An RTM-GWAS procedure reveals the QTL alleles and candidate genes for three yield-related traits in upland cotton

Abstract

Background Cotton (Gossypium spp.) fiber yield is one of the key target traits, and improved fiber yield has always been thought of as an important objective in the breeding programs and production. Although some studies had been reported for the understanding of genetic bases for cotton yield-related traits, the detected quantitative trait loci (QTL) for the traits is still very limited. To uncover the whole-genome QTL controlling three yield-related traits in upland cotton (Gossypium hirsutum L.), phenotypic traits were investigated under four planting environments and 9244 single-nucleotide polymorphism linkage disequilibrium block (SNPLDB) markers were developed in an association panel consisting of 315 accessions. Results A total of 53, 70 and 68 significant SNPLDB loci associated with boll number (BN), boll weight (BW) and lint percentage (LP), were respectively detected through a restricted two-stage multi-locus multi-allele genome-wide association study (RTM-GWAS) procedure in multiple environments. The haplotype/allele effects of the significant SNPLDB loci were estimated and the QTL-allele matrices were organized for offering the abbreviated genetic composition of the population. Among the significant SNPLDB loci, six of them were simultaneously identified in two or more single planting environments and were thought of as the stable SNPLDB loci. Additionally, a total of 115 genes were annotated in the nearby regions of the six stable SNPLDB loci, and 16 common potential candidate genes controlling target traits of them were predicted by two RNA-seq data. One of 16 genes (GH_D06G2161) was mainly expressed in the early ovule-development stages, and the stable SNPLDB locus (LDB_19_62926589) was mapped in its promoter region. Conclusion This study identified the QTL alleles and candidate genes that could provide important insights into the genetic basis of yield-related traits in upland cotton and might facilitate breeding cotton varieties with high yield.

Inheritance, QTLs, and Candidate Genes of Lint Percentage in Upland Cotton

“…Different genome-wide association study (GWAS), single locus-GWAS (SL-GWAS), multi-locus GWAS (ML-GWAS), and restricted two-stage, multi-locus, multi-allele GWAS (RTM-GWAS) approaches have been used to search QTNs for LP in a large number of cotton accessions ( Supplementary Table S1 ). For example, using RTM-GWAS, 86 single-nucleotide polymorphism linkage disequilibrium block (SNPLDB) loci for LP were identified from 315 cotton accessions ( Su J et al, 2020 ). A total of 719 upland cotton accessions were screened by GWAS combined with cottonSNP63K array.…”

“…High density SNP maps have been constructed ( Sun et al, 2018 ), including more than 16 association maps ( Supplementary Table S1 ). Many candidate genes for agronomic traits have also been reported ( Fang et al, 2017 ; Huang et al, 2017 ; Sun et al, 2018 ; Shen et al, 2019 ; Su et al, 2019 ; Su J et al, 2020 ; Zhu G et al, 2020 ; Yu et al, 2021 ).…”

“…TFs modulate transcription and hormone signaling and can affect LP. For example, MYB52 is involved in ABA signaling ( Su J et al, 2020 ) and AIL6, a AP2-like ethylene-responsive TF, involved in ethylene (ET) signaling ( Liu et al, 2014 ).…”

Transcriptome Analysis of a Cotton Cultivar Provides Insights into the Differentially Expressed Genes Underlying Heightened Resistance to the Devastating Verticillium Wilt

“…Breeding of resistant varieties is the effective and economic approach to prevent and control VW [ 13 , 14 , 15 ]. To this date, several VW resistance quantitative trait loci (QTLs) have been characterized based on development of high-throughput sequencing and have been shown to contribute to defense responses against VW [ 14 , 16 , 17 ]. In addition, several genes have been identified that contribute to defense responses against VW, including transcription factors, genes involved in plant hormone signaling network and R genes [ 18 ].…”

Identification of yield-related genes through genome-wide association: case study of weeping forsythia, an emerging medicinal crop

“…Genes affecting crop yield must be identified to reveal the genetic basis and facilitate the increase in crop yield through genetic engineering and hybridization or selective breeding (Gu et al 2020). Genome-wide association study (GWAS) and quantitative trait locus (QTL) analysis are the most efficient methods for detecting yield-related genes in staple crops, such as wheat, maize, rice, and cotton (Alemu et al 2020;Su et al 2020;Wang et al 2020;Zhang et al 2020). However, the yield-related genes of emerging crops are difficult to identify by using GWAS or QTL analysis due to the lack of genomic information.…”