Linkage disequilibrium and association mapping of fibre quality traits in elite Asiatic cotton (Gossypium arboreum) germplasm populations

Abstract

Sethi K., Siwach P., Verma S.K. (2017): Linkage disequilibrium and association mapping of fibre quality traits in elite Asiatic cotton (Gossypium arboreum) germplasm populations. Czech J. Genet. Plant Breed., 53: 159−167.Cotton productivity has been hindered by the narrow genetic base of cultivated cotton. Linkage disequilibriumbased association mapping has become a powerful molecular tool to dissect and exploit genetic diversity. In the present study, population structure and marker-trait associations for fibre quality traits in genotypes belonging to six races of Gossypium arboreum were assessed. Out of 300 simple sequence repeat (SSR) markers, 100 were found polymorphic, yielding a total of 240 alleles (all polymorphic). Structure analysis revealed allelic admixtures between genotypes. A Q-matrix exhibited mixed ancestry for the majority of genotypes, the race indicum forming a significant percent ancestry for almost all genotypes. At significant threshold values of r 2 ≥ 0.05, 7.37% of SSR loci showed significant linkage disequilibrium (LD), while at highly significant threshold of r 2 ≥ 0.1, the value was reduced to 5.31%. LD clearly decayed within the genetic distance of 9-10 cM, with r 2 ≥ 0.1. Twenty-eight SSR markers were found associated with six fibre quality traits using general linear model and mixed linear model.

Identification of Novel Marker–Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs

“…Association between seed cotton yield traits and SSR markers was estimated by three different models, which are MLMM, CMLM, and MLM, so that the model that best fits the results of association mapping could be identified. The strategy of using two or more different models for detection of favorable MTAs in cotton has been practiced previously in several studies ( Badigannavar and Myers, 2015 ; Wang et al, 2016 ; Abdullaev et al, 2017 ; Baytar et al, 2017 ; Sethi et al, 2017 ), and comparative results of those models were considered. Confirmation of MTAs with different models over the different environmental conditions gave more confidence in the results and reduces the chances of false association.…”

Single-Locus and Multi-Locus Genome-Wide Association Studies in the Genetic Dissection of Fiber Quality Traits in Upland Cotton (Gossypium hirsutum L.)

“…Presently, several QTLs/markers related to cotton fiber qualities have been identified using linkage mapping and association mapping in previous studies (Shen et al, 2005 ; Abdurakhmonov et al, 2008 , 2009 ; Kantartzi and Stewart, 2008 ; An et al, 2010 ; Sun et al, 2012 , 2017 ; Wang et al, 2013 ; Zhang et al, 2013 ; Cai et al, 2014 ; Qin et al, 2015 ; Islam et al, 2016 ; Li C. et al, 2016 ; Nie et al, 2016 ; Su et al, 2016b ; Gapare et al, 2017 ; Huang et al, 2017 ; Iqbal and Rahman, 2017 ; Ma et al, 2017 ; Sethi et al, 2017 ; Tan et al, 2018 ). We compared the 342 QTNs detected in our GWAS (Supplementary Table S3 ) with SNPs and SSRs linked to/associated with QTLs for the same traits identified in previous studies by electronic PCR (e-PCR) based on their physical locations on the genome sequence (Zhang T. Z. et al, 2015 ).…”

“…Thus, 12 QTNs detected in our GWAS corresponded to previously reported SNPs and SSRs detected based on linkage and/or association mapping (Table 4 ). Specifically, two QTNs for FL, TM58426 (D5) and TM72875 (D9), corresponded to BNL4047 (Sethi et al, 2017 ) and DPL0395 (Sun et al, 2012 )/MGHES-55 (Iqbal and Rahman, 2017 ), respectively; five QTNs for FS, TM5639 (A2), TM21292 (A7), TM43422 (A13), TM63860 (D7), and TM74995 (D10), corresponded to HAU880 (Wang et al, 2013 ), i18340Gh/i44206Gh/i39753Gh/i02033Gh/i02034Gh/i02035Gh/i02037Gh/i49171Gh/i37604Gh (Sun et al, 2017 ), i30934Gh (Sun et al, 2017 ), BNL3854 (An et al, 2010 ), and TM74991 (Tan et al, 2018 ), respectively; one QTN for FM, TM52959 (D2), corresponded to NAU2353 (Sun et al, 2012 ); two QTNs for FU, TM72633 (D9) and TM74995 (D10), corresponded to MGHES-6 (Iqbal and Rahman, 2017 ) and TM74991 (Tan et al, 2018 ), respectively; five QTNs for FE, TM3939 (A2), TM56516 (D4), TM72628 (D9), TM74999 (D10), and TM80198 (D13), corresponded to BNL1434 (Kantartzi and Stewart, 2008 ; Sethi et al, 2017 ), i12839Gh (Sun et al, 2017 ), BNL1030 (Kantartzi and Stewart, 2008 ), TM74991 (Tan et al, 2018 ), and NAU2730 (Sun et al, 2012 ), respectively. The 15 QTNs controlling the fiber quality, which were simultaneously detected in different populations with different genetic backgrounds, can potentially be used in the MAS of target traits.…”

Identification of stable QTLs for fiber quality and plant structure in Upland cotton (G. hirsutum L.) under drought stress

“…Moreover, AM analysis is relatively faster than bi-parental QTL mapping since it is not necessary to breed a population for detection of marker-trait associations. Association analysis using germplasm panels has been performed in cotton for agronomic traits as well as biotic and abiotic stress tolerance (Abdurakhmonov et al, 2008(Abdurakhmonov et al, , 2009Wang et al, 2013;Cai et al, 2014;Jia et al, 2014;Saeed et al, 2014;Zhao et al, 2014;Sethi et al, 2016;Ademe et al, 2017;Iqbal and Rahman, 2017;Li et al, 2017;Ma et al, 2017).…”