Publication Details

AFRICAN RESEARCH NEXUS

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agricultural and biological sciences

QTL analysis in multiple sorghum mapping populations facilitates dissection of the genetic control of agronomic and yield-related traits in sorghum [Sorghum bicolor (Moench)]

Euphytica, Volume 218, No. 3, Article 24, Year 2022

The genetic architectures of agronomic and yield-related traits are expected to involve multiple loci that are unlikely all to segregate for alternative alleles in a single biparental population. Therefore, the identification of quantitative trait loci (QTL) that are expressed in diverse genetic backgrounds of multiple bi-parental populations provides evidence about both background-specific and common genetic variants. The purpose of this study was to map QTLs for agronomic and yield related traits using three connected mapping populations of different genetic backgrounds, to gain insight into the genomic landscape of these important traits in elite Ethiopian sorghum germplasm. The three bi-parental populations, each with 207 F 2:3 lines were evaluated using an alpha lattice design with two replications under two moisture stress environments. Data analysis was done separately for each population using composite interval mapping, finding a total of 105 QTLs. All the QTLs identified from individual populations were projected on a combined consensus map, comprising a total of 25 meta QTLs for seven traits. The consensus map allowed us to deduce locations of a larger number of markers than possible in any individual map, providing a reference for genetic studies in different genetic backgrounds. The meta QTLs identified in this study could be used for marker-assisted breeding programs in sorghum after validation. Only one trait, reduced leaf senescence, showed a striking bias of allele distribution, indicating substantial standing variation among the lines that might be employed in improving drought tolerance of sorghum.

Statistics
Citations: 6
Authors: 6
Affiliations: 5
Identifiers
Research Areas
Genetics And Genomics
Study Design
Cross Sectional Study
Study Approach
Quantitative