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

Abstract

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 BC 4 F 1 and BC 4 F 2 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.

Validation of QTLs for Fiber Quality Introgressed from Gossypium mustelinum by Selective Genotyping

“…Upland cotton is faced with a low level of genetic diversity as a result of its evolutionary history, domestication, and modern plant breeding, imposing strong selection pressure on only a small number of agronomic and fiber quality traits ( Chandnani et al 2018 ). A narrow range of phenotypic variation in fiber traits suggests that only a small number of fiber quality genes remain to be recruited for future improvement of the elite cotton gene pool.…”

Identification of candidate genes controlling fiber quality traits in upland cotton through integration of meta-QTL, significant SNP and transcriptomic data

“…Eight thousand five hundred and eighty-nine significant SNP loci associated to cotton fiber quality traits were collected from 15 GWAS studies and mapped to TM-1 genome (Chandnani et al 2018;Fang et al 2017;Gapare et al 2017;Handi et al 2017;Huang et al 2017;Islam et al 2016;Li et al 2017bLi et al , 2018aLiu et al 2018b;Ma et al 2018a, b;Su et al 2016Su et al , 2018Sun et al 2017;Wen et al 2018) (Table S1, Table S2). Then they were mapped to the 74 meta-QTLs, and the mapped SNPs are shown in Table S3.…”

“…In this study, 884 QTLs associated with cotton fiber traits from 12 studies (Ali et al 2018;Diouf et al 2018;Huang et al 2017;Jia et al 2018;Keerio et al 2018;Li et al 2016a;Liu et al 2018b;Ma et al 2018a;Tan et al 2018;Wang et al 2015;Zhang et al 2015b;Zou et al 2014) were used for meta-QTL analysis based on upland cotton reference genome TM-1 (Zhang et al 2015a), and 74 meta-QTLs were identified. Combined with 8 589 significant SNP loci associated to cotton fiber quality traits collected from 15 previous publications (Chandnani et al 2018;Fang et al 2017;Gapare et al 2017;Handi et al 2017;Huang et al 2017;Islam et al 2016;Li et al 2017bLi et al , 2018aLiu et al 2018b;Ma et al 2018a, b;Su et al 2016Su et al , 2018Sun et al 2017;Wen et al 2018), 297 candidate genes associated with cotton fiber quality traits were identified. Twenty genes showed high expression levels specifically in the developing fibers, some of which are associated with the fiber development.…”

Identification of Candidate Genes Controlling Fiber Quality Traits in Upland Cotton through Integration of Meta-QTL, Significant SNP and Transcriptomic Data

“…Candidate genes identi cation combined and meta-QTL intervals and signi cant SNPs 8,589 signi cant SNP loci associated to cotton ber quality traits were collected from 15 GWAS studies and mapped to the TM-1 genome (Chandnani et al 2018;Fang et al 2017;Gapare et al 2017;Handi et al 2017;Huang et al 2017;Islam et al 2016;Li et al 2017b;Liu et al 2018b;Ma et al 2018a;Ma et al 2018b;Su et al 2016;Su et al 2018;Sun et al 2017;Wen et al 2018) (Table S3, Table S4, Fig. 2), 4343 of which were mapped in the 74 meta-QTL regions (Table S5).…”

“…In this study, 884 QTLs associated with cotton ber traits from 12 studies (Ali et al 2018;Diouf et al 2018;Huang et al 2017;Jia et al 2018;Keerio et al 2018;Li et al 2016a;Liu et al 2018b;Ma et al 2018a;Tan et al 2018;Wang et al 2015;Zou et al 2018) were used for meta-QTL analysis based on upland cotton reference genome TM-1 (Zhang et al 2015a), and 74 meta-QTLs were identi ed. Combined with 8589 signi cant SNP loci associated to cotton ber quality traits collected from 15 previous publications (Chandnani et al 2018;Fang et al 2017;Gapare et al 2017;Handi et al 2017;Huang et al 2017;Islam et al 2016;Li et al 2017b;Liu et al 2018b;Ma et al 2018a;Ma et al 2018b;Su et al 2016;Su et al 2018;Sun et al 2017;Wen et al 2018), 297 candidate genes associated with cotton ber quality traits were identi ed. Twenty genes showed high expression speci cally in the developing bers, some of which are associated with the ber development.…”