Fiber quality is an important economic index and a major breeding goal in cotton, but direct phenotypic selection is often hindered due to environmental influences and linkage with yield traits. A genome-wide association study (GWAS) is a powerful tool to identify genes associated with phenotypic traits. In this study, we identified fiber quality genes in upland cotton (Gossypium hirsutum L.) using GWAS based on a high-density CottonSNP80K array and multiple environment tests. A total of 30 and 23 significant single nucleotide polymorphisms (SNPs) associated with five fiber quality traits were identified across the 408 cotton accessions in six environments and the best linear unbiased predictions, respectively. Among these SNPs, seven loci were the same, and 128 candidate genes were predicted in a 1-Mb region (±500 kb of the peak SNP). Furthermore, two major genome regions (GR1 and GR2) associated with multiple fiber qualities in multiple environments on chromosomes A07 and A13 were identified, and within them, 22 candidate genes were annotated. Of these, 11 genes were expressed [log2(1 + FPKM)>1] in the fiber development stages (5, 10, 20, and 25 dpa) using RNA-Seq. This study provides fundamental insight relevant to identification of genes associated with fiber quality and will accelerate future efforts toward improving fiber quality of upland cotton.

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Association mapping based on linkage disequilibrium provides a promising tool for dissecting the genetic basis underlying complex traits. To reveal the genetic variations of yield and yield components traits in upland cotton, 403 upland cotton accessions were collected and analyzed by 560 genome-wide simple sequence repeats (SSRs). A diverse panel consisting of 403 upland cotton accessions was grown in six different environments, and the yield and yield component traits were measured, and 560 SSR markers covering the whole genome were mapped. Association studies were performed to uncover the genotypic and phenotypic variations using a mixed linear model. Favorable alleles and typical accessions for yield traits were identified. A total of 201 markers were polymorphic, revealing 394 alleles. The average gene diversity and polymorphism information content were 0.556 and 0.483, respectively. Based on a population structure analysis, 403 accessions were divided into two subgroups. A mixed linear model analysis of the association mapping detected 43 marker loci according to the best linear unbiased prediction and in at least three of the six environments(- lgP > 1.30, P < 0.05). Among the 43 associated markers, five were associated with more than two traits simultaneously and nine were coincident with those identified previously. Based on phenotypic effects, favorable alleles and typical accessions that contained the elite allele loci related to yield traits were identified and are widely used in practical breeding. This study detected favorable quantitative trait loci's alleles and typical accessions for yield traits, these are excellent genetic resources for future high-yield breeding by marker-assisted selection in upland cotton in China.