Set Of Germplasm Research Articles

Assessing genotype-by-environment interactions for maydis leaf blight disease in maize (Zea mays L.) germplasm and identification of stable donors for breeding resistant hybrid varieties

Abstract This study investigates the impact of environmental factors and genotype-by-environment interactions (GEI) on the expression of maydis leaf blight (MLB) resistance in a diverse maize germplasm comprising 359 genotypes. Extensive field trials were conducted, involving artificial inoculations and disease scoring across two locations over two years. Using genotype and genotype–environment (GGE) biplot analysis based on the site regression model (SREG), we identified stable MLB-resistant 10 donors with consistent genotypic responses. These inbred lines, which consistently exhibited disease scores of ⩽3 across locations, are recommended as potential parents for breeding MLB-resistant varieties. Furthermore, the identification of a non-crossover interaction and high correlations among testing locations allowed us to define a single mega-environment for the initial screening of MLB resistance in a large set of maize germplasm. This study suggests that initial screenings can be efficiently conducted in one representative location, with validation of resistant lines at multiple sites during advanced breeding stages. This approach optimizes the use of land, labour and resources in MLB resistance testing.

A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1.

The parasitic weed Striga (Striga hermonthica) limits productivity of sorghum (Sorghum bicolor) and other cereals in sub-Saharan Africa and elsewhere. Improved host plant genetics is an effective control method but verified loci contributing to Striga resistance are limited. LOW GERMINATION STIMULANT 1 remains the only known sorghum locus affecting resistance to Striga. Functional loss (lgs1) alleles at this locus result in low Striga germination stimulant activity. We developed a robust polymerase chain reaction (PCR)-based LGS1 marker that detects all known natural lgs1 alleles. We have successfully used this marker to improve Striga resistance in our sorghum breeding program. To check its utility among diverse sets of germplasm, we genotyped 406 lines of the sorghum association panel (SAP) with the marker and phenotyped them for Striga germination stimulant activity. The SAP contains 23 lines (6%) with lgs1 mutations that involve a complete loss of this gene. Three previously described deletion alleles (lgs1-1, lgs1-2, and lgs1-3) ranging from 28.5 to 34kbp are present among SAP members with a new one, lgs1-6, missing nearly 50kbp relative to the reference genome. All 23 members of the SAP carrying lgs1 alleles had low Striga germination stimulant activity. The smaller previously described intragenic deletion mutations lgs1-4 and lgs1-5 are not present in the SAP. The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.

Analysis of genetic variability for nutritional quality traits among maize inbreds adapted to Punjab: A North‐Western province of India

AbstractBackground and ObjectivesThe deficiency of essential amino acids such as methionine, lysine, and tryptophan in maize protein presents a significant challenge in meeting nutritional demand. Comprehensive evaluation of maize germplasm for variability of essential amino acids, protein content, starch content, and β‐carotene contents in grains, along with their relationships with yield and its associated traits, is a prerequisite for strengthening genetic enhancement for nutritional traits. Thus, the current study presents the profiling of 60 maize inbred lines across 11 nutritional quality and yield, aiming to identify variability in these traits and potential donor candidates for commercial breeding.FindingsThe test inbreds exhibited significant variations in the content of grain methionine (0.04%–0.28%), tryptophan (0.019%–0.069%), lysine (0.063%–0.318%), protein (9.31%–11.80%), starch (58.32%–70.88%), and β‐carotene (2.38 ppm–6.79 ppm). Additionally, variations were noted in grain yield (0.87–2.52 t ha−1). Inter‐relationship analyses suggested the feasibility of simultaneous selection of essential amino acids (methionine, tryptophan, lysine) and β‐carotene. The germplasm stock was clustered, and potential diverse donors identified were recommended for utilization in breeding programs. Furthermore, the use of Attenuated Total Reflectance‐Fourier Transform Infrared (ATR‐FTIR) spectroscopy for screening high methionine and high lysine lines in large germplasm based on differences in peak intensity at 2922 cm−1 and 1110 cm−1 was reported, respectively.ConclusionsThe multilocation experiment revealed genetic diversity in both nutritional quality and yield across the 60 inbreds. The absence of a negative correlation between the amino acids and β‐carotene indicates the possibility of the development of hybrids with elevated essential amino‐acid levels combined with high β‐carotene content.Significance and NoveltyThis is the first holistic report on the variability of nutritional quality traits coupled with yield in maize germplasm. Identified nutrient‐rich inbreds will serve as valuable germplasm in maize biofortification programs. AT‐FTIR is proposed as an easy screening technique and the first report, which can be employed for the initial screening of a large set of germplasm for methionine and lysine content.

Identification of new sources of resistance against late leaf spot disease of groundnut using molecular and field screenings

ABSTRACT Late leaf spot (LLS), caused by the fungus Nothopassalora personata, is an important foliar disease in groundnut (Arachis hypogaea L.) production. The present study used previously reported three single nucleotide polymorphism (SNP) markers to screen a set of groundnut germplasm and compared them with phenotypic values of three hotspot locations. The results indicated that marker-identified genotypes had LLS-resistant or moderately resistant phenotypes. About 56% of the phenotypically confirmed genotypes were confirmed with the SNP genotype. Some of the phenotypically resistant genotypes were not confirmed with marker genotypes, indicating some other quantitative trait locus (QTL) responsible for the LLS resistance might have been involved. The findings in this study are relevant to future LLS resistance breeding programs.

Genetic diversity and population structure of wheat landraces in Southern Winter Wheat Region of China

BackgroundWheat landraces are considered a valuable source of genetic diversity for breeding programs. It is useful to evaluate the genetic diversity in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. In addition, constructing a core germplasm set that represents the genetic diversity of the entire variety set is of great significance for the efficient conservation and utilization of wheat landrace germplasms.ResultsTo understand the genetic diversity in wheat landrace, 2,023 accessions in the Jiangsu Provincial Crop Germplasm Resource Bank were used to explore the molecular diversity and population structure using the Illumina 15 K single nucleotide polymorphism (SNP) chip. These accessions were divided into five subpopulations based on population structure, principal coordinate and kinship analysis. A significant variation was found within and among the subpopulations based on the molecular variance analysis (AMOVA). Subpopulation 3 showed more genetic variability based on the different allelic patterns (Na, Ne and I). The M strategy as implemented in MStratv 4.1 software was used to construct the representative core collection. A core collection with a total of 311 accessions (15.37%) was selected from the entire landrace germplasm based on genotype and 12 different phenotypic traits. Compared to the initial landrace collections, the core collection displayed higher gene diversity (0.31) and polymorphism information content (PIC) (0.25), and represented almost all phenotypic variation.ConclusionsA core collection comprising 311 accessions containing 100% of the genetic variation in the initial population was developed. This collection provides a germplasm base for effective management, conservation, and utilization of the variation in the original set.

Phenotypic Characterization and Yield Screening of Quinoa Germplasms in Diverse Low-Altitude Regions: A Preliminary Study

In this study, 300 quinoa accessions were systematically screened for adaptation to low-altitude areas using seventeen traits. The experiment was conducted in Taigu District, Jinzhong City, Shanxi Province, in 2021, at an altitude of 791 m. Out of the 300 genotypes, 107 were able to produce flowers and fruit, with yields ranging from 538 to 5132 kg ha−1 and with 1000-grain weights between 1.19 g and 2.37 g. These 107 quinoa genotypes were categorized into four groups based on grain yield: below 1500, 1500–2250, 2250–3000, and above 3000 kg ha−1. These groups consisted of 33, 33, 24, and 17 genotypes, respectively. This study found that the 1000-grain weight ranged from 1.19 g to 2.37 g, with an average of 1.72 g, 1.72 g, 1.83 g, and 1.92 g for the respective yield levels. Among the 107 genotypes, 25 had a 1000-grain weight exceeding 2 g, and 13 of these genotypes also had yields exceeding 2250 kg ha−1. The growth period of quinoa genotypes in the low-altitude area was approximately 138–142 days, with longer growth periods associated with higher yield levels. JQ-00084 is the only genotype with a yield > 3000 kg ha−1 and meets all the growth criteria, including a 1000-grain weight exceeding 2 g and a flowering-to-maturity period of over 80 days. Meanwhile, JQ-00315, JQ-00521, JQ-1029, and JQ-02405 fulfilled at least four criteria. These results highlight the importance of germplasm screening for low altitudes, and a large set of germplasms could be screened using the above criteria of phenology, growth, and yield traits.

Combining genome-wide association study and linkage mapping in the genetic dissection of amylose content in maize (Zea mays L.).

Integrated linkage and association analysis revealed genetic basis across multiple environments. The genes Zm00001d003102 and Zm00001d015905 were further verified to influence amylose content using gene-based association study. Maize kernel amylose is an important source of human food and industrial raw material. However, the genetic basis underlying maize amylose content is still obscure. Herein, we used an intermated B73 × Mo17 (IBM) Syn10 doubled haploid population composed of 222 lines and a germplasm set including 305 inbred lines to uncover the genetic control for amylose content under four environments. Linkage mapping detected 16 unique QTL, among which four were individually repeatedly identified across multiple environments. Genome-wide association study revealed 17 significant (P = 2.24E-06) single-nucleotide polymorphisms, of which two (SYN19568 and PZE-105090500) were located in the intervals of the mapped QTL (qAC2 and qAC5-3), respectively. According to the two population co-localized loci, 20 genes were confirmed as the candidate genes for amylose content. Gene-based association analysis indicated that the variants in Zm00001d003102 (Beta-16-galactosyltransferase GALT29A) and Zm00001d015905 (Sugar transporter 4a) affected amylose content across multi-environment. Tissue expression analysis showed that the two genes were specifically highly expressed in the ear and stem, respectively, suggesting that they might participate in sugar transport from source to sink organs. Our study provides valuable genetic information for breeding maize varieties with high amylose.

Gap derivative optimization for modeling wheat grain protein using near‐infrared transmission spectroscopy

AbstractBackground and ObjectiveNear‐infrared spectroscopy is an established tool for the estimation of different nutrients in diverse sample matrices. Of these, near‐infrared transmittance (NIT) has very wide usage in whole grain analysis for oil, protein, and other macronutrients. NIT spectra obtained from samples are regressed with actual laboratory values for developing prediction models. However, the spectra obtained are sloppy, slightly noisy, and show baseline drifts. To increase the resolution and signal‐to‐noise ratio, derivatives are common preprocessing tools, typically implemented along with smoothing.FindingsA systematic study on different derivatives (1, 2, and 3) and gaps (2–90) was performed. The germplasm set with high variability for protein content (8.63%–19.56%) was used, and regression models were developed using the modified partial least squares method. Among all, the second‐order derivative gave best‐fit models; hence, the results of the gap with second‐order derivatives are studied in detail. The plot of R2 for external validation set with different gaps at second‐order gave three peaks, namely, at 47, 60, (69, 70, 71) where the highest R2 (0.985) was obtained for the third peak having three consecutive gap segments.ConclusionHence, math treatment (2, 70, 2, 1) was finalized considering stability where a high residual prediction deviation of 7.149 and a low bias of (0.021) was obtained. A paired t test and reliability test between predicted and laboratory values confirmed nonsignificant differences between them. Thus, the developed model is robust and precise and can be utilized in high throughput screening of wheat germplasm.Significance and NoveltyBetter performance at second derivative and higher gap can be used for developing robust models with low bias by avoiding multi‐collinearity, which is usually a limitation in multi‐variate analysis.

Mining genomic regions associated with agronomic and biochemical traits in quinoa through GWAS

Quinoa (Chenopodium quinoa Willd.), an Andean crop, is a facultative halophyte food crop recognized globally for its high nutritional value and plasticity to adapt to harsh conditions. We conducted a genome-wide association study on a diverse set of quinoa germplasm accessions. These accessions were evaluated for the following agronomic and biochemical traits: days to 50% flowering (DTF), plant height (PH), panicle length (PL), stem diameter (SD), seed yield (SY), grain diameter (GD), and thousand-grain weight (TGW). These accessions underwent genotyping-by-sequencing using the DNBSeq-G400R platform. Among all evaluated traits, TGW represented maximum broad-sense heritability. Our study revealed average SNP density of ≈ 3.11 SNPs/10 kb for the whole genome, with the lowest and highest on chromosomes Cq1B and Cq9A, respectively. Principal component analysis clustered the quinoa population in three main clusters, one clearly representing lowland Chilean accessions, whereas the other two groups corresponded to germplasm from the highlands of Peru and Bolivia. In our germplasm set, we estimated linkage disequilibrium decay to be ≈ 118.5 kb. Marker-trait analyses revealed major and consistent effect associations for DTF on chromosomes 3A, 4B, 5B, 6A, 7A, 7B and 8B, with phenotypic variance explained (PVE) as high as 19.15%. Nine associations across eight chromosomes were also found for saponin content with 20% PVE by qSPN5A.1. More QTLs were identified for PL and TGW on multiple chromosomal locations. We identified putative candidate genes in the genomic regions associated with DTF and saponin content. The consistent and major-effect genomic associations can be used in fast-tracking quinoa breeding for wider adaptation across marginal environments.

C-terminally encoded peptide-like genes are associated with the development of primary root at qRL16.1 in soybean.

Root architecture traits are belowground traits that harness moisture and nutrients from the soil and are equally important to above-ground traits in crop improvement. In soybean, the root length locus qRL16.1 was previously mapped on chromosome 16. The qRL16.1 has been characterized by transcriptome analysis of roots in near-isogenic lines (NILs), gene expression analysis in a pair of lines contrasting with alleles of qRL16.1, and differential gene expression analysis in germplasm accessions contrasting with root length. Two candidate genes, Glyma.16g108500 and Glyma.16g108700, have shown relatively higher expression in longer root accessions than in shorter rooting accessions. The C-terminal domain of Glyma.16g108500 and Glyma.16g108700 is similar to the conserved domain of C-terminally encoded peptides (CEPs) that regulate root length and nutrient response in Arabidopsis. Two polymorphisms upstream of Glyma.16g108500 showed a significant association with primary root length and total root length traits in a germplasm set. Synthetic peptide assay with predicted CEP variants of Glyma.16g108500 and Glyma.16g108700 demonstrated their positive effect on primary root length. The two genes are root-specific in the early stage of soybean growth and showed differential expression only in the primary root. These genes will be useful for improving soybean to develop a deep and robust root system to withstand low moisture and nutrient regimes.

Mapping of a Major-Effect Quantitative Trait Locus for Seed Dormancy in Wheat.

The excavation and utilization of dormancy loci in breeding are effective endeavors for enhancing the resistance to pre-harvest sprouting (PHS) of wheat varieties. CH1539 is a wheat breeding line with high-level seed dormancy. To clarify the dormant loci carried by CH1539 and obtain linked molecular markers, in this study, a recombinant inbred line (RIL) population derived from the cross of weak dormant SY95-71 and strong dormant CH1539 was genotyped using the Wheat17K single-nucleotide polymorphism (SNP) array, and a high-density genetic map covering 21 chromosomes and consisting of 2437 SNP markers was constructed. Then, the germination percentage (GP) and germination index (GI) of the seeds from each RIL were estimated. Two QTLs for GP on chromosomes 5A and 6B, and four QTLs for GI on chromosomes 5A, 6B, 6D and 7A were identified. Among them, the QTL on chromosomes 6B controlling both GP and GI, temporarily named QGp/Gi.sxau-6B, is a major QTL for seed dormancy with the maximum phenotypic variance explained of 17.66~34.11%. One PCR-based diagnostic marker Ger6B-3 for QGp/Gi.sxau-6B was developed, and the genetic effect of QGp/Gi.sxau-6B on the RIL population and a set of wheat germplasm comprising 97 accessions was successfully confirmed. QGp/Gi.sxau-6B located in the 28.7~30.9 Mbp physical position is different from all the known dormancy loci on chromosomes 6B, and within the interval, there are 30 high-confidence annotated genes. Our results revealed a novel QTL QGp/Gi.sxau-6B whose CH1539 allele had a strong and broad effect on seed dormancy, which will be useful in further PHS-resistant wheat breeding.

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

Development of Methods for Improving Flowering and Seed Set of Diverse Germplasm in Cassava Breeding

Development of Methods for Improving Flowering and Seed Set of Diverse Germplasm in Cassava Breeding

Identification of novel germplasm and genetic loci for enhancing mineral element uptake in soybean

Bioavailability, uptake, and spatial distribution of essential and toxic mineral elements are pivotal factors that govern crop growth, development, and productivity. Soybean is a major leguminous crop globally, and yield losses are commonly attributed to various abiotic factors, including nutrient deficiencies or the influence of toxic minerals in the soil. Therefore, understanding the molecular basis of differential mineral element uptake, translocation, and accumulation in soybean is vital for developing improved cultivars. Here, we used portable X-ray fluorescence (p-XRF) for rapid and high-throughput mineral element profiling of a diverse set of soybean germplasm using leaves, stem, root, and seed tissues. Genome-wide association (GWAS) was performed on the element profiles of 219 soybean accessions, revealing lines with notable two to tenfold difference in various mineral nutrient uptake. These identified lines represent valuable genetic resources for germplasm development and gene discovery. The GWAS analysis pinpointed significant genomic loci and haplotypes associated with accumulation of aluminum (Al), silicon (Si), iron (Fe), and manganese (Mn). Remarkably, genes associated with the transport of solutes, metals, and ions have been pinpointed, indicating their potential involvement in the nutrient uptake and soybean improvement. To gain functional insights, Si deposition, allelic variants and expression of three Si transporter genes was studied in greater details. The high Si accumulating line PI548452 showed 12-fold increase in leaf Si content compared to low Si lines. Expression of HiSil2b and HiSil2c effluxer genes showed root and leaf specific expression, respectively, providing evidence for tissue specific Si transport and the basis for precise management of Si uptake to improve abiotic and biotic stresses. In summary, this study uncovered the novel accessions, haplotypes, allelic diversity, and the potential candidate genes underpinning the mineral nutrient accumulation in soybean.

Exploring the extents of genetic diversity and population structure of enset [Ensete ventricosum (Welw.) cheesman] from southern Ethiopia using simple sequence repeat markers: Implications for crop improvement and conservation

Enset (Ensete ventricosum) is a multi-use perennial herbaceous crop used as a staple food for over 20 million people in Ethiopia. Despite its high use values, very few studies have been conducted to improve this crop, particularly using molecular marker systems. In this context, the study aimed at evaluating the magnitude of genetic diversity and population structure of enset germplasm collections from four major enset growing zones in southern Ethiopia using 12 simple sequence repeat (SSR) markers. A total of 147 individual leaf samples were collected from the entire enset populations and gave 289 alleles, ranging from 12 to 41 alleles per locus, with a mean of 24.5. The polymorphism information content for each locus varied from 0.86 to 0.95, with a mean of 0.91. The number of effective alleles ranged from 5.13 to 11.79 with a mean of 8.27. The expected and observed heterozygosity showed average values of 0.85 and 0.84, respectively. The greatest genetic distance (1.16) was between Gurage and wild populations, while the shortest (0.37) was between Gurage and Silte. Among the six populations, the wild had the highest percentage of polymorphic loci (100%). AMOVA attributed 89% of the genetic variation to intra-population and only 11% to among populations. The whole set of germplasm indicates low genetic differentiation and high gene flow (Nm). The UPGMA and principal coordinates largely correspond to each other and indicate three major groups. Overall, the information gained from this study would be useful for enset improvements and conservation strategies.

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.

Genomic characterization and gene bank curation of Aegilops: the wild relatives of wheat.

Genetic diversity found in crop wild relatives is critical to preserve and utilize for crop improvement to achieve sustainable food production amid climate change and increased demand. We genetically characterized a large collection of 1,041 Aegilops accessions distributed among 23 different species using more than 45K single nucleotide polymorphisms identified by genotyping-by-sequencing. The Wheat Genetics Resource Center (WGRC) Aegilops germplasm collection was curated through the identification of misclassified and redundant accessions. There were 49 misclassified and 28 sets of redundant accessions within the four diploid species. The curated germplasm sets now have improved utility for genetic studies and wheat improvement. We constructed a phylogenetic tree and principal component analysis cluster for all Aegilops species together, giving one of the most comprehensive views of Aegilops. The Sitopsis section and the U genome Aegilops clade were further scrutinized with in-depth population analysis. The genetic relatedness among the pair of Aegilops species provided strong evidence for the species evolution, speciation, and diversification. We inferred genome symbols for two species Ae. neglecta and Ae. columnaris based on the sequence read mapping and the presence of segregating loci on the pertinent genomes as well as genetic clustering. The high genetic diversity observed among Aegilops species indicated that the genus could play an even greater role in providing the critical need for untapped genetic diversity for future wheat breeding and improvement. To fully characterize these Aegilops species, there is an urgent need to generate reference assemblies for these wild wheats, especially for the polyploid Aegilops.

Molecular characterization of doubled haploid lines derived from different cycles of the Iowa Stiff Stalk Synthetic (BSSS) maize population.

Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright's F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program.

Productivity and Stability Evaluation of 12 Selected Avena magna ssp. domestica Lines Based on Multi-Location Experiments during Three Cropping Seasons in Morocco

Avena magna (2n = 4x = 28) is a tetraploid oat with a very high protein content compared to the hexaploid common oat, A. sativa (2n = 6x = 42). The wild type of A. magna originates from Morocco; its domestication has been achieved only within the past 25 years. The present study aimed to evaluate the productivity potential of an A. magna ssp. domestica collection of 11 advanced lines and a control variety, ‘Avery’. Twelve trials were conducted during three cropping seasons at four, three, and five locations and revealed significant differences among the accessions. Data on twelve agro-morphological characters and two disease traits were collected, and they confirmed the presence of variability in this oat germplasm set. Mean grain yield was 30.76 q/ha and varied from site to site, ranging from 6.89 q/ha at Bouchane_19 to 85.5 q/ha at Alnif_21. Across experimental sites, plant height ranged from 48.93 to 120.47 cm; thousand kernel weight from 32.83 to 49.73 g; and harvest index from 20.43 to 31.33%. Line AT6 was relatively tolerant of BYDV and crown rust infections, based on disease severity scoring at the heading stage. According to AMMI analysis, 78% of the grain yield variability was due to the environment factor and 4% was explained by the genetic factor. Among the highest-yielding lines, AT5 and ATC were relatively unstable. Line AT5 was more productive at the elevated site of El Kbab_19, and ATC performed better at the oasis location of Alnif_21 under irrigation. Line AT7 showed the most stable behavior; it was high yielding across the five environments and exceeded the general mean of the experiments. The A. magna ssp. domestica lines proved their suitability for cultivation under local farming conditions. Their nutritional quality, especially their high protein content, makes them good candidates for further testing in the Moroccan breeding program and for integration into local cropping systems.

Genetic Diversity Studies and Fusarium Wilt Screening in Chickpea (Cicer arietinum L.) Germplasm

Background: The genetic base of chickpea has narrowed substantially during the domestication process (Thudi et al., 2016). Therefore, increasing the genetic divergence of chickpea has been a major goal for breeders. The assessment of genetic diversity of chickpea germplasm can provide a valuable information to the breeder for parental selection strategies in plant breeding programs. Method: Two hundred and eighty chickpea germplasm including two checks (JG-11 and SA-1) were tested with an objective to explore the genetic divergence by using Mahalanobis D2 statistics. The field experiment was carried out during Rabi 2021-22 at Zonal Agricultural Research Station (ZARS), Kalaburagi. The magnitude of genetic divergence was studied using data obtained from five quantitative traits. Results: The 280 genotypes including checks were divided into seventeen clusters based on their D2 values. Out of seventeen clusters, cluster II (89 genotypes) was the largest followed by cluster I (83 genotypes), cluster III (31 genotypes), cluster VIII (29 genotypes), cluster VII (24 genotypes), cluster VII (15 genotypes). Same set of germplasm were also screened for fusarium wilt in wilt sick plot, 30 germplasm lines showed resistant (10.71%), 83 moderately resistant (29.64%), 44 moderately susceptible (15.71%), 31 susceptible (11.07%) and 92 highly susceptible reaction to fusarium wilt. Clusters XIV and XVI had extreme genetic distance and the germplasm lines in these cluster also showed moderate resistance to fusarium wilt.