeno', a susceptible cultivar was genotyped using genotyping-by-sequencing (GBS)-derived single nucleotide polymorphism (SNP) markers. A GBS-SNP based genetic linkage map for the RIL population was constructed. Quantitative trait loci (QTL) mapping dissected the genetic architecture of P. capsici resistance and candidate genes linked to resistance for this important disease were identified.

RESULTS

Development of a genetic linkage map using 1,973 GBS-derived polymorphic SNP markers identified 12 linkage groups corresponding to the 12 chromosomes of chile pepper, with a total length of 1,277.7 cM and a marker density of 1.5 SNP/cM. The maximum gaps between consecutive SNP markers ranged between 1.9 (LG7) and 13.5 cM (LG5). Collinearity between genetic and physical positions of markers reached a maximum of 0.92 for LG8. QTL mapping identified genomic regions associated with P. capsici resistance in chromosomes P5, P8, and P9 that explained between 19.7 and 30.4% of phenotypic variation for resistance. Additive interactions between QTL in chromosomes P5 and P8 were observed. The role of chromosome P5 as major genomic region containing P. capsici resistance QTL was established. Through candidate gene analysis, biological functions associated with response to pathogen infections, regulation of cyclin-dependent protein serine/threonine kinase activity, and epigenetic mechanisms such as DNA methylation were identified.

CONCLUSIONS

Results support the genetic complexity of the P. capsici-Capsicum pathosystem and the possible role of epigenetics in conferring resistance to Phytophthora root rot. Significant genomic regions and candidate genes associated with disease response and gene regulatory activity were identified which allows for a deeper understanding of the genomic landscape of Phytophthora root rot resistance in chile pepper.

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

New Genotypes and Genomic Regions for Resistance to Wheat Blast in South Asian Germplasm

Wheat blast (WB) disease, since its first identification in Bangladesh in 2016, is now an established serious threat to wheat production in South Asia. There is a need for sound knowledge about resistance sources and associated genomic regions to assist breeding programs. Hence, a panel of genotypes from India and Bangladesh was evaluated for wheat blast resistance and a genome-wide association study (GWAS) was performed. Disease evaluation was done during five crop seasons-at precision phenotyping platform (PPPs) for wheat blast disease at Jashore (2018-19), Quirusillas (2018-19 and 2019-20) and Okinawa (2019 and 2020). Single nucleotide polymorphisms (SNP) across the genome were obtained using DArTseq genotyping-by-sequencing platform, and in total 5713 filtered markers were used. GWAS revealed 40 significant markers associated with WB resistance, of which 33 (82.5%) were in the 2NS/2AS chromosome segment and one each on seven chromosomes (3B, 3D, 4A, 5A, 5D, 6A and 6B). The 2NS markers contributed significantly in most of the environments, explaining an average of 33.4% of the phenotypic variation. Overall, 22.4% of the germplasm carried 2NS/2AS segment. So far, 2NS translocation is the only effective WB resistance source being used in the breeding programs of South Asia. Nevertheless, the identification of non-2NS/2AS genomic regions for WB resistance provides a hope to broaden and diversify resistance for this disease in years to come.

New drought-adaptive loci underlying candidate genes on wheat chromosome 4B with improved photosynthesis and yield responses

Flag leaf serves as an essential source of assimilates during grain filling, thereby contributing to grain yield up to 48%. Thus, high-throughput phenotyping of flag leaves is crucial to determine their physiological and genetic basis of yield formation and drought adaptation. Here, we utilized 200 wheat cultivars to identify drought-adaptive loci underlying candidate genes associated with flag leaf biomass and photosynthesis-related traits using a genome-wide association study (GWAS). GWAS revealed 21 significant marker-trait associations for key photosynthetic traits in response to drought stress. Analysis of linkage disequilibrium (LD) in these SNPs intervals discovered 103 significant SNPs that established distinct LD blocks containing a total of 382 candidate genes putatively involved in physiological processes, including photosynthesis and water responses. Further, in silico transcript analysis identified two candidate genes in locus AX-580365925 on chromosome 4B, those were found to be highly expressed under drought and associated with proton-transporting ATP synthase activity and stress response pathways. Accordingly, we identified significant allelic haplotype differences on this same locus. The tolerant haplotype (higher chlorophyll content under drought) representing major allele was more abundant and stably increased photosynthetic efficiency and yield under drought scenarios. Collectively, this study offers new adaptive loci and beneficial alleles to reshape the flag leaf physiological and associated photosynthetic components for better yield and sustainability to water-deficit stress.

Selective Sweeps and Polygenic Adaptation Drive Local Adaptation along Moisture and Temperature Gradients in Natural Populations of Coast Redwood and Giant Sequoia

Dissecting the genomic basis of local adaptation is a major goal in evolutionary biology and conservation science. Rapid changes in the climate pose significant challenges to the survival of natural populations, and the genomic basis of long-generation plant species is still poorly understood. Here, we investigated genome-wide climate adaptation in giant sequoia and coast redwood, two iconic and ecologically important tree species. We used a combination of univariate and multivariate genotype-environment association methods and a selective sweep analysis using non-overlapping sliding windows. We identified genomic regions of potential adaptive importance, showing strong associations to moisture variables and mean annual temperature. Our results found a complex architecture of climate adaptation in the species, with genomic regions showing signatures of selective sweeps, polygenic adaptation, or a combination of both, suggesting recent or ongoing climate adaptation along moisture and temperature gradients in giant sequoia and coast redwood. The results of this study provide a first step toward identifying genomic regions of adaptive significance in the species and will provide information to guide management and conservation strategies that seek to maximize adaptive potential in the face of climate change.

Identification of an Elite Core Panel as a Key Breeding Resource to Accelerate the Rate of Genetic Improvement for Irrigated Rice

Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield. In an effort to identify and characterize the elite breeding pool of IRRI's irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012-2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha^-1. The rate of genetic gain for grain yield was estimated at 8.75 kg·ha^-1 year^-1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha^-1 year^-1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha^-1 cycle^-1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential. We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.

Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks

The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

QTL for seed shattering and threshability in intermediate wheatgrass align closely with well-studied orthologs from wheat, barley, and rice

Perennial grain crops have the potential to improve agricultural sustainability but few existing species produce sufficient grain yield to be economically viable. The outcrossing, allohexaploid, and perennial forage species intermediate wheatgrass (IWG) [Thinopyrum intermedium (Host) Barkworth & D. R. Dewey] has shown promise in undergoing direct domestication as a perennial grain crop using phenotypic and genomic selection. However, decades of selection will be required to achieve yields on par with annual small-grain crops. Marker-aided selection could accelerate progress if important genomic regions associated with domestication were identified. Here we use the IWG nested association mapping (NAM) population, with 1,168 F₁ progeny across 10 families to dissect the genetic control of brittle rachis, floret shattering, and threshability. We used a genome-wide association study (GWAS) with 8,003 single nucleotide polymorphism (SNP) markers and linkage mapping-both within-family and combined across families-with a robust phenotypic dataset collected from four unique year-by-location combinations. A total of 29 quantitative trait loci (QTL) using GWAS and 20 using the combined linkage analysis were detected, and most large-effect QTL were in common across the two analysis methods. We reveal that the genetic control of these traits in IWG is complex, with significant QTL across multiple chromosomes, sometimes within and across homoeologous groups and effects that vary depending on the family. In some cases, these QTL align within 216 bp to 31 Mbp of BLAST hits for known domestication genes in related species and may serve as precise targets of selection and directions for further study to advance the domestication of IWG.

Genome-wide association mapping of Pyrenophora teres f. maculata and Pyrenophora teres f. teres resistance loci utilizing natural Turkish wild and landrace barley populations

Unimproved landraces and wild relatives of crops are sources of genetic diversity that were lost post domestication in modern breeding programs. To tap into this rich resource, genome-wide association studies in large plant genomes have enabled the rapid genetic characterization of desired traits from natural landrace and wild populations. Wild barley (Hordeum spontaneum), the progenitor of domesticated barley (Hordeum vulgare), is dispersed across Asia and North Africa, and has co-evolved with the ascomycetous fungal pathogens Pyrenophora teres f. teres and P. teres f. maculata, the causal agents of the diseases net form of net blotch and spot form of net blotch, respectively. Thus, these wild and local adapted barley landraces from the region of origin of both the host and pathogen represent a diverse gene pool to identify new sources of resistance, due to millions of years of co-evolution. The barley—P. teres pathosystem is governed by complex genetic interactions with dominant, recessive, and incomplete resistances and susceptibilities, with many isolate-specific interactions. Here, we provide the first genome-wide association study of wild and landrace barley from the Fertile Crescent for resistance to both forms of P. teres. A total of 14 loci, four against P. teres f. maculata and 10 against P. teres f. teres, were identified in both wild and landrace populations, showing that both are genetic reservoirs for novel sources of resistance. We also highlight the importance of using multiple algorithms to both identify and validate additional loci.

Genomic prediction for testes weight of the tiger pufferfish, Takifugu rubripes, using medium to low density SNPs

Aquaculture production is expected to increase with the help of genomic selection (GS). The possibility of performing GS using only a small number of SNPs has been examined in order to reduce genotyping costs; however, the practicality of this approach is still unclear. Here, we tested whether the effects of reducing the number of SNPs impaired the prediction accuracy of GS for standard length, body weight, and testes weight in the tiger pufferfish (Takifugu rubripes). High values for predictive ability (0.563-0.606) were obtained with 4000 SNPs for all traits under a genomic best linear unbiased predictor (GBLUP) model. These values were still within an acceptable range with 1200 SNPs (0.554-0.588). However, predictive abilities and prediction accuracies deteriorated using less than 1200 SNPs largely due to the reduced power in accurately estimating the genetic relationship among individuals; family structure could still be resolved with as few as 400 SNPs. This suggests that the SNPs informative for estimation of genetic relatedness among individuals differ from those for inference of family structure, and that non-random SNP selection based on the effects on family structure (e.g., site-F_ST, principal components, or random forest) is unlikely to increase the prediction accuracy for these traits.

Genetic architecture underlying the expression of eight α-amylase trypsin inhibitors

KEY MESSAGE

Wheat cultivars largely differ in the content and composition of ATI proteins, but heritability was quite low for six out of eight ATIs. The genetic architecture of ATI proteins is built up of few major and numerous small effect QTL. Amylase trypsin inhibitors (ATIs) are important allergens in baker's asthma and suspected triggers of non-celiac wheat sensitivity (NCWS) inducing intestinal and extra-intestinal inflammation. As studies on the expression and genetic architecture of ATI proteins in wheat are lacking, we evaluated 149 European old and modern bread wheat cultivars grown at three different field locations for their content of eight ATI proteins. Large differences in the content and composition of ATIs in the different cultivars were identified ranging from 3.76 pmol for ATI CM2 to 80.4 pmol for ATI 0.19, with up to 2.5-fold variation in CM-type and up to sixfold variation in mono/dimeric ATIs. Generally, heritability estimates were low except for ATI 0.28 and ATI CM2. ATI protein content showed a low correlation with quality traits commonly analyzed in wheat breeding. Similarly, no trends were found regarding ATI content in wheat cultivars originating from numerous countries and decades of breeding history. Genome-wide association mapping revealed a complex genetic architecture built of many small, few medium and two major quantitative trait loci (QTL). The major QTL were located on chromosomes 3B for ATI 0.19-like and 6B for ATI 0.28, explaining 70.6 and 68.7% of the genotypic variance, respectively. Within close physical proximity to the medium and major QTL, we identified eight potential candidate genes on the wheat reference genome encoding structurally related lipid transfer proteins. Consequently, selection and breeding of wheat cultivars with low ATI protein amounts appear difficult requiring other strategies to reduce ATI content in wheat products.

Genomic prediction for fusiform rust disease incidence in a large cloned population of Pinus taeda

In this study, 723 Pinus taeda L. (loblolly pine) clonal varieties genotyped with 16920 SNP markers were used to evaluate genomic selection for fusiform rust disease caused by the fungus Cronartium quercuum f. sp. fusiforme. The 723 clonal varieties were from five full-sib families. They were a subset of a larger population (1831 clonal varieties), field-tested across 26 locations in the southeast US. Ridge regression, Bayes B, and Bayes Cπ models were implemented to study marker-trait associations and estimate predictive ability for selection. A cross-validation scenario based on a random sampling of 80% of the clonal varieties for the model building had higher (0.71-0.76) prediction accuracies of genomic estimated breeding values compared with family and within-family cross-validation scenarios. Random sampling within families for model training to predict genomic estimated breeding values of the remaining progenies within each family produced accuracies between 0.38 and 0.66. Using four families out of five for model training was not successful. The results showed the importance of genetic relatedness between the training and validation sets. Bayesian whole-genome regression models detected three QTL with large effects on the disease outcome, explaining 54% of the genetic variation in the trait. The significance of QTL was validated with GWAS while accounting for the population structure and polygenic effect. The odds of disease incidence for heterozygous AB genotypes were 10.7 and 12.1 times greater than the homozygous AA genotypes for SNP11965 and SNP6347 loci, respectively. Genomic selection for fusiform rust disease incidence could be effective in P. taeda breeding. Markers with large effects could be fit as fixed covariates to increase the prediction accuracies, provided that their effects are validated further.

Genome-Wide Association Studies for Fusarium Head Blight Resistance and Its Trade-Off With Grain Yield in Soft Red Winter Wheat

Identification of quantitative trait loci for Fusarium head blight (FHB) resistance from different sources and pyramiding them into cultivars could provide effective protection against FHB. The objective of this study was to characterize a soft red winter wheat (SRWW) breeding population that has been subjected to intense germplasm introduction and alien introgression for FHB resistance in the past. The population was evaluated under misted FHB nurseries inoculated with Fusarium graminearum-infested corn spawn for two years. Phenotypic data included disease incidence (INC), disease severity (SEV), Fusarium damaged kernels (FDK), FHB index (FHBdx), and deoxynivalenol concentration (DON). Genome-wide association studies using 13,784 SNP markers identified 25 genomic regions at -logP ≥ 4.0 that were associated with five FHB-related traits. Of these 25, the marker trait associations that explained more than 5% phenotypic variation were localized on chromosomes 1A, 2B, 3B, 5A, 7A, 7B, and 7D, and from diverse sources including adapted SRWW lines such as Truman and Bess, and unadapted common wheat lines such as Ning7840 and Fundulea 201R. Furthermore, individuals with favorable alleles at the four loci Fhb1, Qfhb.nc-2B.1 (Q_2B.1), Q_7D.1, and Q_7D.2 showed better FDK and DON scores (but not INC, SEV, and FHBdx) compared with other allelic combinations. Our data also showed while pyramiding multiple loci provides protection against FHB disease, it has a significant trade-off with grain yield.

The details are in the genome-wide SNPs: Fine scale evolution of the Malaysian weedy rice

Weedy rice (Oryza spp.) is a major nuisance to rice farmers from all over the world. Although the emergence of weedy rice in East Malaysia on the island of Borneo is very recent, the threat to rice yield has reached an alarming stage. Using 47,027 genotyping-by-sequencing (GBS)-derived SNPs and candidate gene analysis of the plant architecture domestication gene TAC1, we assessed the genetic variations and evolutionary origin of weedy rice in East Malaysia. Our findings revealed two major evolutionary paths for genetically distinct weedy rice types. Whilst the cultivar-like weedy rice are very likely to be the weedy descendant of local coexisting cultivars, the wild-like weedy rice appeared to have arisen through two possible routes: (i) accidental introduction from Peninsular Malaysia weedy rice populations, and (ii) weedy descendants of coexisting cultivars. The outcome of our genetic analyses supports the notion that Sabah cultivars and Peninsular Malaysia weedy rice are the potential progenitors of Sabah weedy rice. Similar TAC1 haplotypes were shared between Malaysian cultivated and weedy rice populations, which further supported the findings of our GBS-SNP analyses. These different strains of weedy rice have convergently evolved shared traits, such as seeds shattering and open tillers. A comparison with our previous simple-sequence repeat-based population genetic analyses highlights the strength of genome-wide SNPs, including detection of admixtures and low-level introgression events. These findings could inform better strategic management for controlling the spread of weedy rice in the region.

Genomic Variation Shaped by Environmental and Geographical Factors in Prairie Cordgrass Natural Populations Collected across Its Native Range in the USA

Prairie cordgrass (Spartina pectinata Link) is a native perennial warm-season (C4) grass common in North American prairies. With its high biomass yield and abiotic stress tolerance, there is a high potential of developing prairie cordgrass for conservation practices and as a dedicated bioenergy crop for sustainable cellulosic biofuel production. However, as with many other undomesticated grass species, little information is known about the genetic diversity or population structure of prairie cordgrass natural populations as compared to their ecotypic and geographic adaptation in North America. In this study, we sampled and characterized a total of 96 prairie cordgrass natural populations with 9315 high quality SNPs from a genotyping-by-sequencing (GBS) approach. The natural populations were collected from putative remnant prairie sites throughout the Midwest and Eastern USA, which are the major habitats for prairie cordgrass. Partitioning of genetic variance using SNP marker data revealed significant variance among and within populations. Two potential gene pools were identified as being associated with ploidy levels, geographical separation, and climatic separation. Geographical factors such as longitude and altitude, and environmental factors such as annual temperature, annual precipitation, temperature of the warmest month, precipitation of the wettest month, precipitation of Spring, and precipitation of the wettest month are important in affecting the intraspecific distribution of prairie cordgrass. The divergence of prairie cordgrass natural populations also provides opportunities to increase breeding value of prairie cordgrass as a bioenergy and conservation crop.

Identification and Fine-Mapping of Quantitative Trait Loci Controlling Plant Height in Central European Winter Triticale (×Triticosecale Wittmack)

The quantitatively inherited trait plant height is routinely evaluated in triticale breeding programs as it substantially influences lodging and disease susceptibility, is a main contributor to biomass yield, and is required to improve hybrid seed production by fine-tuning plant height in the female and male parental pools in hybrid breeding programs. In this study, we evaluated a panel of 846 diverse Central European triticale genotypes to dissect the genetic architecture underlying plant height by genome-wide association mapping. This revealed three medium- to large-effect QTL on chromosomes 5A, 4B, and 5R. Genetic and physical fine-mapping of the putative QTL revealed that the QTL on chromosome 5R most likely corresponds to Ddw1 and that the QTL on chromosome 5A is likely to be Rht12. Furthermore, we observed a temporal trend in registered cultivars with a decreasing plant height during the past decades, accompanied by an increasing use of the height-reducing alleles at the identified QTL. In summary, our results shed new light on the genetic control of plant height in triticale and open new avenues for future improvement by breeding.

Cryptic introgressions contribute to transgressive segregation for early blight resistance in tomato

We identified cryptic early blight resistance introgressions in tomato breeding lines and demonstrated efficient genotypic selection for resistance in the context of a tomato breeding program. Early blight is a widespread and problematic disease affecting tomatoes (Solanum lycopersicum). Caused by the fungal pathogen Alternaria linariae (syn. A. tomatophila), symptoms include lesions on tomato stems, fruit, and foliage, often resulting in yield losses. Breeding tomatoes with genetic resistance would enhance production sustainability. Using cross-market breeding populations, we identified several quantitative trait loci (QTL) associated with early blight resistance. Early blight resistance putatively derived from 'Campbell 1943' was confirmed in modern fresh market tomato breeding lines. This resistance offered substantial protection against early blight stem lesions (collar rot) and moderate protection from defoliation. A distinctive and potentially novel form of early blight foliar resistance was discovered in a processing tomato breeding line and is probably derived from S. pimpinellifolium via 'Hawaii 7998'. Additional field trials validated the three most promising large-effect QTL, EB-1.2, EB-5, and EB-9. Resistance effects for EB-5 and EB-9 were consistent across breeding populations and environments, while EB-1.2's effect was population specific. Using genome-wide marker-assisted backcrossing, we developed fresh market tomato lines that were near-isogenic for early blight QTL. Resistance in these lines was largely mediated by just two QTL, EB-5 and EB-9, that together captured 49.0 and 68.7% of the defoliation and stem lesion variance, respectively. Our work showcases the value of mining cryptic introgressions in tomato lines, and across market classes, for use as additional sources of disease resistance.

Identification and Confirmation of Loci Associated With Canopy Wilting in Soybean Using Genome-Wide Association Mapping

Drought causes significant soybean [Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship (P ≤ 0.05;0.37 ≤ r ≤ 0.5) was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance.