QTL analysis and candidate gene identification for plant height in cotton based on an interspecific backcross inbred line population of Gossypium hirsutum × Gossypium barbadense

Ma, Jianjiang; Pei, Wenfeng; Ma, Qin; Geng, Yushui; Liu, Guoyuan; Liu, Ji; Cui, Yupeng; Zhang, Xia; Wu, Man; Li, Xingli; Li, Dan; Zang, Xinshan; Song, Jikun; Tang, Shuangshuang; Zhang, Jinfa; Yu, Shuang; Yu, Jie-Ae

doi:10.1007/s00122-019-03380-7

Cited by 34 publications

(38 citation statements)

References 63 publications

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“…In the current study, QTL mapping for agronomic traits is based on a high-density genetic map that covers a total genetic distance of 2 477.99 cM, composing 4 729 SNP markers and 122 SSR markers. Comparing the results of this study with previous common QTLs summarized with meta-analysis (Said et al 2013), and QTLs identified in recent years (Jia et al 2016;Su et al 2018;Zhang et al 2019a;Zhang et al 2019b;Ma et al 2019a), QTLs on c04 for PH and those on c01, c07, c12, c20-c21, c24, and c26 for FBN were all newly identified ones. As the existence of significant G × E interactions, QTLs identified in every environment moved around.…”

Section: Comparison With Previous Qtlssupporting

confidence: 73%

“…In stable QTLs of the current study, 723 of 925 genes for PH and 335 of 437 genes for FBN had annotation information (Additional file 6: Table S6). In previous studies, Gh_D03G0922 (MADS-box family gene; AT5G60910) and Gh_D01G1471 (GhPIN3; AT1G70940) were, respectively, annotated as AGAMOUSlike 8 and Auxin efflux carrier family protein in Arabidopsis and were verified to be responsible for PH in cotton (Su et al 2018;Ma et al 2019a). OsPIN2 and ZmPIN1a, which were also the PIN gene family members, were verified to have an effect on PH of rice and maize (Chen et al 2012;Li et al 2018b).…”

Section: Candidate Gene Functioning Analysismentioning

confidence: 97%

“…These phenotype data across multiple environments were collected and analyzed with SPSS21.0 software. The heritability of PH and FBN across environments was evaluated by QTLIciMapping software (version 4.1) (Meng et al 2015;Ma et al 2019a).…”

Section: Phenotypingmentioning

confidence: 99%

“…The genetic linkage maps have been used to detect quantitative trait locus (QTL) for cotton fiber quality, yield and various agronomic traits, which is of great significance for both marker-assisted selection as well as functional studies of candidate genes (Ma et al 2019a;Zhang et al 2016). However, the disadvantages of previous genetic maps, such as low marker density, asymmetric distribution of mapped markers, and unavailability of reference genomes for upland cotton, hindered the above-mentioned applications of the QTL detection results (Deschamps et al 2012;Jamshed et al 2016;Yang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the rapid development of highthroughput sequencing technologies, the reduction of sequencing cost, and the establishment of the reference genome of upland cotton (TM-1), a number of highdensity genetic maps have been constructed by single nucleotide polymorphism (SNP) markers including genotyping by sequencing (GBS) (Diouf et al 2018;Qi et al 2017), restriction-site associated DNA sequencing (RAD-Seq) (Hegarty et al 2013;Kundu et al 2015;Wang et al 2017), specific locus-amplified fragment sequencing (SLAF-seq) (Ali et al 2018;Zhang et al 2016), CottonSNP63K array (Hulse-Kemp et al 2015;Li et al 2016;Li et al 2018a;Zhang et al 2016), and Cot-tonSNP80K array (Cai et al 2017;Tan et al 2018;Liu et al 2018;Zou et al 2018). These high-density genetic maps significantly improved QTL detection accuracy (Ma et al 2019a;Su et al 2018;Jia et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Background: Plant height (PH) and fruit branch number (FBN) are important traits for improving yield and mechanical harvesting of cotton. In order to identify genes of PH and FBN in cotton germplasms to develop superior cultivars, quantitative trait loci (QTLs) for these traits were detected based on the phenotypic evaluation data in nine environments across four locations and 4 years and a previously reported genetic linkage map of an recombinant inbred line (RIL) population of upland cotton. Results: In total, 53 QTLs of PH and FBN, were identified on 21 chromosomes of the cotton genome except chromosomes c02, c09-c11, and c22. For PH, 27 QTLs explaining 3.81%-8.54% proportions of phenotypic variance were identified on 18 chromosomes except c02, c08-c12, c15, and c22. For FBN, 26 QTLs explaining 3.23%-11.00% proportions of phenotypic variance were identified on 16 chromosomes except c02-c03, c06, c09-c11, c17, c22-c23, and c25. Eight QTLs were simultaneously identified in at least two environments. Three QTL clusters containing seven QTLs were identified on three chromosomes (c01, c18 and c21). Eleven QTLs were the same as previously reported ones, while the rest were newly identified. Conclusions: The QTLs and QTL clusters identified in the current study will be helpful to further understand the genetic mechanism of PH and FBN development of cotton and will enhance the development of excellent cultivars for mechanical managements in cotton production.

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Section: Comparison With Previous Qtlssupporting

confidence: 73%

Section: Candidate Gene Functioning Analysismentioning

confidence: 97%

Section: Phenotypingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Advances and Applicability of Genotyping Technologies in Cotton Improvement

Bhardwaj

Devkar

Kumar

et al. 2022

Genotyping by Sequencing for Crop Improvement

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Identification of quantitative trait loci for fiber quality, yield, and plant height traits in Upland cotton

et al. 2023

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In cotton, most agronomic traits are controlled by polygenes. In this study, 110 F 6 recombinant inbred lines (RILs), derived from Upland cotton cross NC05AZ06 x NC11-2100, were used to develop a linkage map and to identify quantitative trait loci (QTL) for six fiber quality traits, three yield traits, and plant height. These RILs were genotyped using the CottonSNP63K array and phenotyped for fiber quality, yield traits, and plant height in 2-year field trials. Analysis of variance revealed significant (p < 0.05) differences among RILs for all traits studied, and the heritability estimates were moderate (30%-60%) to high (> 60%). Both positive and negative correlations were observed for fiber quality and yield traits. A total of 3,774 polymorphic SNP markers were used to develop a genetic map with an average marker density of 1.54 SNP/cM. Thirty QTL for fiber quality traits, yield traits, and plant height were detected on 15 different chromosomes, explaining 6.80%-20.02% of the phenotypic variance (PVE). Of these, 14 were major QTL (PVE > 10%), and three major QTL were detected in both years. Candidate gene analysis in the major QTL detected in both years and plant height QTL with PVE of 20.02% revealed five putative genes for fiber quality traits and one putative gene for plant height. The linkage map and identified QTL along with the candidate genes in the study could serve as additional breeding resources for Upland cotton genetic improvement.

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QTL analysis and candidate gene identification for plant height in cotton based on an interspecific backcross inbred line population of Gossypium hirsutum × Gossypium barbadense

Cited by 34 publications

References 63 publications

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

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

Advances and Applicability of Genotyping Technologies in Cotton Improvement

Identification of quantitative trait loci for fiber quality, yield, and plant height traits in Upland cotton

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