Fiber Quality Improvement in Upland Cotton (Gossypium hirsutum L.): Quantitative Trait Loci Mapping and Marker Assisted Selection Application

Abstract

Genetic improvement in fiber quality is one of the main challenges for cotton breeders. Fiber quality traits are controlled by multiple genes and are classified as complex quantitative traits, with a negative relationship with yield potential, so the genetic gain is low in traditional genetic improvement by phenotypic selection. The availability of Gossypium genomic sequences facilitates the development of high-throughput molecular markers, quantitative trait loci (QTL) fine mapping and gene identification, which helps us to validate candidate genes and to use marker assisted selection (MAS) on fiber quality in breeding programs. Based on developments of high density linkage maps, QTLs fine mapping, marker selection and omics, we have performed trait dissection on fiber quality traits in diverse populations of upland cotton. QTL mapping combined with multi-omics approaches such as, RNA sequencing datasets to identify differentially expressed genes have benefited the improvement of fiber quality. In this review, we discuss the application of molecular markers, QTL mapping and MAS for fiber quality improvement in upland cotton.

QTL and genetic analysis controlling fiber quality traits using paternal backcross population in upland cotton

“…Generally, main fiber quality traits consist of fiber length (FL), fiber uniformity (FU), fiber strength (FS), fiber elongation (FE), and fiber micronaire (FM). Each trait has its own genetic mechanism (Ijaz et al 2019).…”

“…A number of QTLs were located on Chr 5, Chr 19 and Chr 21 (Said et al 2013(Said et al , 2015. For fiber quality, numerous QTLs were detected based on mapping in recombinant inbred lines (RIL) populations of upland cotton (Wu et al 2009;Sun et al 2012;Ning et al 2014;Tan et al 2015;Shang et al 2015;Tang et al 2015;Zhang et al 2015b;Jamshed et al 2016;Li et al 2016;Ma et al 2017;Ijaz et al 2019). However, RIL population can only be dissected additive and additive × additive effects, not to be dissected dominance and dominance-related genetic effects due to lacking heterozygous genotypes.…”

Identification of Candidate Genes Controlling Fiber Quality Traits in Upland Cotton Through Integration of Meta-QTL, Significant SNP and Transcriptomic Data

“…The meta-QTL contributes to cotton ber quality improvement by MAS MAS needs to be enabled through the identi cation of robust QTL, the design of reliable marker systems to select for these QTL, and the delivery of these QTL into elite genomic backgrounds to enable their use without associated genetic drag (Cobb et . QTL mapping with molecular markers provides a powerful approach to dissect the molecular mechanism underlying complex ber quality traits (Ijaz et al 2019). There were thousands of QTL for cotton ber quality traits identi ed in different mapping populations such as RILs, bi-parental segregating populations, and BC populations (Said et al 2015b), which provide the potential to be manipulated by MAS for the improvement of cotton ber quality traits.…”

Identification of candidate genes controlling fiber quality traits in upland cotton through integration of meta-QTL, significant SNP and transcriptomic data

“…Marker-assisted selection (MAS) has been successfully applied in genetic improvement of varieties of crops, especially for the major QTLs/genes, such as improvement of rice blast resistance by pyramiding three genes in rice (Xiao et al 2019), improvement of drought adaptation in maize (Ribaut and Ragot 2007), yield traits in soybean (Reyna and Sneller 2001;Sebastian et al 2010), Fusarium head blight resistance in wheat (Anderson 2007), and Verticillium wilt resistance in cotton (Zhang et al 2014). QTL mapping with molecular markers provides a powerful approach to dissect the molecular mechanism underlying complex fiber quality traits (Ijaz et al 2019). There were thousands of QTLs for cotton fiber quality traits identified in different mapping populations such as RILs, bi-parental segregating populations, and backcross populations (Said et al 2015b), which provide the potential to be manipulated by MAS for the improvement of cotton fiber quality traits.…”