Association study of crude seed protein and fat concentration in a USDA pea diversity panel.

Pea (Pisum sativum L.) is a key rotational crop and is increasingly important in the food processing sector for its protein. This study focused on identifying diverse high seed protein concentration (SPC) lines in pea plant genetic resources. Objectives included identifying high-protein pea lines, exploring genetic architecture across environments, pinpointing genes and metabolic pathways associated with high protein, and documenting information for single nucleotide polymorphism (SNP)-based marker-assisted selection. From 2019 to 2021, a 487-accession pea diversity panel, More protein, More pea, More profit, was evaluated in a randomized complete block design. DNA was extracted for genomic analysis via genotype-by-sequencing. Phenotypic analysis included protein and fat measurements in seeds and flower color. Genome-wide association study (GWAS) used multiple models, and the Pathways Association Study Tool was used for metabolic pathway analysis. Significant associations were found between SNPs and pea seed protein and fat concentration. Gene Psat7g216440 on chromosome 7, which targets proteins to cellular destinations, including seed storage proteins, was identified as associated with SPC. Genes Psat4g009200, Psat1g199800, Psat1g199960, and Psat1g033960, all involved in lipid metabolism, were associated with fat concentration. GWAS also identified genes annotated for storage proteins associated with fat concentration, indicating a complex relationship between fat and protein. Metabolic pathway analysis identified 20 pathways related to fat and seven to protein concentration, involving fatty acids, amino acid and protein metabolism, and the tricarboxylic acid cycle. These findings will assist in breeding of high-protein, diverse pea cultivars, and SNPs that can be converted to breeder-friendly molecular marker assays are identified for genes associated with high protein.

Effective population size in field pea.

BACKGROUND: Effective population size (Ne) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller Ne can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of Ne has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols. Here, we present the first Ne estimates based on linkage disequilibrium (LD) in the pea genome. RESULTS: We calculated and compared Ne using SNP markers from North Dakota State University (NDSU) modern breeding lines and United States Department of Agriculture (USDA) diversity panel. The extent of LD was highly variable not only between populations but also among different regions and chromosomes of the genome. Overall, NDSU had a higher and longer-range LD than the USDA that could extend up to 500 Kb, with a genome-wide average r2 of 0.57 (vs 0.34), likely due to its lower recombination rates and the selection background. The estimated Ne for the USDA was nearly three-fold higher (Ne = 174) than NDSU (Ne = 64), which can be confounded by a high degree of population structure due to the selfing nature of pea. CONCLUSIONS: Our results provided insights into the genetic diversity of the germplasm studied, which can guide plant breeders to actively monitor Ne in successive cycles of breeding to sustain viability of the breeding efforts in the long term.

Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea.

Chickpea (Cicer arietinum L.)-an important legume crop cultivated in arid and semiarid regions-has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicer super-pangenome based on the de novo genome assemblies of eight annual Cicer wild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicer species into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea.

Corrigendum: How could the use of crop wild relatives in breeding increase the adaptation of crops to marginal environments?

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Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.

We extended the use of sparse phenotyping into the multi-trait genomic selection (MT-GS) framework by split testing of entries. The sparse-phenotyping-aided MT-GS can increase predictive ability by >12% across traits. Heritability and genetic correlation are possible metrics to optimize and further improve prediction performance of MT-GS. The sparse-testing-aided MT-GS can be further extended to multi-environment, multi-trait GS framework.

How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments?

Alongside the use of fertilizer and chemical control of weeds, pests, and diseases modern breeding has been very successful in generating cultivars that have increased agricultural production several fold in favorable environments. These typically homogeneous cultivars (either homozygous inbreds or hybrids derived from inbred parents) are bred under optimal field conditions and perform well when there is sufficient water and nutrients. However, such optimal conditions are rare globally; indeed, a large proportion of arable land could be considered marginal for agricultural production. Marginal agricultural land typically has poor fertility and/or shallow soil depth, is subject to soil erosion, and often occurs in semi-arid or saline environments. Moreover, these marginal environments are expected to expand with ongoing climate change and progressive degradation of soil and water resources globally. Crop wild relatives (CWRs), most often used in breeding as sources of biotic resistance, often also possess traits adapting them to marginal environments. Wild progenitors have been selected over the course of their evolutionary history to maintain their fitness under a diverse range of stresses. Conversely, modern breeding for broad adaptation has reduced genetic diversity and increased genetic vulnerability to biotic and abiotic challenges. There is potential to exploit genetic heterogeneity, as opposed to genetic uniformity, in breeding for the utilization of marginal lands. This review discusses the adaptive traits that could improve the performance of cultivars in marginal environments and breeding strategies to deploy them.

Phenotypic and Genetic Characterization of the Lentil Single Plant-Derived Core Collection for Resistance to Root Rot Caused by Fusarium avenaceum.

Lentil (Lens culinaris) is a pulse crop grown for its amino acid profile, moderate drought tolerance, and ability to fix nitrogen. As the global demand for lentils expands and new production regions emerge so too have the complement of diseases that reduce yield, including the root rot complex. Although the predominant causal pathogen varies based on growing region, Fusarium avenaceum is often found to be an important contributor to disease. This study screened part of the lentil single plant-derived core collection for resistance to F. avenaceum in a greenhouse. Plants were phenotyped for disease severity using three scoring scales and the differences in biomass traits due to pathogen presence were measured. Lentil accessions varied in disease severity and differences in biomass traits were found to be correlated with each visual severity estimate (r = -0.37 to -0.63, P < 0.001), however, heritability estimates were low to moderate among traits (H2 = 0.12 to 0.43). Results of a genome-wide association study (GWAS) using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing revealed 11 quantitative trait loci (QTL) across four chromosomes. Two pairs of QTL colocated for two traits and were found near putative orthologs that have been previously associated with plant disease resistance. The identification of lentil accessions that did not exhibit a difference in biomass traits may serve as parental material in breeding or in the development of biparental mapping populations to further validate and dissect the genetic control of resistance to root rot caused by F. avenaceum.

Integrating Genomic and Phenomic Approaches to Support Plant Genetic Resources Conservation and Use.

Plant genebanks provide genetic resources for breeding and research programs worldwide. These programs benefit from having access to high-quality, standardized phenotypic and genotypic data. Technological advances have made it possible to collect phenomic and genomic data for genebank collections, which, with the appropriate analytical tools, can directly inform breeding programs. We discuss the importance of considering genebank accession homogeneity and heterogeneity in data collection and documentation. Citing specific examples, we describe how well-documented genomic and phenomic data have met or could meet the needs of plant genetic resource managers and users. We explore future opportunities that may emerge from improved documentation and data integration among plant genetic resource information systems.

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.

Limited divergent adaptation despite a substantial environmental cline in wild pea.

Isolation by environment (IBE) is a widespread phenomenon in nature. It is commonly expected that the degree of difference among environments is proportional to the level of divergence between populations in their respective environments. It is therefore assumed that a species' genetic diversity displays a pattern of IBE in the presence of a strong environmental cline if gene flow does not mitigate isolation. We tested this common assumption by analysing the genetic diversity and demographic history of Pisum fulvum, which inhabits contrasting habitats in the southern Levant and is expected to display only minor migration rates between populations, making it an ideal test case. Ecogeographical and subpopulation structure were analysed and compared. The correlation of genetic with environmental distances was calculated to test the effect of isolation by distance and IBE and detect the main drivers of these effects. Historical effective population size was estimated using stairway plot. Limited overlap of ecogeographical and genetic clustering was observed, and correlation between genetic and environmental distances was statistically significant but small. We detected a sharp decline of effective population size during the last glacial period. The low degree of IBE may be the result of genetic drift due to a past bottleneck. Our findings contradict the expectation that strong environmental clines cause IBE in the absence of extensive gene flow.

Environmental and genetic determinants of amphicarpy in Pisum fulvum, a wild relative of domesticated pea.

Pisum fulvum is an annual legume native to Syria, Lebanon, Israel and Jordan. In certain locations, P. fulvum individuals were documented to display a reproductive dimorphism - amphicarpy, with both above and below ground flowers and pods. Herein we aimed to study the possible role of soil texture on amphicarpy in P. fulvum, to investigate the possible bio-climatic associations of P. fulvum amphicarpy and to identify genetic markers associated with this phenotype. A set of 127 germplasm accessions sampled across the Israeli distribution range of the species was phenotyped in two common garden nurseries. Land use and bioclimatic data were used to delineate the eco-geographic clustering of accession's sampling sites. Single nucleotide polymorphism (SNP) markers were employed in genome-wide association study to identify associated loci. Amphicarpy was subject to strong experimental site x genotype interaction with higher phenotypic expression in fine textured soil relative to sandy loam. Amphicarpy was more prevalent among accessions sampled in eastern Judea and Samaria and was weakly associated with early phenology and relatively modest above ground biomass production. Twelve SNP markers were significantly associated with amphicarpy, each explaining between 8 and 12 % of the phenotypic variation. In P. fulvum amphicarpy seems to be a polygenetic trait controlled by an array of genes that is likely to be affected by environmental stimuli. The probable selective advantage of the association between amphicarpy and early flowering is in line with its relative prevalence in drought prone territories subject to heavy grazing.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study.

Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.

A reference genome for pea provides insight into legume genome evolution.

We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel's original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement.

Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil.

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Confirmation of Fusarium root rot resistance QTL Fsp-Ps 2.1 of pea under controlled conditions.

BACKGROUND: Dry pea production has increased substantially in North America over the last few decades. With this expansion, significant yield losses have been attributed to an escalation in Fusarium root rots in pea fields. Among the most significant rot rotting pathogenic fungal species, Fusarium solani fsp. pisi (Fsp) is one of the main causal agents of root rot of pea. High levels of partial resistance to Fsp has been identified in plant genetic resources. Genetic resistance offers one of the best solutions to control this root rotting fungus. A recombinant inbred population segregating for high levels of partial resistance, previously single nucleotide polymorphism (SNP) genotyped using genotyping-by-sequencing, was phenotyped for disease reaction in replicated and repeated greenhouse trials. Composite interval mapping was deployed to identify resistance-associated quantitative trait loci (QTL). RESULTS: Three QTL were identified using three disease reaction criteria: root disease severity, ratios of diseased vs. healthy shoot heights and dry plant weights under controlled conditions using pure cultures of Fusarium solani fsp. pisi. One QTL Fsp-Ps 2.1 explains 44.4-53.4% of the variance with a narrow confidence interval of 1.2 cM. The second and third QTL Fsp-Ps3.2 and Fsp-Ps3.3 are closely linked and explain only 3.6-4.6% of the variance. All of the alleles are contributed by the resistant parent PI 180693. CONCLUSION: With the confirmation of Fsp-Ps 2.1 now in two RIL populations, SNPs associated with this region make a good target for marker-assisted selection in pea breeding programs to obtain high levels of partial resistance to Fusarium root rot caused by Fusarium solani fsp. pisi.

Molecular Evidence for Two Domestication Events in the Pea Crop.

Pea, one of the founder crops from the Near East, has two wild species: Pisum sativum subsp. elatius, with a wide distribution centered in the Mediterranean, and P. fulvum, which is restricted to Syria, Lebanon, Israel, Palestine and Jordan. Using genome wide analysis of 11,343 polymorphic single nucleotide polymorphisms (SNPs) on a set of wild P. elatius (134) and P. fulvum (20) and 74 domesticated accessions (64 P. sativum landraces and 10 P. abyssinicum), we demonstrated that domesticated P. sativum and the Ethiopian pea (P. abyssinicum) were derived from different P. elatius genepools. Therefore, pea has at least two domestication events. The analysis does not support a hybrid origin of P. abyssinicum, which was likely introduced into Ethiopia and Yemen followed by eco-geographic adaptation. Both P. sativum and P. abyssinicum share traits that are typical of domestication, such as non-dormant seeds. Non-dormant seeds were also found in several wild P. elatius accessions which could be the result of crop to wild introgression or natural variation that may have been present during pea domestication. A sub-group of P. elatius overlaps with P. sativum landraces. This may be a consequence of bidirectional gene-flow or may suggest that this group of P. elatius is the closest extant wild relative of P. sativum.

Author Correction: Genomic diversity and macroecology of the crop wild relatives of domesticated pea.

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Genomic diversity and macroecology of the crop wild relatives of domesticated pea.

There is growing interest in the conservation and utilization of crop wild relatives (CWR) in international food security policy and research. Legumes play an important role in human health, sustainable food production, global food security, and the resilience of current agricultural systems. Pea belongs to the ancient set of cultivated plants of the Near East domestication center and remains an important crop today. Based on genome-wide analysis, P. fulvum was identified as a well-supported species, while the diversity of wild P. sativum subsp. elatius was structured into 5 partly geographically positioned clusters. We explored the spatial and environmental patterns of two progenitor species of domesticated pea in the Mediterranean Basin and in the Fertile Crescent in relation to the past and current climate. This study revealed that isolation by distance does not explain the genetic structure of P. sativum subsp. elatius in its westward expansion from its center of origin. The genetic diversity of wild pea may be driven by Miocene-Pliocene events, while the phylogenetic diversity centers may reflect Pleisto-Holocene climatic changes. These findings help set research and discussion priorities and provide geographical and ecological information for germplasm-collecting missions, as well as for the preservation of extant diversity in ex-situ collections.