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Codon usage bias (CUB) is an important evolutionary feature in a genome which provides important information for studying organism evolution, gene function and exogenous gene expression. The CUB and its shaping factors in the nuclear genomes of four sequenced cotton species, G. arboreum (A2), G. raimondii (D5), G. hirsutum (AD1) and G. barbadense (AD2) were analyzed in the present study. The effective number of codons (ENC) analysis showed the CUB was weak in these four species and the four subgenomes of the two tetraploids. Codon composition analysis revealed these four species preferred to use pyrimidine-rich codons more frequently than purine-rich codons. Correlation analysis indicated that the base content at the third position of codons affect the degree of codon preference. PR2-bias plot and ENC-plot analyses revealed that the CUB patterns in these genomes and subgenomes were influenced by combined effects of translational selection, directional mutation and other factors. The translational selection (P2) analysis results, together with the non-significant correlation between GC12 and GC3, further revealed that translational selection played the dominant role over mutation pressure in the codon usage bias. Through relative synonymous codon usage (RSCU) analysis, we detected 25 high frequency codons preferred to end with T or A, and 31 low frequency codons inclined to end with C or G in these four species and four subgenomes. Finally, 19 to 26 optimal codons with 19 common ones were determined for each species and subgenomes, which preferred to end with A or T. We concluded that the codon usage bias was weak and the translation selection was the main shaping factor in nuclear genes of these four cotton genomes and four subgenomes.

Cotton (Gossypium spp.) is a leading natural fiber crop and an important source of vegetable protein and oil for humans and livestock. To investigate the genetic architecture of seed nutrients in upland cotton, a genome-wide association study (GWAS) was conducted in a panel of 196 germplasm resources under three environments using a CottonSNP80K chip of 77,774 loci. Relatively high genetic diversity (average gene diversity being 0.331) and phenotypic variation (coefficient of variation, CV, exceeding 3.9%) were detected in this panel. Correlation analysis revealed that the well-documented negative association between seed protein (PR) and oil may be to some extent attributable to the negative correlation between oleic acid (OA) and PR. Linkage disequilibrium (LD) was unevenly distributed among chromosomes and subgenomes. It ranged from 0.10–0.20 Mb (Chr19) to 5.65–5.75 Mb (Chr25) among the chromosomes and the range of Dt-subgenomes LD decay distances was smaller than At-subgenomes. This panel was divided into two subpopulations based on the information of 41,815 polymorphic single-nucleotide polymorphism (SNP) markers. The mixed linear model considering both Q-matrix and K-matrix [MLM(Q+K)] was employed to estimate the association between the SNP markers and the seed nutrients, considering the false positives caused by population structure and the kinship. A total of 47 SNP markers and 28 candidate quantitative trait loci (QTLs) regions were found to be significantly associated with seven cottonseed nutrients, including protein, total fatty acid, and five main fatty acid compositions. In addition, the candidate genes in these regions were analyzed, which included three genes, Gh_D12G1161, Gh_D12G11 62, and Gh_D12G1165 that were most likely involved in the control of cottonseed protein concentration. These results improved our understanding of the genetic control of cottonseed nutrients and provided potential molecular tools to develop cultivars with high protein and improved fatty acid compositions in cotton breeding programs through marker-assisted selection. [ABSTRACT FROM AUTHOR]

• Single-locus and multi-locus models were employed for association mapping accuracy. • Fiber quality traits in six environments were used for the stable loci detection. • Promising genes were identified using the gene expression in fiber development. • Compared to previous studies, 23 stable QTL and 71 stable genes were identified in this study. • Common genes associated with both fiber quality and cottonseed nutritional traits were detected. Upland cotton makes an important contribution to natural fiber production worldwide. Given the necessity to high quality fiber required in the textile industry, the genomic variation of diverse accessions and the loci underpinning fiber quality should be extensively investigated. Here, a panel of 196 upland cottons and 41,815 single-nucleotide polymorphism (SNP) markers were examined for detection of candidate quantitative trait loci (QTL) for five fiber quality traits in six environments using both single- and multi-locus genome-wide association studies (GWAS) models. With discriminant analysis of principal components (DAPC), the accessions were divided into four subpopulations, and among these, 40 SNPs and 38 QTL were significant in at least three environments and at least two models. Reference to the TM-1 genome and related gene expression data, 89 candidate genes preferentially expressed in fiber or ovules were identified. In this study, 23 candidate QTL and 71 candidate genes were also identified in previous studies, and of them, five promising QTL and 13 promising genes may have pleiotropic effects. In addition, nine putative genes were identified with quantitative real-time PCR. Furthermore, 17 common genes between fiber quality traits and cottonseed nutritional traits were identified. All of the SNP markers and promising genes detected in this study could be used in future breeding practices and for putative gene functional studies. [ABSTRACT FROM AUTHOR]