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Abstract
Identification of stable and robust QTLs are prerequisites for marker-assisted selection in crop improvement. Here, we used high density linkage maps for genetic dissection of quantitative variation in biomass sorghum and EMS induced cotton mutants using multiple populations. In sorghum, we analyzed six F2:3 populations, each sharing a common maternal parent, to dissect the genetic architecture of key traits, including tiller number, plant height, stem diameter, biomass yield, flowering time, seed head maturity, and panicle traits. A total of 114 significant QTLs were detected. Notably, 60 high-confidence meta-QTLs (MQTLs) were identified through integrative analysis, revealing co-localized loci influencing multiple traits and highlighting regions of evolutionary and breeding importance. We also identified several heterotic QTLs displaying strong over-dominant effects, underscoring the role of heterosis and the potential for hybrid breeding in biomass sorghum improvement.In cotton, three F₂ populations derived from crosses between EMS-induced naked mutants and fuzzy G. barbadense lines were evaluated to identify QTLs underlying the naked seed phenotype. The segregation pattern indicated a dominant effect of the mutant alleles, as the naked seed phenotype was observed in the F1 generation. A major QTL, qNST18.1, on chromosome 18 (D13) was consistently identified across all populations, and displayed strong overdominance, with heterozygotes exhibiting lower fuzz scores than homozygotes. In addition, population-specific QTLs, qNST22.1 on chromosome 22 (D04) and qNST26.1 on chromosome 26 (D12) were also identified, likely reflecting fuzz suppressing alleles in G. barbadense background or population-dependent modifier loci. All three QTLs were further validated through joint linkage mapping, which also revealed an additional QTL on chromosome 1 (A01), highlighting the increased power of this approach to detect QTLs not consistently observed in individual populations.
Together, these studies highlight the effectiveness of combining multiple mapping populations and integrative approaches, such as MQTL and joint linkage analyses, in resolving complex trait architectures. The identified QTLs and associated markers offer valuable tools for marker-assisted selection and genomic prediction, paving the way for developing high-yielding bioenergy sorghum and fuzzless cotton varieties.