The Plant Genome (Mar 2018)

Genetic Analysis of Gossypium Fiber Quality Traits in Reciprocal Advanced Backcross Populations

  • Rahul Chandnani,
  • Changsoo Kim,
  • Hui Guo,
  • Tariq Shehzad,
  • Jason G. Wallace,
  • Daohua He,
  • Zhengsheng Zhang,
  • Jinesh D. Patel,
  • Jeevan Adhikari,
  • Sameer Khanal,
  • Andrew H. Paterson

DOI
https://doi.org/10.3835/plantgenome2017.06.0057
Journal volume & issue
Vol. 11, no. 1

Abstract

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In mapping populations segregating for many loci, the large amount of variation among genotypes often masks small-effect quantitative trait loci (QTL). This problem can be reduced by development of populations with fewer chromosome segments segregating. Here, we report early QTL detection in reciprocal advanced backcross populations from crosses between elite Gossypium hirsutum L. ‘Acala Maxxa’ (GH) and G. barbadense L. ‘Pima S6’ (GB). A total of 297 BCF and BCF progeny rows—127 segregating for GB chromosome segments in GH background and 170 segregating for GH chromosome segments in GB background—were evaluated in three environments. Totals of 3186 and 3026 polymorphic single-nucleotide polymorphisms (SNPs) in GH and GB backgrounds, respectively, were identified and used for trait mapping. Small-effect QTL (<10% variance explained) made up 87 and 100% of QTL in GH and GB backgrounds, respectively. In both species, favorable alleles were found with effects being masked or neutralized by unfavorable alleles, with greater scope for improvement of GH than GB by introgressive breeding. A total of three stable QTL—two in GH background for fiber elongation (ELO) and micronaire (MIC) and one in GB background for upper-half mean length (UHM)—were identified in two out of three environments. Curiously, only four QTL—three for UHM and one for ELO—showed the expected opposite effects in reciprocal backgrounds, perhaps reflecting the combined consequences of epistasis, small phenotypic effects, and low coverage of some genomic regions. Along with new information for marker-assisted breeding, this study adds to knowledge that can be used to unravel complex genetic networks governing fiber quality traits.