Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm

Deciphering genetic factors contributing to enhanced resistance against Cercospora leaf blight in soybean (Glycine max L.) using GWAS analysis

Cercospora leaf blight (CLB), caused by Cercospora cf. flagellaris, C. kikuchii, and C. cf. sigesbeckiae, is a significant soybean [Glycine max (L.) Merr.] disease in regions with hot and humid conditions causing yield loss in the United States and Canada. There is limited information regarding resistant soybean cultivars, and there have been marginal efforts to identify the genomic regions underlying resistance to CLB. A Genome-Wide Association Study was conducted using a diverse panel of 460 soybean accessions from maturity groups III to VII to identify the genomic regions associated to the CLB disease. These accessions were evaluated for CLB in different regions of the southeastern United States over 3 years. In total, the study identified 99 Single Nucleotide Polymorphism (SNPs) associated with the disease severity and 85 SNPs associated with disease incidence. Across multiple environments, 47 disease severity SNPs and 23 incidence SNPs were common. Candidate genes within 10 kb of these SNPs were involved in biotic and abiotic stress pathways. This information will contribute to the development of resistant soybean germplasm. Further research is warranted to study the effect of pyramiding desirable genomic regions and investigate the role of identified genes in soybean CLB resistance.

Deciphering the genetic architecture of resistance to Corynespora cassiicola in soybean (Glycine max L.) by integrating genome-wide association mapping and RNA-Seq analysis

Target spot caused by Corynespora cassiicola is a problematic disease in tropical and subtropical soybean (Glycine max) growing regions. Although resistant soybean genotypes have been identified, the genetic mechanisms underlying target spot resistance has not yet been studied. To address this knowledge gap, this is the first genome-wide association study (GWAS) conducted using the SoySNP50K array on a panel of 246 soybean accessions, aiming to unravel the genetic architecture of resistance. The results revealed significant associations of 14 and 33 loci with resistance to LIM01 and SSTA C. cassiicola isolates, respectively, with six loci demonstrating consistent associations across both isolates. To identify potential candidate genes within GWAS-identified loci, dynamic transcriptome profiling was conducted through RNA-Seq analysis. The analysis involved comparing gene expression patterns between resistant and susceptible genotypes, utilizing leaf tissue collected at different time points after inoculation. Integrating results of GWAS and RNA-Seq analyses identified 238 differentially expressed genes within a 200 kb region encompassing significant quantitative trait loci (QTLs) for disease severity ratings. These genes were involved in defense response to pathogen, innate immune response, chitinase activity, histone H3-K9 methylation, salicylic acid mediated signaling pathway, kinase activity, and biosynthesis of flavonoid, jasmonic acid, phenylpropanoid, and wax. In addition, when combining results from this study with previous GWAS research, 11 colocalized regions associated with disease resistance were identified for biotic and abiotic stress. This finding provides valuable insight into the genetic resources that can be harnessed for future breeding programs aiming to enhance soybean resistance against target spot and other diseases simultaneously.

Multi-omics assisted breeding for biotic stress resistance in soybean

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

Breeding for disease resistance in soybean: a global perspective

KEY MESSAGE

This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat

This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.

Molecular Breeding to Overcome Biotic Stresses in Soybean: Update

Soybean (Glycine max (L.) Merr.) is an important leguminous crop and biotic stresses are a global concern for soybean growers. In recent decades, significant development has been carried outtowards identification of the diseases caused by pathogens, sources of resistance and determination of loci conferring resistance to different diseases on linkage maps of soybean. Host-plant resistance is generally accepted as the bestsolution because of its role in the management of environmental and economic conditions of farmers owing to low input in terms of chemicals. The main objectives of soybean crop improvement are based on the identification of sources of resistance or tolerance against various biotic as well as abiotic stresses and utilization of these sources for further hybridization and transgenic processes for development of new cultivars for stress management. The focus of the present review is to summarize genetic aspects of various diseases caused by pathogens in soybean and molecular breeding research work conducted to date.

Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea

Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines' advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high-throughput phenotyping (normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31-0.71) and narrow-sense heritability (0.13-0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0-S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6-50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Soybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application ( https://soyfungigcn.venanciogroup.uenf.br/ ) and an R/Shiny package ( https://github.com/almeidasilvaf/SoyFungiGCN ) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations

Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties. Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71-0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium, plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.

Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections

KEY MESSAGE

Genomic prediction of quantitative resistance toward Phytophthora sojae indicated that genomic selection may increase breeding efficiency. Statistical model and marker set had minimal effect on genomic prediction with > 1000 markers. Quantitative disease resistance (QDR) toward Phytophthora sojae in soybean is a complex trait controlled by many small-effect loci throughout the genome. Along with the technical and rate-limiting challenges of phenotyping resistance to a root pathogen, the trait complexity can limit breeding efficiency. However, the application of genomic prediction to traits with complex genetic architecture, such as QDR toward P. sojae, is likely to improve breeding efficiency. We provide a novel example of genomic prediction by measuring QDR to P. sojae in two diverse panels of more than 450 plant introductions (PIs) that had previously been genotyped with the SoySNP50K chip. This research was completed in a collection of diverse germplasm and contributes to both an initial assessment of genomic prediction performance and characterization of the soybean germplasm collection. We tested six statistical models used for genomic prediction including Bayesian Ridge Regression; Bayesian LASSO; Bayes A, B, C; and reproducing kernel Hilbert spaces. We also tested how the number and distribution of SNPs included in genomic prediction altered predictive ability by varying the number of markers from less than 50 to more than 34,000 SNPs, including SNPs based on sequential sampling, random sampling, or selections from association analyses. Predictive ability was relatively independent of statistical model and marker distribution, with a diminishing return when more than 1000 SNPs were included in genomic prediction. This work estimated relative efficiency per breeding cycle between 0.57 and 0.83, which may improve the genetic gain for P. sojae QDR in soybean breeding programs.

High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenne L.)

The natural genetic diversity of agricultural species is an essential genetic resource for breeding programs aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations (France, Belgium, Germany). Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species.

Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality

Trait-assisted genomic prediction approach is a way to improve genetic gain by cost unit, by reducing budget allocated to phenotyping or by increasing the program's size for the same budget. This study compares different strategies of genomic prediction to optimize resource allocation in breeding schemes by using information from cheaper correlated traits to predict a more expensive trait of interest. We used bread wheat baking score (BMS) calculated for French registration as a case study. To conduct this project, 398 lines from a public breeding program were genotyped and phenotyped for BMS and correlated traits in 11 locations in France between 2000 and 2016. Single-trait (ST), multi-trait (MT) and trait-assisted (TA) strategies were compared in terms of predictive ability and cost. In MT and TA strategies, information from dough strength (W), a cheaper trait correlated with BMS (r = 0.45), was evaluated in the training population or in both the training and the validation sets, respectively. TA models allowed to reduce the budget allocated to phenotyping by up to 65% while maintaining the predictive ability of BMS. TA models also improved the predictive ability of BMS compared to ST models for a fixed budget (maximum gain: + 0.14 in cross-validation and + 0.21 in forward prediction). We also demonstrated that the budget can be further reduced by approximately one fourth while maintaining the same predictive ability by reducing the number of phenotypic records to estimate BMS adjusted means. In addition, we showed that the choice of the lines to be phenotyped can be optimized to minimize cost or maximize predictive ability. To do so, we extended the mean of the generalized coefficient of determination (CD_mean) criterion to the multi-trait context (CD_multi).

Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP). However, records for several traits at once are usually documented for breeding lines in commercial breeding programs. By incorporating benefits from genetic characterizations of correlated phenotypes, multiple trait genomic prediction (MTGP) may be a useful tool for improving prediction accuracy in genetic evaluations. The objective of this study was to test whether the use of MTGP and including proper modeling of spatial effects can improve the prediction accuracy of breeding values in commercial barley and wheat breeding lines. We genotyped 1,317 spring barley and 1,325 winter wheat lines from a commercial breeding program with the Illumina 9K barley and 15K wheat SNP-chip (respectively) and phenotyped them across multiple years and locations. Results showed that the MTGP approach increased correlations between future performance and estimated breeding value of yields by 7% in barley and by 57% in wheat relative to using the STGP approach for each trait individually. Analyses combining genomic data, pedigree information, and proper modeling of spatial effects further increased the prediction accuracy by 4% in barley and 3% in wheat relative to the model using genomic relationships only. The prediction accuracy for yield in wheat and barley yield trait breeding, were improved by combining MTGP and spatial effects in the model.

Fine mapping of a Phytophthora-resistance locus RpsGZ in soybean using genotyping-by-sequencing

BACKGROUND

Phytophthora root rot (PRR) caused by Phytophthora sojae (P. sojae) is one of the most serious limitations to soybean production worldwide. The identification of resistance gene(s) and their incorporation into elite varieties is an effective approach for breeding to prevent soybean from being harmed by this disease. A valuable mapping population of 228 F8:11 recombinant inbred lines (RILs) derived from a cross of the resistant cultivar Guizao1 and the susceptible cultivar BRSMG68 and a high-density genetic linkage map with an average distance of 0.81 centimorgans (cM) between adjacent bin markers in this population were used to map and explore candidate gene(s).

RESULTS

PRR resistance in Guizao1 was found to be controlled by a single Mendelian locus and was finely mapped to a 367.371-kb genomic region on chromosome 3 harbouring 19 genes, including 7 disease resistance (R)-like genes, in the reference Willliams 82 genome. Quantitative real-time PCR assays of possible candidate genes revealed that Glyma.03 g05300 was likely involved in PRR resistance.

CONCLUSIONS

These findings from the fine mapping of a novel Rps locus will serve as a basis for the cloning and transfer of resistance genes in soybean and the breeding of P. sojae-resistant soybean cultivars through marker-assisted selection.

Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce

Plantation-grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single- and multi-trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate-to-high heritability and low genotype-by-environment interactions. Weevil resistance was genetically positively correlated with tree height, height-to-diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi-trait models performed similarly as single-trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi-trait GS models. A GS index that corresponded to the breeders' priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high-quality, weevil-resistant Norway spruce reforestation stock with high accuracy achieved from single-trait or multi-trait GS.

Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping

Secondary traits from high-throughput phenotyping could be used to select for complex target traits to accelerate plant breeding and increase genetic gains. This study aimed to evaluate the potential of using spectral reflectance indices (SRI) for indirect selection of winter-wheat lines with high yield potential and to assess the effects of including secondary traits on the prediction accuracy for yield. A total of five SRIs were measured in a diversity panel, and F5 and doubled haploid wheat breeding populations planted between 2015 and 2018 in Lind and Pullman, WA. The winter-wheat panels were genotyped with 11,089 genotyping-by-sequencing derived markers. Spectral traits showed moderate to high phenotypic and genetic correlations, indicating their potential for indirect selection of lines with high yield potential. Inclusion of correlated spectral traits in genomic prediction models resulted in significant (p < 0.001) improvement in prediction accuracy for yield. Relatedness between training and test populations and heritability were among the principal factors affecting accuracy. Our results demonstrate the potential of using spectral indices as proxy measurements for selecting lines with increased yield potential and for improving prediction accuracy to increase genetic gains for complex traits in US Pacific Northwest winter wheat.

Genome-wide association study of the seed transmission rate of soybean mosaic virus and associated traits using two diverse population panels

KEY MESSAGE

Genome-wide association analyses identified candidates for genes involved in restricting virus movement into embryonic tissues, suppressing virus-induced seed coat mottling and preserving yield in soybean plants infected with soybean mosaic virus. Soybean mosaic virus (SMV) causes significant reductions in soybean yield and seed quality. Because seedborne infections can serve as primary sources of inoculum for SMV infections, resistance to SMV seed transmission provides a means to limit the impacts of SMV. In this study, two diverse population panels, Pop1 and Pop2, composed of 409 and 199 soybean plant introductions, respectively, were evaluated for SMV seed transmission rate, seed coat mottling, and seed yield from SMV-infected plants. The phenotypic data and genotypic data from the SoySNP50K dataset were analyzed using GAPIT and rrBLUP. For SMV seed transmission rate, a single locus was identified on chromosome 9 in Pop1. For SMV-induced seed coat mottling, loci were identified on chromosome 9 in Pop1 and on chromosome 3 in Pop2. For seed yield from SMV-infected plants, a single locus was identified on chromosome 3 in Pop2 that was within the map interval of a previously described quantitative trait locus for seed number. The high linkage disequilibrium regions surrounding the markers on chromosomes 3 and 9 contained a predicted nonsense-mediated RNA decay gene, multiple pectin methylesterase inhibitor genes (involved in restricting virus movement), two chalcone synthase genes, and a homolog of the yeast Rtf1 gene (involved in RNA-mediated transcriptional gene silencing). The results of this study provided additional insight into the genetic architecture of these three important traits, suggested candidate genes for downstream functional validation, and suggested that genomic prediction would outperform marker-assisted selection for two of the four trait-marker associations.

Different loci associated with root and foliar resistance to sudden death syndrome (Fusarium virguliforme) in soybean

KEY MESSAGE

Different loci associated with root resistance to F. virguliforme colonization and foliar resistance to phytotoxin damage in soybean. Use of resistant cultivars is the most efficacious approach to manage soybean sudden death syndrome (SDS), caused by Fusarium virguliforme. The objectives of this study were to (1) map the loci associated with root and foliar resistance to F. virguliforme infection and (2) decipher the relationships between root infection, foliar damage, and plot yield. A mapping population consisting of 153 F4-derived recombinant inbred lines from the cross U01-390489 × E07080 was genotyped by SoySNP6 K BeadChip assay. Both foliar damage and F. virguliforme colonization in roots were investigated in the field, and a weak positive correlation was identified between them. Foliar damage had a stronger negative correlation with plot yield than F. virguliforme colonization. Twelve loci associated with foliar damage were identified, and four of them were associated with multiple traits across environments. In contrast, only one locus associated with root resistance to F. virguliforme colonization was identified and mapped on Chromosome 18. It colocalized with the locus associated with foliar damage in the same environment. The locus on Chromosome 6, qSDS6-2, and the locus on Chromosome 18, qSDS18-1, were associated with resistance to SDS phytotoxins and resistance to F. virguliforme colonization of roots, respectively. Both loci affected plot yield. Foliar damage-related traits, especially disease index, are valuable indicators for SDS resistance breeding because of consistency of the identified loci and their stronger correlation with plot yield. The information provided by this study will facilitate marker-assisted selection to improve SDS resistance in soybean.

Genome-Wide Association Mapping of Host-Plant Resistance to Soybean Aphid

Soybean aphid [ Matsumura (Hemiptera: Aphididae)] is the most damaging insect pest of soybean [ (L.) Merr.] in the Upper Midwest of the United States and is primarily controlled by insecticides. Soybean aphid resistance (i.e., genes) has been documented in some soybean accessions but more sources of resistance are needed. Incorporation of the resistance into marketed varieties has also been slow. Genome-wide association mapping can aid in identifying resistant accessions by correlating phenotypic data with single nucleotide polymorphisms (SNPs) across a genome. Aphid population measures from 2366 soybean accessions were collected from published studies screening cultivated soybean () and wild soybean ( Siebold & Zucc.) with aphids exhibiting Biotype 1, 2, or 3 characteristics. Genotypic data were obtained from the SoySNP50K high-density genotyping array previously used to genotype the USDA Soybean Germplasm Collection. Significant associations between SNPs and soybean aphid counts were found on 18 of the 20 soybean chromosomes. Significant SNPs were found on chromosomes 7, 8, 13, and 16 with known genes. SNPs were also significant on chromosomes 1, 2, 4 to 6, 9 to 12, 14, and 17 to 20 where genes have not yet been mapped, suggesting that many genes remain to be discovered. These SNPs can be used to determine accessions that are likely to have novel aphid resistance traits of value for breeding programs.

GWAS of a soybean breeding collection from South East and South Kazakhstan for resistance to fungal diseases

Soybean (Glycine max(L.) Merr) is an essential food, feed, and technical culture. In Kazakhstan the area under soybean is increasing every year, helping to solve the problem of protein deficiency in human nutrition and animal feeding. One of the main problems of soybean production is fungal diseases causing yields losses of up to 30 %. Modern genomic studies can be applied to facilitate efficient breeding research for improvement of soybean fungal disease tolerance. Therefore, the objective of this genome-wide association study (GWAS) was analysis of a soybean collection consisting of 182 accessions in relation to fungal diseases in the conditions of South East and South Kazakh­stan. Field evaluation of the soybean collection suggested thatFusariumspp. andCercospora sojinaaffected plants in the South region (RIBSP), andSeptoria glycines– in the South East region (KRIAPP). The major objective of the study was identification of QTL associated with resistance to fusarium root rot (FUS), frogeye leaf spot (FLS), and brown spot (BS). GWAS using 4 442 SNP (single nucleotide polymorphism) markers of Illumina iSelect array allowed for identification of fifteen marker trait associations (MTA) resistant to the three diseases at two different stages of growth. Two QTL both for FUS (chromosomes 13 and 17) and BS (chromosomes 14 and 17) were genetically mapped, including one presumably novel QTL for BS (chromo­some 17). Also, five presumably novel QTL for FLS were genetically mapped on chromosomes 2, 7, and 15. The results can be used for improvement of the local breeding projects based on marker-assisted selection approach.          

Field evaluation of three sources of genetic resistance to sudden death syndrome of soybean

KEY MESSAGE

Despite numerous challenges, field testing of three sources of genetic resistance to sudden death syndrome of soybean provides information to more effectively improve resistance to this disease in cultivars. Sudden death syndrome (SDS) of soybean [Glycine max (L.) Merrill] is a disease that causes yield loss in soybean growing regions across the USA and worldwide. While several quantitative trait loci (QTL) for SDS resistance have been mapped, studies to further evaluate these QTL are limited. The objective of our research was to map SDS resistance QTL and to test the effect of mapped resistance QTL on foliar symptoms when incorporated into elite soybean backgrounds. We mapped a QTL from Ripley to chromosome 10 (CHR10) and a QTL from PI507531 to chromosomes 1 and 18 (CHR1 and 18). Six populations were then developed to test the following QTL: cqSDS-001, with resistance originating from PI567374, CHR10, CHR1, and CHR18. The populations which segregated for resistant and susceptible QTL alleles were field tested in multiple environments and evaluated for SDS foliar symptoms. While foliar disease development was variable across environments and populations, a significant effect of each QTL on disease was detected within at least one environment. This includes the detection of cqSDS-001 in three genetic backgrounds. The QTL allele from the resistant parents was associated with greater resistance than the susceptible alleles for all QTL and backgrounds with the exception of the allele for CHR18, where the opposite occurred. This study highlights the importance and difficulties of evaluating QTL and the need for multi-year SDS field testing. The information presented in this study can aid breeders in making decisions to improve resistance to SDS.

Integration of sudden death syndrome resistance loci in the soybean genome

KEY MESSAGE

Complexity and inconsistencies in resistance mapping publications of soybean sudden death syndrome (SDS) result in interpretation difficulty. This review integrates SDS mapping literature and proposes a new nomenclature system for reproducible SDS resistance loci. Soybean resistance to sudden death syndrome (SDS) is composed of foliar resistance to phytotoxins and root resistance to pathogen invasion. There are more than 80 quantitative trait loci (QTL) and dozens of single nucleotide polymorphisms (SNPs) associated with soybean resistance to SDS. The validity of these QTL and SNPs is questionable because of the complexity in phenotyping methodologies, the disease synergism between SDS and soybean cyst nematode (SCN), the variability from the interactions between soybean genotypes and environments, and the inconsistencies in the QTL nomenclature. This review organizes SDS mapping results and proposes the Rfv (resistance to Fusarium virguliforme) nomenclature based on supporting criteria described in the text. Among ten reproducible loci receiving our Rfv nomenclature, Rfv18-01 is mostly supported by field studies and it co-localizes to the SCN resistance locus rhg1. The possibility that Rfv18-01 is a pleiotropic resistance locus and the concern about Rfv18-01 being confounded with Rhg1 is discussed. On the other hand, Rfv06-01, Rfv06-02, Rfv09-01, Rfv13-01, and Rfv16-01 were identified both by screening soybean leaves against phytotoxic culture filtrates and by evaluating SDS severity in fields. Future phenotyping using leaf- and root-specific resistance screening methodologies may improve the precision of SDS resistance, and advanced genetic studies may further clarify the interactions among soybean genotypes, F. virguliforme, SCN, and environments. The review provides a summary of the SDS resistance literature and proposes a framework for communicating SDS resistance loci for future research considering molecular interactions and genetic breeding for soybean SDS resistance.

Mapping and Predicting Non-Linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation

Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size using data from one treatment (uncrowded plants in one growing season). Models successfully predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL-by-treatment interactions, predicted phenotypes across sites differing in plant density.

Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.)

Predictabilities for wheat hybrids less related to the estimation set were improved by shifting from single- to multiple-trait genomic prediction of Fusarium head blight severity. Breeding for improved Fusarium head blight resistance (FHBr) of wheat is a very laborious and expensive task. FHBr complexity is mainly due to its highly polygenic nature and because FHB severity (FHBs) is greatly influenced by the environment. Associated traits plant height and heading date may provide additional information related to FHBr, but this is ignored in single-trait genomic prediction (STGP). The aim of our study was to explore the benefits in predictabilities of multiple-trait genomic prediction (MTGP) over STGP of target trait FHBs in a population of 1604 wheat hybrids using information on 17,372 single nucleotide polymorphism markers along with indicator traits plant height and heading date. The additive inheritance of FHBs allowed accurate hybrid performance predictions using information on general combining abilities or average performance of both parents without the need of markers. Information on molecular markers and indicator trait(s) improved FHBs predictabilities for hybrids less related to the estimation set. Indicator traits must be observed on the predicted individuals to benefit from MTGP. Magnitudes of genetic and phenotypic correlations along with improvements in predictabilities made plant height a better indicator trait for FHBs than heading date. Thus, MTGP having only plant height as indicator trait already maximized FHBs predictabilities. Provided a good indicator trait was available, MTGP could reduce the impacts of genotype environment [Formula: see text] interaction on STGP for hybrids less related to the estimation set.

Leveraging genomic prediction to scan germplasm collection for crop improvement

The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.

A predictive assessment of genetic correlations between traits in chickens using markers

Background

Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.

Methods

A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λG + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.

Results

Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.

Conclusions

Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

Characterization of Disease Resistance Loci in the USDA Soybean Germplasm Collection Using Genome-Wide Association Studies

Genetic resistance is a key strategy for disease management in soybean. Over the last 50 years, soybean germplasm has been phenotyped for resistance to many pathogens, resulting in the development of disease-resistant elite breeding lines and commercial cultivars. While biparental linkage mapping has been used to identify disease resistance loci, genome-wide association studies (GWAS) using high-density and high-quality markers such as single nucleotide polymorphisms (SNPs) has become a powerful tool to associate molecular markers and phenotypes. The objective of our study was to provide a comprehensive understanding of disease resistance in the United States Department of Agriculture Agricultural Research Service Soybean Germplasm Collection by using phenotypic data in the public Germplasm Resources Information Network and public SNP data (SoySNP50K). We identified SNPs significantly associated with disease ratings from one bacterial disease, five fungal diseases, two diseases caused by nematodes, and three viral diseases. We show that leucine-rich repeat (LRR) receptor-like kinases and nucleotide-binding site-LRR candidate resistance genes were enriched within the linkage disequilibrium regions of the significant SNPs. We review and present a global view of soybean resistance loci against multiple diseases and discuss the power and the challenges of using GWAS to discover disease resistance in soybean.

Uncovering novel loci for mesocotyl elongation and shoot length in indica rice through genome-wide association mapping

Totally, 23 loci were detected, and 383 candidate genes were identified, and four of these candidate genes, Os01g0392100, Os04g0630000, Os01g0904700 and Os07g0615000, were regarded as promising targets. Direct-seeding cultivation is becoming popular in rice (Oryza sativa L.)-planting countries because it is labor- and time-efficient. However, low seedling establishment and slow seedling emergence have restricted the application and popularity of the technique. Mesocotyl elongation and shoot length are two important traits that can enhance rice seedling emergence. A single nucleotide polymorphism (SNP) is a genome sequence variation caused by a single base within a population, and SNPs evenly distributed throughout the genomes of plant species. In this study, a genome-wide association study (GWAS), based on 4136 SNPs, was performed using a compressed mixed linear model that accounted for population structure and relative kinship to detect novel loci for the two traits. Totally, 23 loci were identified, including five loci located known QTLs region. For the mesocotyl elongation, 17 major loci were identified, explaining ~19.31 % of the phenotypic variation. For the shoot length, six major loci were detected, explaining ~ 39.79 % of the phenotypic variation. In total, 383 candidate genes were included in a 200-kb genomic region (± 100 kb of each locus). Additionally, 32 SNPs were identified in 30 candidate genes. Relative expression level analyses indicated that four candidate genes containing SNP variations, Os01g0392100, Os04g0630000, Os01g0904700 and Os07g0615000, represented promising targets. Finally, eight elite accessions with long mesocotyl and shoot lengths were chosen as breeding donors for further rice direct-seeding variety modifications.

Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes

KEY MESSAGE

Exploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding.

ABSTRACT

Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.