Genome-wide association study reveals novel quantitative trait loci and candidate genes of lint percentage in upland cotton based on the CottonSNP80K array

生物 候选基因 数量性状位点 皮棉 单核苷酸多态性 全基因组关联研究 遗传学 连锁不平衡 基因 遗传关联 基因型 农学
作者
Yu Chen,Yang Gao,Pengyun Chen,Juan Zhou,Chuanyun Zhang,Zhangqiang Song,Xuehan Huo,Zhaohai Du,Jǔwǔ Gōng,Chengjie Zhao,Shengli Wang,Jingxia Zhang,Furong Wang,Jun Zhang
出处
期刊:Theoretical and Applied Genetics [Springer Science+Business Media]
卷期号:135 (7): 2279-2295 被引量:19
标识
DOI:10.1007/s00122-022-04111-1
摘要

Thirty-four SNPs corresponding with 22 QTLs for lint percentage, including 13 novel QTLs, was detected via GWAS. Two candidate genes underlying this trait were also identified. Cotton (Gossypium spp.) is an important natural textile fiber and oilseed crop cultivated worldwide. Lint percentage (LP, %) is one of the important yield components, and increasing LP is a core goal of cotton breeding improvement. However, the genetic and molecular mechanisms underlying LP in upland cotton remain unclear. Here, we performed a genome-wide association study (GWAS) for LP based on 254 upland cotton accessions in four environments as well as the best linear unbiased predictors using the high-density CottonSNP80K array. In total, 41,413 high-quality single-nucleotide polymorphisms (SNPs) were screened, and 34 SNPs within 22 quantitative trait loci (QTLs) were significantly associated with LP. In total, 175 candidate genes were identified from two major genomic loci (GR1 and GR2), and 50 hub genes were identified through GO enrichment and weighted gene co-expression network analysis. Two candidate genes (Gh_D01G0162 and Gh_D07G0463), which may participate in early fiber development to affect the number of fiber protrusions and LP, were also identified. Their genetic variation and expression were verified by linkage disequilibrium blocks, haplotypes, and quantitative real-time polymerase chain reaction, respectively. The weighted gene interaction network analysis showed that the expression of Gh_D07G0463 was significantly correlated with that of Gh_D01G0162. These identified SNPs, QTLs and candidate genes provide important insights into the genetic and molecular mechanisms underlying variations in LP and serve as a foundation for LP improvement via marker-assisted breeding.
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