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Uncovering key genomic regions for agronomical traits and stress tolerance in sorghum [Sorghum bicolor (L.) Moench] through meta-QTL analysis

  • Shruti Verma,
  • Zeba Quaiyum,
  • Bishun Deo Prasad,
  • Sangita Sahni,
  • Yogesh Dashrath Naik,
  • Rushikesh Sanjay Mane,
  • Ramesh Eerapagula,
  • Ajay Kumar Mahato,
  • Sonam Jha,
  • Sagar Krushnaji Rangari,
  • Soumya Mishra,
  • V. K. Sharma,
  • K. L. Bhutia,
  • Mahendar Thudi

摘要

Sorghum [Sorghum bicolor (L.) Moench] is a crucial food and fodder crop worldwide, particularly significant in the current era of climate change due to its capacity to withstand abiotic stresses such as drought and heat. Despite its resilience, the growth and production of sorghum are highly impacted by various stresses. Identifying consensus genomic regions and high confidence QTLs will enhance trait improvement through genomics assisted breeding. Towards this direction, in this study, we performed Meta-QTL analysis deploying 235 QTLs responsible for biotic stresses (sorghum shoot fly and ergot disease), abiotic stresses (drought and salinity), yield and other agronomic traits reported earlier. We constructed a high-density genetic map comprising 1257 markers spanning a distance of 2214.77 cM. Additionally, we have identified 26 Meta-QTLs in sorghum. The average confidence interval of reported Meta-QTLs underwent 2.56 fold refinement compared to the initial QTLs, resulting in identifying both well-known functionally characterized genes and several potential novel candidate genes for the investigated traits. Tissue-specific in silico expression analysis of defense-related genes, such as Sobic.002G013800 (Meta-QTL 2.1), Sobic.005G053800 (Meta-QTL 5.1), Sobic.005G062600 (Meta-QTL 5.1), Sobic.005G075100 (Meta-QTL 5.1) and Sobic.007G027100 (Meta-QTL 7.3), revealed that they were significantly upregulated in several tissues during different stress conditions. These findings provide novel insights into the molecular responses to agronomic traits, biotic and abiotic stresses, significantly advancing our understanding towards enhanced genetic mapping and marker-assisted selection for improvement in sorghum.