Agromorphological Studies for Variability, Heritability and their Associations of Local Wheat Varieties (Triticum Spp.) Grown in South Gondar Zone, Ethiopia

Wheat is the most important cereal crop in Ethiopia ranking third in terms of area after teff and maize and second in terms of production after maize. Six local wheat varieties commonly cultivated in South Gondar, one of the eleven zones found in Amhara region, were collected from the local farmers to study their variability and associations of yield and yield related traits using twelve agro-morphological traits. The studied genotypes were grown in main rain season of 2014/15 at Farta district in a complete randomized block design with three replications. Analysis of variance indicated the presence of highly significant difference among the studied genotypes which revealed the presence of high genetic variability of wheat in the study area. Highest yield was recorded from the local variety Ferno with 1957 kg/ha followed by Chekole (1588.33kg) and Canada Sendie (1580.7kg). Higher value of GCV and PCV were recorded in most of the studied traits indicating selection may be effective from these traits and phenotypic expression would be good indication of the genotypic potential. Broad sense heritability estimates were very high for most traits signifying the possibility of success in selection. Correlation study revealed that number of tillers per plant, number of seeds per plant and harvest index had positive and highly significant correlation with grain yield. The present investigation will guide in planning future breeding strategy with desired traits to improve this crop in the study area. Keywords: Local wheat varieties; agro-morphological traits; genetic variability; Heritability; Correlation

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

Estimating the allelic variation and exploring the genetic basis of quantitatively inherited complex traits are the two foremost breeding scenarios for sustainable crop production. The current study utilized 188 wrinkled vining pea genotypes comprising historical varieties and breeding lines to evaluate the existing genetic diversity and to detect molecular markers associated with traits relevant to vining pea production, such as wrinkled vining pea yield (YTM100), plant height (PH), earliness (ERL), adult plant resistance to downy mildew (DM), pod length (PDL), numbers of pods per plant (PDP), number of peas per pod (PPD), and percent of small wrinkled vining peas (PSP). Marker-trait associations (MTAs) were conducted using 6902 quality single nucleotide polymorphism (SNP) markers generated from the diversity arrays technology sequencing (DArTseq) and Genotyping-by-sequencing (GBS) sequencing methods. The best linear unbiased prediction (BLUP) values were estimated from the two-decadeslong (1999–2020) unbalanced phenotypic data sets recorded from two private breeding programs, the Findus and the Birds eye, now owned by Nomad Foods. Analysis of variance revealed a highly significant variation between genotypes and genotype-byenvironment interactions for the ten traits. The genetic diversity and population structure analyses estimated an intermediate level of genetic variation with two optimal subgroups within the current panel. A total of 48 significant (P < 0.0001) MTAs were identified for eight different traits, including five for wrinkled vining pea yield on chr2LG1, chr4LG4, chr7LG7, and scaffolds (two), and six for adult plant resistance to downy mildew on chr1LG6, chr3LG5 (two), chr6LG2, and chr7LG7 (two). We reported several novel MTAs for different crucial traits with agronomic importance in wrinkled vining pea production for the first time, and these candidate markers could be easily validated and integrated into the active breeding programs for marker-assisted selection

Genetic characterization and genome-wide association mapping for stem rust resistance in spring bread wheat

BACKGROUND: Emerging wheat stem rust races have become a major threat to global wheat production. Finding additional loci responsible for resistance to these races and incorporating them into currently cultivated varieties is the most economic and environmentally sound strategy to combat this problem. Thus, this study was aimed at characterizing the genetic diversity and identifying the genetic loci conferring resistance to the stem rust of wheat. To accomplish this, 245 elite lines introduced from the International Center for Agricultural Research in the Dry Areas (ICARDA) were evaluated under natural stem rust pressure in the field at the Debre Zeit Agricultural Research Center, Ethiopia. The single nucleotide polymorphisms (SNP) marker data was retrieved from a 15 K SNP wheat array. A mixed linear model was used to investigate the association between SNP markers and the best linear unbiased prediction (BLUP) values of the stem rust coefficient of infection (CI). RESULTS: Phenotypic analysis revealed that 46% of the lines had a coefficient of infection (CI) in a range of 0 to 19. Genome-wide average values of 0.38, 0.20, and 0.71 were recorded for Nei’s gene diversity, polymorphism information content, and major allele frequency, respectively. A total of 46 marker-trait associations (MTAs) encompassed within eleven quantitative trait loci (QTL) were detected on chromosomes 1B, 3A, 3B, 4A, 4B, and 5A for CI. Two major QTLs with –log(10) (p) ≥ 4 (EWYP1B.1 and EWYP1B.2) were discovered on chromosome 1B. CONCLUSIONS: This study identified several novel markers associated with stem rust resistance in wheat with the potential to facilitate durable rust resistance development through marker-assisted selection. It is recommended that the resistant wheat genotypes identified in this study be used in the national wheat breeding programs to improve stem rust resistance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-022-01030-4

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

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 (r

Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat

Septoria tritici blotch (STB) is a destructive foliar diseases threatening wheat grain yield. Wheat breeding for STB disease resistance has been identifed as the most sustainable and environmentfriendly approach. In this work, a panel of 316 winter wheat breeding lines from a commercial breeding program were evaluated for STB resistance at the seedling stage under controlled conditions followed by genome-wide association study (GWAS) and genomic prediction (GP). The study revealed a signifcant genotypic variation for STB seedling resistance, while disease severity scores exhibited a normal frequency distribution. Moreover, we calculated a broad-sense heritability of 0.62 for the trait. Nine single- and multi-locus GWAS models identifed 24 marker-trait associations grouped into 20 quantitative trait loci (QTLs) for STB seedling-stage resistance. The seven QTLs located on chromosomes 1B, 2A, 2B, 5B (two), 7A, and 7D are reported for the frst time and could potentially be novel. The GP cross-validation analysis in the RR-BLUP model estimated the genomic-estimated breeding values (GEBVs) of STB resistance with a prediction accuracy of 0.49. Meanwhile, the GWAS assisted wRR-BLUP model improved the accuracy to 0.58. The identifed QTLs can be used for markerassisted backcrossing against STB in winter wheat. Moreover, the higher prediction accuracy recorded from the GWAS-assisted GP analysis implies its power to successfully select superior candidate lines based on their GEBVs for STB resistance

Genome-wide association mapping for grain shape and color traits in Ethiopian durum wheat (Triticum turgidum ssp. durum)

Abstract Grain shape and color strongly influence yield and quality of durum wheat. Identifying QTL for these traits is essential for transferring favorable alleles based on selection strategies and breeding objectives. In the present study, 192 Ethiopian durum wheat accessions comprising 167 landraces and 25 cultivars were genotyped with a high-density Illumina iSelect 90 K single-nucleotide polymorphism (SNP) wheat array to conduct a genome-wide association analysis for grain width (GW), grain length (GL), CIE (Commission Internationale l'Eclairage) L* (brightness), CIE a* (redness), and CIE b* (yellowness) traits. The accessions were planted at Sinana Agricultural Research Center, Ethiopia in the 2015/2016 cropping season in a complete randomized block design with three replications. Twenty homogeneous and healthy seeds per replicate were used for trait measurement. Digital image analysis of seeds with GrainScan software package was used to generate the phenotypic data. Analysis of variance revealed highly significant differences between accessions for all traits. A total of 46 quantitative trait loci (QTL) were identified for all traits across all chromosomes. One novel major candidate QTL (−lg P ≥ 4) with pleiotropic effects for grain CIE L* (brightness) and CIE a* (redness) was identified on the long arm of chromosome 2A. Eighteen nominal QTL (−lg P ≥ 3) and 26 suggestive QTL (−lg P ≥ 2.5) were identified. Pleiotropic QTL influencing both grain shape and color were identified

Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment

Globally, over 2 billion people suffer from malnutrition due to inadequate intake of micronutrients. Genomic-assisted breeding is identified as a valuable method to facilitate developing new improved plant varieties targeting grain yield and micronutrient-related traits. In this study, a genome-wide association study (GWAS) and single- and multi-trait-based genomic prediction (GP) analysis was conducted using a set of 252 elite wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA). The objective was to identify linked SNP markers, putative candidate genes and to evaluate the genomic estimated breeding values (GEBVs) of grain yield and micronutrient-related traits.. For this purpose, a field trial was conducted at a drought-prone station, Merchouch, Morocco for 2 consecutive years (2018 and 2019) followed by GWAS and genomic prediction analysis with 10,173 quality SNP markers. The studied genotypes exhibited a significant genotypic variation in grain yield and micronutrient-related traits. The GWAS analysis identified highly significantly associated markers and linked putative genes on chromosomes 1B and 2B for zinc (Zn) and iron (Fe) contents, respectively. The genomic predictive ability of selenium (Se) and Fe traits with the multi-trait-based GP GBLUP model was 0.161 and 0.259 improving by 6.62 and 4.44%, respectively, compared to the corresponding single-trait-based models. The identified significantly linked SNP markers, associated putative genes, and developed GP models could potentially facilitate breeding programs targeting to improve the overall genetic gain of wheat breeding for grain yield and biofortification of micronutrients via marker-assisted (MAS) and genomic selection (GS) methods

Genomic selection in plant breeding: key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP—theoretically reaching one when using the Pearson’s correlation as a metric—is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding

Single‑ and multi‑trait genomic prediction and genome‑wide association analysis of grain yield and micronutrient‑related traits in ICARDA wheat under drought environment

Globally, over 2 billion people sufer from malnutrition due to inadequate intake of micronutrients. Genomic-assisted breeding is identifed as a valuable method to facilitate developing new improved plant varieties targeting grain yield and micronutrient-related traits. In this study, a genome-wide association study (GWAS) and single- and multi-trait-based genomic prediction (GP) analysis was conducted using a set of 252 elite wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA). The objective was to identify linked SNP markers, putative candidate genes and to evaluate the genomic estimated breeding values (GEBVs) of grain yield and micronutrient-related traits.. For this purpose, a feld trial was conducted at a drought-prone station, Merchouch, Morocco for 2 consecutive years (2018 and 2019) followed by GWAS and genomic prediction analysis with 10,173 quality SNP markers. The studied genotypes exhib ited a signifcant genotypic variation in grain yield and micronutrient-related traits. The GWAS analysis identifed highly signifcantly associated markers and linked putative genes on chromosomes 1B and 2B for zinc (Zn) and iron (Fe) contents, respectively. The genomic predictive ability of selenium (Se) and Fe traits with the multi-trait-based GP GBLUP model was 0.161 and 0.259 improving by 6.62 and 4.44%, respectively, compared to the corresponding single-trait-based models. The identifed signifcantly linked SNP markers, associated putative genes, and developed GP models could potentially facilitate breeding programs targeting to improve the overall genetic gain of wheat breeding for grain yield and biofortifcation of micronutrients via marker-assisted (MAS) and genomic selection (GS) method

Genome-Wide Association Analysis and Genomic Prediction for Adult-Plant Resistance to Septoria Tritici Blotch and Powdery Mildew in Winter Wheat

Septoria tritici blotch (STB) caused by the fungal pathogen Zymoseptoria tritici and powdery mildew (PM) caused by Blumeria graminis f.sp tritici (Bgt) are among the forefront foliar diseases of wheat that lead to a significant loss of grain yield and quality. Resistance breeding aimed at developing varieties with inherent resistance to STB and PM diseases has been the most sustainable and environment-friendly approach. In this study, 175 winter wheat landraces and historical cultivars originated from the Nordic region were evaluated for adult-plant resistance (APR) to STB and PM in Denmark, Estonia, Lithuania, and Sweden. Genome-wide association study (GWAS) and genomic prediction (GP) were performed based on the adult-plant response to STB and PM in field conditions using 7,401 single-nucleotide polymorphism (SNP) markers generated by 20K SNP chip. Genotype-by-environment interaction was significant for both disease scores. GWAS detected stable and environment-specific quantitative trait locis (QTLs) on chromosomes 1A, 1B, 1D, 2B, 3B, 4A, 5A, 6A, and 6B for STB and 2A, 2D, 3A, 4B, 5A, 6B, 7A, and 7B for PM adult-plant disease resistance. GP accuracy was improved when assisted with QTL from GWAS as a fixed effect. The GWAS-assisted GP accuracy ranged within 0.53-0.75 and 0.36-0.83 for STB and PM, respectively, across the tested environments. This study highlights that landraces and historical cultivars are a valuable source of APR to STB and PM. Such germplasm could be used to identify and introgress novel resistance genes to modern breeding lines