Multi-locus genome-wide association mapping for major agronomic and yield-related traits in sorghum (Sorghum bicolor (L.) moench) landraces.

BACKGROUND

Sorghum 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.

OBJECTIVE

Explore 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.

METHOD

This 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.

RESULT

Pearson 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 (0.003). A total of 351,692 SNP markers were identified across 10 sorghum chromosomes from 216 Ethiopian sorghum landraces, and we have been refining this to 50,165 filtered SNPs. Manhattan plots indicated significant marker-trait associations (MTAs) across multiple chromosomes, particularly for days to flowering and plant height. Significant QTNs were associated with key traits including flowering time, plant height, and grain yield. ML-GWAS identified 176 QTNs with varying LOD scores and phenotypic effects. Multiple genes linked to these QTNs highlight the complexity of genetic interactions of studied traits with 36 unique and 12 major QTNs. Notable SNP markers were concentrated on chromosomes 1, 2, and 3, reinforcing the importance of these regions for breeding efforts. Candidate gene analysis revealed key genes regulating flowering time, stress response, and yield traits, which could serve as targets for genetic enhancement. In our study, key candidate genes have been successfully identified, these are regulating flowering time, maturity, and stress resilience. Genes such as Sobic.001G196700 and Sobic.002G183400 are identified as critical regulators of floral development. The stress-responsive gene Sobic.005G176100 (a mannose-6-phosphate isomerase), emphasizes the importance of resilience in sorghum cultivation under adverse conditions. Additionally, Sobic.003G324400 and Sobic.004G178300 are essential for regulating plant height and seed weight, making them valuable for yield enhancement breeding programs.

CONCLUSION

This 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.