A genome‐wide association study of early‐maturation traits in upland cotton based on the CottonSNP80K array

Abstract

Genome-wide association studies (GWASs) efficiently identify genetic loci controlling traits at a relatively high resolution. In this study, variations in major early-maturation traits, including seedling period (SP), bud period (BP), flower and boll period (FBP), and growth period (GP), of 169 upland cotton accessions were investigated, and a GWAS of early maturation was performed based on a CottonSNP80K array. A total of 49,650 high-quality single-nucleotide polymorphisms (SNPs) were screened, and 29 significant SNPs located on chromosomes A6, A7, A8, D1, D2, and D9, were repeatedly identified as associated with early-maturation traits, in at least two environments or two algorithms. Of these 29 significant SNPs, 1, 12, 11, and 5 were related to SP, BP, FBP, and GP, respectively. Six peak SNPs, TM47967, TM13732, TM20937, TM28428, TM50283, and TM72552, exhibited phenotypic contributions of approximately 10%, which could allow them to be used for marker-assisted selection. One of these, TM72552, as well as four other SNPs, TM72554, TM72555, TM72558, and TM72559, corresponded to the quantitative trait loci previously reported. In total, 274 candidate genes were identified from the genome sequences of upland cotton and were categorized based on their functional annotations. Finally, our studies identified Gh_D01G0340 and Gh_D01G0341 as potential candidate genes for improving cotton early maturity.

Dissecting the genetic architecture of seed-cotton and lint yields in Upland cotton using genome-wide association mapping

“…In our previous study, the average LD decay distance of our population for the AD genome was estimated to be ~400 kb, where r 2 dropped to half of the maximum value (Li et al 2018a). Because of the LD decay distance and data from other publications, such as Sun et al (2017) and Su et al (2016), which assumed that the regions of SNP-associated genes for target traits were 200 kb and 1 Mb, respectively, we did a statistical analysis to identify genes located within 400 kb (200 kb upstream and downstream) of significant trait-associated SNPs.…”

“…The association mapping panel consisted of 169 Upland cotton backbone cultivars (lines). Among them, 159 cultivars were selected from major cotton regions in China (Yellow River, Yangtze River, Northwestern China, and Northern China), and 10 were introduced from abroad (Li et al 2018a). All the accessions were legally planted in the ecological cotton-growing areas of the Yellow River (Xinxiang City, Henan Province) (113°52′ E, 35°18′ N, 95 m above sea level) and Northwestern China (Shihezi City, Xinjiang Province) (85°56′, 44°16′ N, 442 m above sea level) in 2012 and 2013 with two replications.…”

“…Detailed descriptions of the SNP genotyping processes were published previously (Li et al 2018a). Briefly, single-base extension was performed, and the chip was scanned using Illumina iScan (Illumina Inc., San Diego, CA, USA).…”

Role of SNPs in determining QTLs for major traits in cotton

“…A total of 49 650 SNPs were discovered using the cotton SNP 80 K array, and 29 SNPs were highly associated with early maturity. In addition, two potential candidate genes (Gh_D01G0340 and Gh_D01G0341) were also related to earliness (Li et al 2018b). Likewise, the GBS method has been used to construct a high-density genetic linkage map to discover QTLs related to this trait.…”

QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

“…However, the disadvantages of previous genetic maps, such as low marker density, asymmetric distribution of mapped markers, and unavailability of reference genomes for upland cotton, hindered the above-mentioned applications of the QTL detection results (Deschamps et al 2012;Jamshed et al 2016;Yang et al 2015). Due to the rapid development of highthroughput sequencing technologies, the reduction of sequencing cost, and the establishment of the reference genome of upland cotton (TM-1), a number of highdensity genetic maps have been constructed by single nucleotide polymorphism (SNP) markers including genotyping by sequencing (GBS) (Diouf et al 2018;Qi et al 2017), restriction-site associated DNA sequencing (RAD-Seq) (Hegarty et al 2013;Kundu et al 2015;Wang et al 2017), specific locus-amplified fragment sequencing (SLAF-seq) (Ali et al 2018;Zhang et al 2016), CottonSNP63K array (Hulse-Kemp et al 2015;Li et al 2016;Li et al 2018a;Zhang et al 2016), and Cot-tonSNP80K array (Cai et al 2017;Tan et al 2018;Liu et al 2018;Zou et al 2018). These high-density genetic maps significantly improved QTL detection accuracy (Ma et al 2019a;Su et al 2018;Jia et al 2016).…”