Genome‐wide quantitative trait loci reveal the genetic basis of cotton fibre quality and yield‐related traits in a Gossypium hirsutum recombinant inbred line population

Abstract

SummaryCotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty‐seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA‐Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu‐chr13‐2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.

Disequilibrium evolution of Fructose-1,6-bisphosphatase gene family leads to their functional biodiversity in Gossypium species

“…Cotton fiber is developed from the differentiation of a single ectodermic epidermal cell, and the fiber formation process can be divided into four distinct but partially overlapping periods: initiation, elongation (primary wall formation), secondary wall thickening, and dehydration maturity [19]. Many methods, including QTL identification [20][21][22], GWAS analysis [23][24][25][26], and functional gene identification [27][28][29], have been used to tackle the problems of fiber development and fiber quality formation. Studies have revealed that fiber development is a very complex process, with a large number of metabolic pathways providing material support, and thousands of specific genes being involved in expression regulation.…”

Identification and analysis of oil candidate genes revealed the molecular basis of oil accumulation in Gossypium hirsutum L. for cottonseed biofuels development

“…This study developed an intra-speci c RIL population in upland cotton for QTL mapping, which consisting of 196 lines with the parents 0-153 and sGK9708. The consensus genetic map was constructed by Zhang et al [41] in previous study, which covered the whole genome of upland cotton with a high saturation and was a valuable tool for QTL mapping across the whole genome. 39 QTLs for cottonseed oil content were identi ed in the study.…”

“…The consensus high-density genetic map used in this study was constructed with three types of markers (8295 markers, 5197.17 cM) by Zhang et al [41]. We could determine the physical positions of linkage groups using the map, which could supply powerful information for subsequently selecting candidate genes.…”

A Statistical Framework for QTL Hotspot Detection

“…These regions enriched in QTL are referred to as QTL hotspots, and, statistically, they harbor a significantly higher number of QTL than expected by random chance. It has been noted that the phenomenon of QTL hotspots may have several causes, such as: QTL with large and consistent effects can be identified in the similar regions under different conditions in various studies; QTL with high allelic polymorphisms have a greater chance of being detected in different crosses and environments; pleiotropic or closely linked QTL that control correlated traits are frequently co-localized in the same regions in different experiments (Falconer and Mackay 1996;Zhao et al 2011;Vuong et al 2015;Mengistu et al 2016;Zhang et al 2019). As the QTL hotspots can lead to identifying genes that affect the traits of interest, and further help to build networks among QTL hotspots, genes and traits, the QTL hotspot detection analysis at genome-wide level has been a key step towards deciphering the genetic architectures of quantitative traits in genes, genomes and genetics studies (Breitling et al 2008;Fu et al 2009;Neto et al 2012;Wang et al 2014;Yang et al 2019).…”