Unraveling the Genetic Basis of Key Agronomic Traits of Wrinkled Vining Pea (Pisum sativum L.) for Sustainable Production

Exploring novel SNPs and candidate genes associated with seed allometry in Pisum sativum L

Seed size is an important agronomic trait that indicates seed quality. In legumes, pods with equal and larger seeds remain the first preference of farmers and consumers. Genetic understanding related to seed size including seed allometric traits has been limited in the case of peas. To fill this void the findings presented here used the genome-wide association studies (GWAS) to identify novel candidate gene(s) putatively linked with seed size in Pisum sativum L. The study was conducted on 240 Pea Single Plant Plus Collection (PSPPC) panels of pea germplasm. Allometric traits measured included seed_length, seed_width, seed_thickness, seed_volume, seed_biomass, and seed_biomass by volume (SB_V). GWAS was performed using the Genome Association and Prediction Integrated Tool (GAPIT) on R-studio. The Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model provided significant single nucleotide polymorphisms (SNPs) linked with all the seed allometric traits. When analyzed the genomic regions of these SNPs provided a list of candidate genes that may be related to seed size. The present study thus provides a list of significant SNPs and relevant genes viz. Psat2g072000 for seed_length, Psat4g104320 for seed_width, Psat6g125800 and Psat6g125840 for seed_thickness, Psat6g228320 for seed_volume, Psat2g143920 for seed_biomass, and Psat2g120400 for SB_V which may prove useful in the improvement of pea seed size using breeding programs or CRISPR intervention. Understanding the genetic basis of seed size could lead to crop development with desirable seed characteristics, such as equal and larger-sized seeds with maximum yield and higher nutritional content.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-024-01499-6.

Developing genomic tools to assist turnip rape [Brassica rapa (L.) subsp.oleifera (DC.) Metzg.] breeding

Introduction

Turnip rape is recognized as an oilseed crop contributing to environmentally sustainable agriculture via integration into crop rotation systems. Despite its various advantages, the crop's cultivation has declined globally due to a relatively low productivity, giving way to other crops. The use of genomic tools could enhance the breeding process and accelerate genetic gains. Therefore, the present research investigated 170 turnip rape accessions representing its global gene pool to identify SNP markers associated nine phenological and agro-morphological traits and estimate the genomic breeding values (GEBVs) of the germplasm through GWAS and genomic prediction analyses, respectively.

Methods

Field trials were conducted at two sites in northern and southern Sweden to obtain the phenotypic data while genotyping was conducted via the genotyping-by-sequencing (GBS) method. The traits studied include days to flowering (DTF) and maturity (DTM), plant height (PH), seed yield (YLD), thousand seed weight (TSW), silique length (SL), number of siliques (NS), number of seeds per silique (SS), and pod shattering resistance (PSHR).

Results and conclusion

Analysis of variance revealed substantial variation among accessions, with significant genotype-by-environment interaction for most traits. A total of 25, 17, 16, 14, 7, 5, 3, and 3 MTAs were identified for TSW, DTF, PH, PSHR, SL, YLD, SS and DTM, respectively. An 80%-20% training-test set genomic prediction analysis was conducted using the ridge regression - BLUP (RR-BLUP) model. The accuracy of genomic prediction for most traits was high, indicating that these tools may assist turnip rape breeders in accelerating genetic gains. The study highlights the potential of genomic tools to significantly advance breeding programs for turnip rape by identifying pivotal SNP markers and effectively estimating genomic breeding values. Future breeding perspectives should focus on leveraging these genomic insights to enhance agronomic traits and productivity, thereby reinstating turnip rape as a competitive and sustainable crop in Sweden and broader global agriculture.

Genetic diversity and population structure analysis of a diverse panel of pea (Pisum sativum)

Breeding resilient cultivars with increased tolerance to environmental stress and enhanced resistance to pests and diseases demands pre-breeding efforts that include understanding genetic diversity. This study aimed to evaluate the genetic diversity and population structure of 265 pea accessions. The diversity arrays technology (DArT) genotyping method was employed to identify single-nucleotide polymorphisms (SNPs) and silico markers. After stringent filtering, 6966 SNP and 8,454 silico markers were selected for diversity analysis. Genetic diversity was estimated by grouping accessions based on plant material type, geographic origin, growth habit, and seed color. Generally, diversity estimations obtained using SNPs were similar to those estimated using silico markers. The polymorphism information content (PIC) of the SNP markers ranged from 0.0 to 0.5, with a quarter of them displaying PIC values exceeding 0.4, making them highly informative. Analysis based on plant material type revealed narrow observed heterozygosity (Ho = 0.02-0.03) and expected heterozygosity (He = 0.26-0.31), with landrace accessions exhibiting the highest diversity. Geographic origin-based diversity analysis revealed Ho = 0.02-0.03 and He = 0.22 to 0.30, with European accessions showing the greatest diversity. Moreover, private alleles unique to landrace (4) and European (22) accessions were also identified, which merit further investigation for their potential association with desirable traits. The analysis of molecular variance revealed a highly significant genetic differentiation among accession groups classified by seed color, growth habit, plant material types, and geographic origin (p < 0.01). Principal coordinate analysis and neighbor-joining cluster analysis revealed weak clustering of accessions at different grouping levels. This study underscores the significance of genetic diversity in pea collections, offering valuable insights for targeted breeding and conservation efforts. By leveraging genomic data and exploring untapped genetic resources, pea breeding programs can be fortified to ensure sustainable plant protein production and address future challenges in agriculture.

Novel SNP markers for flowering and seed quality traits in faba bean (Vicia faba L.): characterization and GWAS of a diversity panel

Faba bean (Vicia faba L.) is a legume crop grown in diverse climates worldwide. It has a high potential for increased cultivation to meet the need for more plant-based proteins in human diets, a prerequisite for a more sustainable food production system. Characterization of diversity panels of crops can identify variation in and genetic markers for target traits of interest for plant breeding. In this work, we collected a diversity panel of 220 accessions of faba bean from around the world consisting of gene bank material and commercially available cultivars. The aims of this study were to quantify the phenotypic diversity in target traits to analyze the impact of breeding on these traits, and to identify genetic markers associated with traits through a genome-wide association study (GWAS). Characterization under field conditions at Nordic latitude across two years revealed a large genotypic variation and high broad-sense heritability for eleven agronomic and seed quality traits. Pairwise correlations showed that seed yield was positively correlated to plant height, number of seeds per plant, and days to maturity. Further, susceptibility to bean weevil damage was significantly higher for early flowering accessions and accessions with larger seeds. In this study, no yield penalty was found for higher seed protein content, but protein content was negatively correlated to starch content. Our results showed that while breeding advances in faba bean germplasm have resulted in increased yields and number of seeds per plant, they have also led to a selection pressure towards delayed onset of flowering and maturity. DArTseq genotyping identified 6,606 single nucleotide polymorphisms (SNPs) by alignment to the faba bean reference genome. These SNPs were used in a GWAS, revealing 51 novel SNP markers significantly associated with ten of the assessed traits. Three markers for days to flowering were found in predicted genes encoding proteins for which homologs in other plant species regulate flowering. Altogether, this work enriches the growing pool of phenotypic and genotypic data on faba bean as a valuable resource for developing efficient breeding strategies to expand crop cultivation.

Genomic prediction and allele mining of agronomic and morphological traits in pea (Pisum sativum) germplasm collections

Well-performing genomic prediction (GP) models for polygenic traits and molecular marker sets for oligogenic traits could be useful for identifying promising genetic resources in germplasm collections, setting core collections, and establishing molecular variety distinction. This study aimed at (i) defining GP models and key marker sets for predicting 15 agronomic or morphological traits in germplasm collections, (ii) verifying the GP model usefulness also for selection in breeding programs, (iii) investigating the consistency between molecular and phenotypic diversity patterns, and (iv) identifying genomic regions associated with to the target traits. The study was based on phenotyping data and over 41,000 genotyping-by-sequencing-generated SNP markers of 220 landraces or old cultivars belonging to a world germplasm collection and 11 modern cultivars. Non-metric multi-dimensional scaling (NMDS) and an analysis of population genetic structure indicated a high level of genetic differentiation of material from Western Asia, a major West-East diversity gradient, and quite limited genetic diversity of the improved germplasm. Mantel's test revealed a low correlation (r = 0.12) between phenotypic and molecular diversity, which increased (r = 0.45) when considering only the molecular diversity relative to significant SNPs from genome-wide association analyses. These analyses identified, inter alia, several areas of chromosome 6 involved in a largely pleiotropic control of vegetative or reproductive organ pigmentation. We found various significant SNPs for grain and straw yield under severe drought and onset of flowering, and one SNP on chromosome 5 for grain protein content. GP models displayed moderately high predictive ability (0.43 to 0.61) for protein content, grain and straw yield, and onset of flowering, and high predictive ability (0.76) for individual seed weight, based on intra-population, intra-environment cross-validations. The inter-population, inter-environment assessment of the models trained on the germplasm collection for breeding material of three recombinant inbred line (RIL) populations, which was challenged by much narrower diversity of the material, over eight-fold less available markers and quite different test environments, led to an overall loss of predictive ability of about 40% for seed weight, 50% for protein content and straw yield, and 60% for onset of flowering, and no prediction for grain yield. Within-RIL population predictive ability differed among populations.

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r² < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.