Germplasm Panel Research Articles

Identification of resistance sources and genomic regions regulating Septoria tritici blotch resistance in South Asian bread wheat germplasm.

The Septoria tritici blotch (STB) [Zymoseptoria tritici (Desm.)] of wheat (Triticum aestivum L.) is characterized by its polycyclic and hemibiotrophic nature. It is one of the most dangerous diseases affecting wheat production worldwide. Durable resistance is largely decided by the combined effect of several quantitative trait loci (QTLs) having a minor effect. Currently, STB is not important in South Asia. However, STB expanding and wider adaptability, changing climatic conditions, and agronomic practices can create a situation of concern. Therefore, dissection of the genetic architecture of adult-plant resistance with genome-wide association mapping and selection of resistant sources for adult plant STB resistance were carried out on a panel of South Asian germplasm. We discovered the 91 quantitative trait nucleotides (QTNs) associated with STB resistance; 23 QTNs were repetitive across the different years and models. Many of these QTNs could differentiate the mapping panel into resistant versus susceptible groups and were linked to candidate genes related to disease resistance functions within linkage disequilibrium blocks. The repetitive QTNs, namely, Q.CIM.stb.2DL.2, Q.CIM.stb_dh.2DL.3, Q.CIM.stb.2AL.5, and Q.CIM.stb.7BL.1, may be novel due to the absence of co-localization of previously reported QTLs, meta-quantitative trait loci, and STB genes. There was a perfect negative correlation between the stacking of favorable alleles and STB susceptibility, and STB resistance response was improved by ∼50% with the stacking of ≥60% favorable alleles. The genotypes, namely, CIM20, CIM56, CIM57, CIM18, CIM44, WK2395, and K1317, could be used as resistant sources in wheat breeding programs. Therefore, this study could aid in designing the breeding programs for STB resistance before the onset of the alarming situation of STB in South Asia.

An island of receptor-like genes at the Rrs13 locus on barley chromosome 6HS co-locate with three novel sources of scald resistance

Three Hordeum spontaneum-derived resistances (referred to as 145L2, 41T1 and 40Y5) have demonstrated long-term effectiveness against barley scald, caused by Rhynchosporium commune, in western Canada. Genetic mapping of these resistances in three populations, and the use of five barley genome assemblies, revealed they co-located to a narrowly defined 0.58–1.2 Mbp region of chromosome 6HS containing the Rrs13 scald resistance gene. Differential disease reactions among the three resistances and a Rrs13 carrier (AB6) to a panel of 24 scald isolates indicated that the four resistances were unique from one another. A marker created to target the 6HS scald locus was screened across a panel of barley germplasm that included H. vulgare, H. spontaneum and H. bulbosum lines. The marker showed specificity to H. vulgare lines known to carry the 6HS scald resistances and to two H. spontaneum lines that trace their origins to Jordan. Within the 0.58–1.2 Mbp region were 2–7 tandemly repeated leucine-rich repeat receptor-like proteins (LRR-RLP) and one lectin receptor-like kinase (Lec-RLK) genes with abundant sequence variation between them. The well-defined role that RLP and RLK genes play in plant defense responses make them logical candidate resistance genes, with one possible hypothesis being that each unique scald resistance may be encoded by a different RLP that interacts with a common RLK. It is suggested the three scald resistances be temporarily named Rrs13145L2, Rrs1341T1 and Rrs1340Y5 to recognize their co-location to the Rrs13 locus until it is determined whether these resistances represent unique genes or alleles of the same gene.

Genetic diversity and genome-wide association study of partial resistance to Sclerotinia stem rot in a Canadian soybean germplasm panel.

Developing genetically resistant soybean cultivars is key in controlling the destructive Sclerotinia Stem Rot (SSR) disease. Here, a GWAS study in Canadian soybeans identified potential marker-trait associations and candidate genes, paving the way for more efficient breeding methods for SSR. Sclerotinia stem rot (SSR), caused by the fungal pathogenSclerotinia sclerotiorum, is one of the most important diseases leading to significant soybean yield losses in Canada and worldwide. Developing soybean cultivars that are genetically resistant to the disease is the most inexpensive and reliable method to control the disease. However, breeding for resistance is hampered by the highly complex nature of genetic resistance to SSR in soybean. This study sought to understand the genetic basis underlying SSR resistance particularly in soybean grown in Canada. Consequently, a panel of 193 genotypes was assembled based on maturity group and genetic diversity as representative of Canadian soybean cultivars. Plants were inoculated and screened for SSR resistance in controlled environments, where variation for SSR phenotypic response was observed. The panel was also genotyped via genotyping-by-sequencing and the resulting genotypic data were imputed using BEAGLE v5 leading to a catalogue of 417K SNPs. Through genome-wide association analyses (GWAS) using FarmCPU method with threshold of FDR-adjustedp-values < 0.1, we identified significant SNPs on chromosomes 2 and 9 with allele effects of 16.1 and 14.3, respectively. Further analysis identified three potential candidate genes linked to SSR disease resistance within a 100Kb window surrounding each of the peak SNPs. Our results will be important in developing molecular markers that can speed up the breeding for SSR resistance in Canadian grown soybean.

Genetic dissection of minerals and phytate content in pearl millet germplasm panel using genome‐wide association study

AbstractBillions of people around the world suffer from malnutrition, leading to severe adverse health effects. Pearl millet (Pennisetum glaucum) is a multifaceted versatile crop with excellent nutritional profile which can help to combat nutritional disorders and climate change. In this study, we evaluated the global pearl millet germplasm panel known as PMiGAP for natural variation and genetic maker trait associations for important minerals, that is, iron, zinc, calcium, magnesium, potassium and sodium along with phytate. The genotypes IP‐15947, IP‐5121, IP‐4020, IP‐12768, IP‐5695, IP‐8786 and IP‐11310 were found to be superior for majority of minerals examined but had lower phytate‐to‐zinc ratio. Phytate/mineral molar ratios are typically used to predict the bioavailability of iron and calcium contents in grains, and surprisingly none of the PMiGAP genotypes showed such ratios below threshold indicating PMiGAP entries studied in this study seriously suffer from bioavailability issues of these minerals. On the other hand, 73 genotypes had lower zinc/phytate ratio than the threshold in the germplasm panel. Iron and zinc content had significant positive association among them but phytate content in general was not significantly correlated with minerals except for magnesium and potassium. A genome‐wide association study using 456 K SNPs identified 74 significant marker–trait associations and 59 candidate genes around 50 Kb distance near the significant SNPs. Ten significant SNPs were found within the candidate genes. The associated markers and the candidate genes provide new insights into the genetic architecture of the mineral traits studied and will facilitate marker‐assisted selection to accelerate breeding of such minerals in future varieties to combat rising malnutrition problem via diet.

Genetic diversity in Pima (Gossypium barbadense L.) and advanced interspecific hybrids (Gossypium hirsutum x Gossypium barbadense) of cotton germplasm in Ethiopia

This study was conducted to assess the genetic diversity in Pima (Gossypium barbadense L.) and advanced interspecific hybrids (G. hirsutum L. x G. barbadense L.) of cotton germplasm in Ethiopia. A total of 26 germplasm were genotyped using 26 polymorphic simple sequence repeat (SSR) markers. A total of 165 polymorphic loci, with a range of 3 to 13 alleles and a mean of 6.35 per marker were detected. About 11% of total alleles were unique alleles in 11 germplasm. The gene diversity varied from 0.39 to 0. 89, while the heterozygosity was in the range of 0 to 1.00. Furthermore, the polymorphic information content of the markers varied from 0.37 (BNL1417) to 0.88 (BNL1672) with an average of 0.68. Among the complete panel of cotton germplasm used in this study, the pairwise genetic distance ranged from 0.08 to 0.94, with an overall mean of 0.61. The UPGMA cluster analysis grouped the Pima cotton germplasm into two cluster groups and the advanced interspecific hybrid cotton germplasm makes one cluster. Principal coordinate analysis indicates that the first three most informative principal coordinates explained 68.4% of the genetic variation. The result evidenced very low to high genetic dissimilarity and overall, a considerable genetic diversity among and within germplasm which could be used to further broaden the genetic base and to enlarge the number of available cotton germplasm.

Genetic variation in Zea mays influences microbial nitrification and denitrification in conventional agroecosystems

Abstract Background and Aims Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. Methods To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds (Z. mays ssp. mays), ex-PVP hybrids (Z. mays ssp. mays), and teosinte (Z. mays ssp. mexicana and Z. mays ssp. parviglumis). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Results Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Conclusion Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Allele mining for Granule Bound Starch Synthase1 (GBSS1) gene governing amylose content in aromatic rice (Oryza sativa L.) germplasm

The current research investigated sequence polymorphism and allelic variation in the GBSS1 gene responsible for determining the apparent amylose content (AAC) in an aromatic rice germplasm panel. Analysis of 271 germplasm accessions from India revealed a wide range of variation in AAC, from 4.81% (Manipur Black Rice) to 35.88% (IGSR -3-1-5). Re-sequencing and sequence analysis of a representative subset of 28 accessions unveiled a nucleotide diversity (π) of 0.00374, identifying 72 sequence variants. Two novel non-synonymous SNPs were identified: a G/A transition in exon 2, resulting in the substitution of glutamic acid at the 91st position with lysine, and a C/T transition in exon 11, substituting alanine at the 468thposition with valine. Additionally, two previously reported SNPs were validated: A/C in exon 6 (Tyr224Ser) and C/T in exon 10 (Pro415Ser). Furthermore, a 23 bp exon 2 duplication was found in Manipur Black Rice (4.81%), Bhogali Bora (6.20%), and NJ-72 (7.36%), indicating the prevalence of this allele in the very low AAC category (3–9%) in addition to the glutinous or waxy rice category with 0–2% AAC. These findings are significant for manipulating starch content in aromatic rice germplasm.

Genome‐wide association study of adult plant resistance to spot blotch in an elite Canadian two‐row barley germplasm collection

AbstractSpot blotch, caused by Bipolaris sorokiniana, is a growing concern for barley (Hordeum vulgare) production in most humid, temperate growing regions of the world. Under epidemic conditions, this disease can reduce yields significantly. Reduction of kernel plumpness is associated with lower malt extract and consequently grain quality. Enhanced resistance to this disease is a major goal of western Canadian two‐row barley breeding programmes; however, two‐row barley is considered to be more susceptible than six‐row barley. Plant–pathogen interactions observed for Canadian germplasm are polygenic, where quantitative breeding methods could be beneficial for the development of resistant germplasm. A germplasm panel consisting of 200 two‐row, spring barley genotypes with differential reaction to spot blotch was evaluated at Brandon, Manitoba (MB), and Melfort, Saskatchewan (SK), over eight site years. Genome‐wide association study was conducted using a 50k single‐nucleotide polymorphism (SNP) Illumina iSelect genotyping array. The most‐resistant germplasm was found to carry Midwest Six‐rowed Durable Resistant Haplotype (MSDRH) alleles at the loci located on chromosomes 1H (Rcs‐qtl‐1H‐11_10764), 3H (Rcs‐qtl‐3H‐11_10565) and 7H (Rcs‐qtl‐7H‐11_20162, Rcs5 locus), where the 3H locus was found to be the most under‐represented in Canadian germplasm. Additional marker‐trait associations within nurseries were identified on chromosomes 1H, 2H and 4H. Several of the SNP markers identified were found to be polymorphic within the Canadian two‐row germplasm panel and thus could be useful for enhancing spot blotch resistance in two‐row barley for development of resistant cultivars.

Dissecting the Genetic Diversity of USDA Cowpea Germplasm Collection Using Kompetitive Allele Specific PCR-Single Nucleotide Polymorphism Markers.

Cowpea (Vigna unguiculata L. Walp) is an important grain legume crop of the subtropics, particularly in West Africa, where it contributes to the livelihoods of small-scale farmers. Despite being a drought-resilient crop, cowpea production is hampered by insect pests, diseases, parasitic weeds, and various abiotic stresses. Genetic improvement can help overcome these limitations, and exploring diverse cowpea genetic resources is crucial for cowpea breeding. This study evaluated the genetic diversity of 361 cowpea accessions from the USDA core collection for the species using 102 Kompetitive Allele Specific PCR (KASP) single nucleotide polymorphism (SNP) markers. A total of 102 KASP-SNP was validated in the germplasm panel, and 72 showed polymorphism across the germplasm panel. The polymorphism information content (PIC) of all SNPs ranged from 0.1 to 0.37, with an average of 0.29, while the mean observed heterozygosity was 0.52. The population structure revealed three distinct populations that clustered into two major groups after phylogenetic analysis. Analysis of molecular variance (AMOVA) indicated greater genetic variation within populations than among populations. Although cowpea generally has a narrow genetic diversity, the accessions used in this study exhibited considerable variation across geographical regions, sub-species, and improvement status. These results indicated that the selected KASP genotyping assay can provide robust and accurate genotyping data for application in the selection and management of cowpea germplasm in breeding programs and genebanks.

The Phenotypic Diversity of 232 Germplasm Accessions Identifies the Adverse Effects of Flowering Redundancy on Peanut Yield

Peanut is a leguminous crop with an indeterminate growth habit that will continuously flower during the entire reproductive development stage. Flowering redundancy adversely affects the yield and quality of peanut. In this study, eight flowering and five yield-related traits were collected and comprehensively evaluated from a diverse germplasm panel consisting of 232 peanut accessions, aiming to provide a theoretical basis for improving the flowering habit and yield for future peanut breeding efforts. As a result, large phenotypic diversity was observed in 13 traits. Most of the traits suggested high heritability, except high effective flowering duration (HEFD), days to 100% flower cessation (DTC100), and yield per plant (YPP). Days to 90% flower cessation (DTC90), days to DTC100, flowering duration (FD), and low effective flowering duration (LEFD) showed significant negative correlations with 100-seed weight, 100-pod weight, YPP, average weight per pod, and shelling percentage. Principal component analysis (PCA) suggested that flowering redundancy traits as well as yield-related traits contributed more to the first three PCs when compared to other traits. This study addresses the lack of peanut flowering phenotypic and genetic diversity and lays the foundation for in-depth research on redundancy-related genes in peanut flowering.

The Effects of Genetic Distance and Genetic Diversity on Genomic Prediction Accuracy for Soybean Quantitative Disease Resistance to Phytophthora sojae

Assessing quantitative disease resistance and other complex traits can be time- and space-consuming, limiting the number of lines that can be evaluated in a breeding program. A potential improvement is the application of genomic prediction models, where genotypic data are used to calculate genomic estimated breeding values, referred to as genomic selection. Genomic prediction uses training datasets, where a germplasm panel is phenotyped and genotyped to calculate genomic estimated breeding values in a validation panel of lines based solely on genotypic data. To develop an initial phenotypic dataset, breeders may consider utilizing previously phenotyped lines in a publicly available dataset, such as plant introductions (PIs) from the USDA Soybean collection. A relevant question is, how effective is genomic prediction across diverse training and prediction panels? To answer this, we used previously collected phenotypic data for quantitative disease resistance toward Phytophthora sojae. Diverse germplasm panels were represented by sets of PIs originating from the United States, the Republic of Korea, and worldwide, for a total collection of 1,768 PIs. The accuracy of the prediction model was significantly influenced by population differentiation between panels, as well as genetic and phenotypic diversity. Low prediction accuracy resulted when the panel used to create the prediction model was highly differentiated from the validation panel. However, the addition of a small number of accessions to the training panel that were more closely related to the validation panel resulted in increased accuracy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Genome-wide genetic architecture for plant maturity and drought tolerance in diploid potatoes.

Cultivated potato (Solanum tuberosum) is known to be highly susceptible to drought. With climate change and its frequent episodes of drought, potato growers will face increased challenges to achieving their yield goals. Currently, a high proportion of untapped potato germplasm remains within the diploid potato relatives, and the genetic architecture of the drought tolerance and maturity traits of diploid potatoes is still unknown. As such, a panel of 384 ethyl methanesulfonate-mutagenized diploid potato clones were evaluated for drought tolerance and plant maturity under field conditions. Genome-wide association studies (GWAS) were conducted to dissect the genetic architecture of the traits. The results obtained from the genetic structure analysis of the panel showed five main groups and seven subgroups. Using the Genome Association and Prediction Integrated Tool-mixed linear model GWAS statistical model, 34 and 17 significant quantitative trait nucleotides (QTNs) were found associated with maturity and drought traits, respectively. Chromosome 5 carried most of the QTNs, some of which were also detected by using the restricted two-stage multi-locus multi-allele-GWAS haploblock-based model, and two QTNs were found to be pleiotropic for both maturity and drought traits. Using the non-parametric U-test, one and three QTNs, with 5.13%-7.4% phenotypic variations explained, showed favorable allelic effects that increase the maturity and drought trait values. The quantitaive trait loci (QTLs)/QTNs associated with maturity and drought trait were found co-located in narrow (0.5-1kb) genomic regions with 56 candidate genes playing roles in plant development and senescence and in abiotic stress responses. A total of 127 potato clones were found to be late maturing and tolerant to drought, while nine were early to moderate-late maturing and tolerant to drought. Taken together, the data show that the studied germplasm panel and the identified candidate genes are prime genetic resources for breeders and biologists in conventional breeding and targeted gene editing as climate adaptation tools.

Durum wheat heat tolerance loci defined via a north-south gradient.

The global production of durum wheat (Triticum durum Desf.) is hindered by a constant rise in the frequency of severe heat stress events. To identify heat-tolerant germplasm, three different germplasm panels ("discovery," "investigation," and "validation") were studied under a range of heat-stressed conditions. Grain yield (GY) and its components were recorded at each site and a heat stress susceptibility index was calculated, confirming that each 1°C temperature rise corresponds to a GY reduction in durum wheat of 4.6%-6.3%. A total of 2552 polymorphic single nucleotide polymorphisms (SNPs) defined the diversity of the first panel, while 5642 SNPs were polymorphic in the "investigation panel." The use of genome-wide association studies revealed that 36 quantitative trait loci were associated with the target traits in the discovery panel, of which five were confirmed in a "subset" tested imposing heat stress by plastic tunnels, and in the investigation panel. A study of allelic combinations confirmed that Q.icd.Heat.003-1A, Q.icd.Heat.007-1B, and Q.icd.Heat.016-3B are additive in nature and the positive alleles at all three loci resulted in a 16% higher GY under heat stress. The underlying SNPs were converted into kompetitive allele specific PCR markers and tested on the validation panel, confirming that each explained up to 9% of the phenotypic variation for GY under heat stress. These markers can now be used for breeding to improve resilience to climate change and increase productivity in heat-stressed areas.

Genomic regions involved in the control of 1,000-kernel weight in wild relative-derived populations of durum wheat

Terminal drought is one of the most common and devastating climatic stress factors affecting durum wheat (Triticum durum Desf.) production worldwide. The wild relatives of this crop are deemed a vast potential source of useful alleles to adapt to this stress. A nested association mapping (NAM) panel was generated using as a recurrent parent the Moroccan variety ‘Nachit’ derived from Triticum dicoccoides and known for its large grain size. This was recombined to three top-performing lines derived from T. dicoccoides, T. araraticum, and Aegilops speltoides, for a total of 426 inbred progenies. This NAM was evaluated across eight environments (Syria, Lebanon, and Morocco) experiencing different degrees of terminal moisture stress over two crop seasons. Our results showed that drought stress caused on average 41% loss in yield and that 1,000-kernel weight (TKW) was the most important trait for adaptation to it. Genotyping with the 25K TraitGenetics array resulted in a consensus map of 1,678 polymorphic SNPs, spanning 1,723 cM aligned to the reference ‘Svevo’ genome assembly. Kinship distinguished the progenies in three clades matching the parent of origin. A total of 18 stable quantitative trait loci (QTLs) were identified as controlling various traits but independent from flowering time. The most significant genomic regions were named Q.ICD.NAM-04, Q.ICD.NAM-14, and Q.ICD.NAM-16. Allelic investigation in a second germplasm panel confirmed that carrying the positive allele at all three loci produced an average TKW advantage of 25.6% when field-tested under drought conditions. The underlying SNPs were converted to Kompetitive Allele-Specific PCR (KASP) markers and successfully validated in a third germplasm set, where they explained up to 19% of phenotypic variation for TKW under moisture stress. These findings confirm the identification of critical loci for drought adaptation derived from wild relatives that can now be readily exploited via molecular breeding.

Morpho-genetic assessment and dissecting the genetic architecture for Cercospora leaf spot (CLS) resistance in mung bean [Vigna radiata (L.) Wilczek

Cercospora leaf spot (CLS) has a substantial negative impact on mung bean production across the world. It is crucial to identify and incorporate alleles that might be helpful in combating CLS in order to maintain mung bean health, and productivity. Therefore, 90 mung bean genotypes, including four check varieties, and 66 polymorphic SSR markers were included in the present study during pre-rabi 2018 and pre-rabi 2019. Morpho-genetic analysis revealed a higher value of GCV for yield, cluster and pod numbers per plant, and plant height. All traits had excellent genetic advance (GA), with the exception of the number of seeds per pod, which was medium. PCA was used to separate up to 85.656 % of the variation in the characteristics between the two primary PCs. Molecular dissection revealed, the markers CEDG118 and CEDG074 to yield more genetic diversity, followed by CEDG073, CEDG030 and CEDG147. Estimation of allele frequencies revealed some alleles of the markers found linked to CLS resistance, i.e., CEDG001 (111 bp and 135 bp), CEDG006 (135 bp), CEDG153 (117 bp), CEDG210 (193 bp), CEDG117 (112 bp), CEDG020 (143 bp), CEDG067 (93 bp), CEDG146 (111 bp), and CEDG071 (231 bp and 267 bp). The highest private allelic frequency was employed by the markers CEDG153 and CEDG146. The dendrogram divided the panel population into two major clusters. However, two distinct genetic structure were found within the population. According to these findings, the mung bean germplasm panel used in the present study may have the ability to improve the local gene pool and aid in the development of CLS resistance breeding strategies using genomic tools and techniques.

Genetic diversity and population structure of maize inbred lines using phenotypic traits and single nucleotide polymorphism (SNP) markers

Understanding germplasm’s genetic diversity is essential for developing new and improved cultivars with stable yields under diverse environments. The objective of this study was to determine the genetic diversity and population structure of 128 maize inbred lines sourced from the International Institute of Tropical Agriculture (IITA), the International Maize and Wheat Improvement Centre (CIMMYT), and the University of KwaZulu-Natal (UKZN) using 11,450 informative single nucleotide polymorphism (SNP) markers. The inbred lines revealed highly significant (p < 0.001) levels of variability for the key phenotypic traits. The SNP markers had a mean gene diversity (GD) and polymorphic information content (PIC) of 0.40 and 0.31, respectively, indicating the existence of substantial genetic variation across the germplasm panel. The model-based population structure analysis identified three subpopulations (K = 3) among the inbred lines. This corroborated the phylogenetic analysis using phenotypic traits and molecular markers which classified the inbred lines into three groups. The findings of this study identified considerable genetic diversity for the selection of inbred lines with favourable alleles for multiple traits and could be useful to initiate marker-assisted selection (MAS) to identify significant loci associated with agronomic performance and multiple-stress tolerance.

The physio-biochemical characterization reflected different calcium utilization efficiency between the sensitive and tolerant peanut accessions under calcium deficiency.

Peanut yield in southern China is usually limited by calcium deficiency in soil. Most previous studies have found that small-seed varieties showed higher tolerance than large-seed varieties (e.g. Virginia type) under calcium deficiency, however, our preliminary research found that sensitive varieties also existed in small-seed counterparts. Few studies have been conducted to characterize low-calcium tolerance among small-seed germplasms with genetic diversity, and the differences in physiological characteristics between sensitive and tolerant varieties has not been reported yet. Thus, in order to better understand such differences, the current study firstly collected and characterized a diversity germplasm panel consisting of 50 small-seed peanut genotypes via a 2-year field trial, followed by the physiological characterization in sensitive (HN032) and tolerant (HN035) peanut genotypes under calcium deficiency. As a result, the adverse effects brought by calcium deficiency on calcium uptake and distribution in HN032 was much larger than HN035. In details, calcium uptake in the aboveground part (leaves and stems) was reduced by 16.17% and 33.66%, while in the underground part (roots and pods), it was reduced by 13.69% and 68.09% under calcium deficiency for HN035 and HN032, respectively; The calcium distribution rate in the pods of HN035 was 2.74 times higher than HN032. The utilization efficiency of calcium in the pods of HN035 was 1.68 and 1.37 times than that of HN032 under calcium deficiency and sufficiency, respectively. In addition, under calcium deficiency conditions, the activities of antioxidant enzymes SOD, POD, and CAT, as well as the MDA content, were significantly increased in the leaves of HN032, peanut yield was significantly reduced by 22.75%. However, there were no significant changes in the activities of antioxidant enzymes, MDA content, and peanut yield in HN035. Therefore, higher calcium absorption and utilization efficiency may be the key factors maintaining peanut yield in calcium-deficient conditions for tolerant genotypes. This study lays a solid foundation for selecting low-calcium tolerant varieties in future peanut breeding.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Association Mapping of Quantitative Trait Loci for Agronomic Traits in a Winter Wheat Collection Grown in Kazakhstan

Central Asia is an important region for the growth of winter wheat, with a cultivation area of more than 15 million hectares (ha). However, the average yield is approximately 3 tons per ha, which is significantly lower than that in developed countries. Therefore, the development of new competitive high-yielding cultivars, including those based on the application of modern molecular genetics tools, is a key priority in winter wheat breeding projects. One of these tools is the result of the identification of new genes and quantitative trait loci (QTLs) for agronomic traits using diverse germplasm panels and genome-wide association studies (GWAS). In this work, a panel of winter wheat accessions was assembled using 115 accessions from Central Asia and 162 samples from other regions of the world. The GWAS, based on a two-year field evaluation of the collection in Kazakhstan’s southern and southeastern regions and 10,481 polymorphic SNP (single-nucleotide polymorphism) markers, allowed for the detection of 173 stable QTLs in nine studied agronomic traits. A survey of the published scientific literature suggests that 23 of these 173 stable QTLs have locations similar to those of previously reported QTLs, supporting the robustness of the research. In addition, 221 and 162 accessions surpassed local standards for grain yield at Kazakhstan’s southern and southeastern stations, respectively. Therefore, this study is an additional contribution to the identification of new QTLs for key agronomic traits and valuable genetic lines in winter wheat breeding projects.