Detection of Favorable QTL Alleles and Candidate Genes for Lint Percentage by GWAS in Chinese Upland Cotton

Abstract

Improving cotton yield is a major breeding goal for Chinese upland cotton. Lint percentage is an important yield component and a critical economic index for cotton cultivars, and raising the lint percentage has a close relationship to improving cotton lint yield. To investigate the genetic architecture of lint percentage, a diversity panel consisting of 355 upland cotton accessions was grown, and the lint percentage was measured in four different environments. Genotyping was performed with specific-locus amplified fragment sequencing (SLAF-seq). Twelve single-nucleotide polymorphisms (SNPs) associated with lint percentage were detected via a genome-wide association study (GWAS), in which five SNP loci distributed on chromosomes At3 (A02) and At4 (A08) and contained two major-effect QTLs, which were detected in the best linear unbiased predictions (BLUPs) and in more than three environments simultaneously. Furthermore, favorable haplotypes (FHs) of two major-effect QTLs and 47 putative candidate genes in the two linkage disequilibrium (LD) blocks of these associated loci were identified. The expression levels of these putative candidate genes were estimated using RNA-seq data from ten upland cotton tissues. We found that Gh_A02G1268 was very highly expressed during the early fiber development stage, whereas the gene was poorly expressed in the seed. These results implied that Gh_A02G1268 may determine the lint percentage by regulating seed and fiber development. The favorable QTL alleles and candidate genes for lint percentage identified in this study will have high potential for improving lint yield in future Chinese cotton breeding programs.

Genome-Wide Association Study for Pre-harvest Sprouting Resistance in a Large Germplasm Collection of Chinese Wheat Landraces

“…These markers satisfied the threshold for significance applied in this study; a –log 10 P value > 6.55 [-log 10 (0.01/178803)] (Su et al, 2016) and were detected in at least two test environments. The chromosome position of these 10 highly associated markers revealed three main QTL regions that potentially contain PHS resistance genes ( Figure 3A , Table 2 , and Supplementary Table S5).…”

“…GWAS sets 3 and 4 were conducted to explore the possibility of detecting QTL specific to the white- and red-grained germaplsm pools, termed grain colour releated QTL (GCR-QTL). The threshold for significant marker-trait associations was set at -log 10 (0.01/ n , where n = number of markers) for GWAS sets 1 and 2, and –log 10 (0.001/n, where n = number of markers) for GWAS sets 3 and 4, which roughly equates to a Bonferroni correction (Weisstein, 2004; Su et al, 2016). Manhattan plots were generated using the qqman R package (Turner, 2014) in R i386 3.0.3 (R Core Team, 2014).…”

Genotyping by sequencing of 270 Indica rice varieties revealed genetic markers probably related to heavy metal accumulation

“…Association study between Cd and other heavy metal accumulation and SNPs was carried out using the EMMAX software. The significant p threshold value was 6.28E–07 ( p = 0.05/ n , where n = the number of markers used; −log10 (0.05/79,545) = 6.2) (Su et al., ).…”

Genetic Mapping in Cotton

“…12 QTLs connected to lint percentage were observed at whole genome level in upland cotton [126]. They also assumed that QTL Gh_A02G1268 was also found in fiber development and these QTLs can be used for fiber improvement at whole genome level.…”