ArticleBMC genomics2025
Multi-locus genome-wide association mapping for major agronomic and yield-related traits in sorghum (Sorghum bicolor (L.) moench) landraces.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Genome-wide association analysis reveals genetic variations and candidate genes associated with tannin and starch contents in sorghum.BMC genomics · 2026Article
- Genome-wide association analysis of salinity tolerance-related loci in bread wheat (Triticum aestivum L.) at the germination stage.BMC plant biology · 2026Article
- A genome-wide association study unveils the genetic architecture of morphological and biomass partitioning traits in maize under iron-deficient stress.Scientific reports · 2026Article
- Multi-locus genome-wide association mapping reveals loci underlying malt quality traits in heterogeneous Ethiopian Barley (Hordeum vulgare L.) germplasm.Scientific reports · 2026Article
- Genome-wide association and haplotype analyses reveal genetic architecture of seed dormancy in grain sorghum with implications for preharvest sprouting tolerance.The plant genome · 2026Article
- Genome-wide identification of QTNs and candidate genes in Ethiopian sorghum (Sorghum bicolor (L.) moench) landraces using SNP-based approaches.BMC genomic data · 2025Article
- Whole-genome sequencing reveals genetic diversity, population structure, and core collection construction in Korean peach (Frontiers in plant science · 2025Article
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4 authors.
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Abstract
backgroundSorghum is a vital cereal crop for over 750 million people, ranking 5th globally. It has multiple purposes, including food, feed, and biofuels, and is essential in Ethiopia, which has a rich genetic diversity of various agroecological zones.
objectiveExplore marker-trait associations (MTAs) to identify quantitative trait nucleotides (QTNs) and new candidate genes associated with agronomic and yield contributing traits in Ethiopian sorghum landraces using multi-locus GWAS models to assist the genomic-assisted breeding strategies.
methodThis study investigates the genetic basis of agronomic traits in Ethiopian sorghum landraces through multi-locus Genome-Wide Association Studies (ML-GWAS). 216 landraces, improved varieties, and check cultivars were obtained from the Ethiopian Biodiversity Institute and the National Sorghum Improvement Program for this study. The experiment was conducted over two cropping seasons, employing an α-lattice design for phenotyping key traits such as days to flowering, days to maturity, plant height, seed number per plant, grain yield, and thousand seed weight. A mixed linear model (MLM) was used to analyze the phenotypic data and estimate the genetic parameters including variances and the broad sense heritability. GBS with the ApeKI restriction enzyme provided 50,165 high-quality SNP markers. The six ML-GWAS models identified significant QTNs with a LOD score threshold value of ≥ 4.0. The analysis revealed major QTNs associated with traits across multiple chromosomes, supported by a stringent filtering criterion that ensured reliability. Co-localization with known QTLs was explored using the Sorghum QTL Atlas database and candidate genes within significant QTN regions, providing the genetic architecture influencing agronomic performance were identified via the Phytozome platform using the biomaRt package.
resultPearson correlation analysis revealed significant associations among most traits, with p-values less than 0.0001, except for grain yield per plant which showed lower correlations with other traits. Genetic variability analysis indicated that days to flowering exhibited high heritability (0.7) and genetic advance (19.6%) as percent of mean, suggesting strong genetic control, while grain yield displayed extremely low h
conclusionThis study enhances our understanding of the genetic diversity of Ethiopian sorghum landraces, crucial for breeding programs. It identifies key QTNs and candidate genes associated with important agronomic traits, offering insights for marker-assisted and genomic-assisted breeding. The ML-GWAS models highlight the genetic complexity of flowering time and grain yield traits, emphasizing the need for targeted breeding efforts to maximize sorghum productivity.
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