QTL Mapping of Cotton Fiber Quality Traits in Reciprocal Advanced Backcross Populations

Cotton is the world’s foremost textile fiber crop, and premium fiber is determined by quality parameters such as length, strength, elongation, micronaire and uniformity index. Fiber quality traits are complex, governed by many small and large effect QTLs. Mapping populations segregating for many loci often mask small effect QTL. This problem can be solved by development of populations with fewer chromosome segments segregating. Here, we report QTL detection in reciprocal advanced backcross populations between elite Gossypium hirsutum cultivar ‘Acala Maxxa’ and Gossypium barbadense cultivar ‘Pima S6’. A total of 297 BC₄F₁ and BC₄F₂ progeny rows, 127 in Gossypium hirsutum and 170 in Gossypium barbadense background were evaluated in three environments. Using a genotyping by sequencing approach, 3186 polymorphic SNPs in Gossypium hirsutum background and 3026 in Gossypium barbadense background were used for genotyping. In total, we identified 53 QTLs in Gossypium hirsutum background and 50 QTLs in Gossypium barbadense background. Proportions of 87% and 100% of QTL were of ‘small effect’ (PV%<10%) in Gossypium hirsutum and Gossypium barbadense backgrounds, respectively. Despite Gossypium barbadense being the parent with superior fiber quality, 32% of QTLs identified in this background had favorable alleles transferred from Gossypium hirsutum. A total of 3 stable QTLs, 2 in Gossypium hirsutum background, each for ELO and MIC; and one in Gossypium barbadense background for UHM were identified in 2 out of 3 environments. One QTL for UHM was identified on chromosome D08 with opposite effects in reciprocal background and with significant genotype by background interaction. Slightly more QTLs were contributed by the Dt than the At subgenome in Gossypium hirsutum background, whereas there was no difference in Gossypium barbadense. Along with stable QTLs for marker assisted breeding for fiber quality traits, this study adds to knowledge that can be utilized to understand complex genetic networks governing fiber quality in these cultivated species.