GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers

Abstract

It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F6:8 recombinant inbred lines (RILs), derived from an intraspecific cross between Xinluzao24, a cultivar with elite fiber quality, and Lumianyan28, a cultivar with wide adaptability and high yield potential, were measured in nine environments. This RIL population was genotyped by 122 SSR and 4729 SNP markers, which were also used to construct the genetic map. The map covered 2477.99 cM of hirsutum genome, with an average marker interval of 0.51 cM between adjacent markers. As a result, a total of 134 QTLs for fiber quality traits and 122 QTLs for yield components were detected, with 2.18–24.45 and 1.68–28.27% proportions of the phenotypic variance explained by each QTL, respectively. Among these QTLs, 57 were detected in at least two environments, named stable QTLs. A total of 209 and 139 quantitative trait nucleotides (QTNs) were associated with fiber quality traits and yield components by four multilocus genome-wide association studies methods, respectively. Among these QTNs, 74 were detected by at least two algorithms or in two environments. The candidate genes harbored by 57 stable QTLs were compared with the ones associated with QTN, and 35 common candidate genes were found. Among these common candidate genes, four were possibly “pleiotropic.” This study provided important information for MAS and candidate gene functional studies.

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

“…In this study, QTLs for PH and FBN were detected, based on the phenotypic evaluations of an intraspecific RIL population of upland cotton across six environments in three locations from 2013 to 2016 and the previously reported (Liu et al 2018) genetic linkage map of that population. A total of 27 QTLs for PH and 26 QTLs for FBN were identified, in which six for PH and two for FBN were stable QTLs, and seven QTLs formed three QTL clusters.…”

“…A segregation population consisting of 231 F 6:8 RIL individuals was developed from an intra-specific cross of G. hirsutum between two homozygous cultivars Lumian-yan28 (LMY28) and Xinluzao24 (XLZ24). The attributes of the two parental lines and the development procedures of the population were previously described (Liu et al 2018). Briefly, the cross was made in an experimental farm at the Institute of Cotton Research of Chinese Academy of Agricultural Sciences in Anyang in 2008.…”

“…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).…”

“…This study was based on a previously constructed high-density genetic map through chip-SNP genotyping (cottonSNP80K array) (Cai et al, 2017;Liu et al, 2018). The field phenotypes of PH and FBN were evaluated and analyzed across multiple environments, and their QTLs were detected.…”

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

“…In recent years, a large number of multivariate GWAS methods have been developed, including MLMM (multi-locus mixed-model) [26], FarmCPU (Fixed and random model Circulating Probability Unification) [27], mrMLM (multi-locus random-SNP-effect MLM) [28], FASTmrMLM (fast mrMLM) [29], FASTmrEMMA (fast multi-locus random-SNP-effect efficient mixed model analysis) [30], pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes) [31], 6 pKWmEB (integration of Kruskal-Wallis test with empirical Bayes) [32], ISIS EM-BLASSO (iterative modified-sure independence screening expectation-maximization-Bayesian least absolute shrinkage and selection operator) [33], and GPWAS (Genome-Phenome Wide Association Study) [34]. The MLMM [26] [35], rice [36,37], foxtail millet [38], soybean [39,40], maize [41,42], and wheat [43,44].…”