| Title |
Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in Upland cotton (Gossypium hirsutum L.)
|
|---|---|
| Published in |
Molecular Genetics and Genomics, July 2017
|
| DOI | 10.1007/s00438-017-1347-8 |
| Pubmed ID | |
| Authors |
Xueying Liu, Zhonghua Teng, Jinxia Wang, Tiantian Wu, Zhiqin Zhang, Xianping Deng, Xiaomei Fang, Zhaoyun Tan, Iftikhar Ali, Dexin Liu, Jian Zhang, Dajun Liu, Fang Liu, Zhengsheng Zhang |
| Abstract |
Cotton is a significant commercial crop that plays an indispensable role in many domains. Constructing high-density genetic maps and identifying stable quantitative trait locus (QTL) controlling agronomic traits are necessary prerequisites for marker-assisted selection (MAS). A total of 14,899 SSR primer pairs designed from the genome sequence of G. raimondii were screened for polymorphic markers between mapping parents CCRI 35 and Yumian 1, and 712 SSR markers showing polymorphism were used to genotype 180 lines from a (CCRI 35 × Yumian 1) recombinant inbred line (RIL) population. Genetic linkage analysis was conducted on 726 loci obtained from the 712 polymorphic SSR markers, along with 1379 SSR loci obtained in our previous study, and a high-density genetic map with 2051 loci was constructed, which spanned 3508.29 cM with an average distance of 1.71 cM between adjacent markers. Marker orders on the linkage map are highly consistent with the corresponding physical orders on a G. hirsutum genome sequence. Based on fiber quality and yield component trait data collected from six environments, 113 QTLs were identified through two analytical methods. Among these 113 QTLs, 50 were considered stable (detected in multiple environments or for which phenotypic variance explained by additive effect was greater than environment effect), and 18 of these 50 were identified with stability by both methods. These 18 QTLs, including eleven for fiber quality and seven for yield component traits, could be priorities for MAS. |
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| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
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| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
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|---|---|---|
| Unknown | 13 | 100% |
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| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 23% |
| Student > Doctoral Student | 2 | 15% |
| Student > Postgraduate | 2 | 15% |
| Researcher | 1 | 8% |
| Unknown | 5 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 46% |
| Environmental Science | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Medicine and Dentistry | 1 | 8% |
| Unknown | 4 | 31% |
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