Quantitative trait loci mapping for plant architecture traits across two upland cotton populations using SSR markers

Abstract

SUMMARYCotton plant architecture is an important agronomic trait affecting yield and quality. In the present study, two F2:3 upland cotton (Gossypium hirsutum L.) populations were developed from Baimian2/TM-1 and Baimian2/CIR12 to map quantitative trait loci (QTL) for cotton plant architecture traits using simple sequence repeat (SSR) markers. A total of 73 QTL (37 significant and 36 suggestive) affecting plant architecture traits were detected in both populations. Four common QTL, qTFN-17 for total fruit nodes, qFBN-17 for fruit branch nodes, qFBL-17 for fruit branch length and qTFB-17a/qTFB-17b (qTFB-17) for total fruit branches, were found across the two populations. These common QTL should have high reliability and could be used for marker-assisted selection (MAS) to facilitate cotton plant architecture. The two common QTL, qTFN-17 and qFBL-17, were especially significant in both populations, and moreover, they explained >0·100 of the phenotypic variation in at least one population. These two QTL should be considered preferentially for MAS. The synergistic alleles and the negative alleles could be utilized in cotton plant architecture breeding programmes according to specific breeding objectives.

Identification of quantitative trait loci with main and epistatic effects for plant architecture traits in Upland cotton (Gossypium hirsutum L.)

“…, Song and Zhang , Li et al. ), and this also occurred in the present study. A total of seven chromosomes/linkages were found existing QTL clusters which involved in the control of two or more traits (Table , Fig.…”

“…, Song and Zhang , Li et al. ). Regarding the mapping populations, the F 2 population has the most complete genetic composition that can provide the most abundant genetic information.…”

“…Li et al. () identified five QTLs for PH, 10 for SNL, six for SIL, nine for TFB, three for EFB, eight for FBL, eight for FBN, eight for FBIL, 10 for TFN and six for FBA in two F 2:3 Upland cotton populations. Although there are a few reports applying molecular marker techniques to detect QTLs for cotton plant architecture traits, reliable QTLs must be verified through different generations, populations and environments.…”

High-density linkage map construction and QTL analysis for earliness-related traits in Gossypium hirsutum L

“…In particular, QTLs for FBP, WGP, NFFB, HNFFB and PH were detected as follows: QTLs for FBP on chromosomes A3, A7, A8/D8, A9, A13, D3, D4 and D7 [38]; QTLs for WGP on chromosomes A13, D3 and D8 [38]; QTLs for NFFB on chromosomes A1, A5, A6, A9, A10, A11, A13, D1, D3, D6, D7, D9 and D11 [37, 39, 41]; QTLs for HNFFB on chromosomes A9, D3 and D6 [37]; and QTLs for PH on chromosomes A3, A4, A5, A9, A11, A13, D2, D3, D4, D5, D6, D8, D9 and D11 [36, 42]. These results indicate that D3 contains QTLs for FBP, WGP, NFFB, HNFFB and PH, which suggests that D3 is one of the chromosomes responsible for cotton earliness.…”

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

“…Plenty of genetic maps have been constructed, based on which QTLs of target traits were identified in upland cotton. Compared with QTLs identified for fiber quality and yield traits, QTLs for agronomic traits are comparatively less reported (Li et al 2014;Song and Zhang 2009;Wang et al 2006;Zhang et al 2006). Therefore, it is necessary to map QTLs for agronomic traits using highdensity genetic maps.…”