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| Title |
A dense SNP genetic map constructed using restriction site-associated DNA sequencing enables detection of QTLs controlling apple fruit quality
|
|---|---|
| Published in |
BMC Genomics, October 2015
|
| DOI | 10.1186/s12864-015-1946-x |
| Pubmed ID | |
| Authors |
Rui Sun, Yuansheng Chang, Fengqiu Yang, Yi Wang, Hui Li, Yongbo Zhao, Dongmei Chen, Ting Wu, Xinzhong Zhang, Zhenhai Han |
| Abstract |
Genetic map based quantitative trait locus (QTL) analysis is an important method for studying important horticultural traits in apple. To facilitate molecular breeding studies of fruit quality traits in apple, we aim to construct a high density map which was efficient for QTL mapping and possible to search for candidate genes directly in mapped QTLs regions. A total of 1733 F1 seedlings derived from 'Jonathan' × 'Golden Delicious' was used for the map constructionand QTL analysis. The SNP markers were developed by restriction site-associated DNA sequencing (RADseq). Phenotyping data of fruit quality traits were calculated in 2008-2011. Once QTLs were mapped, candidate genes were searched for in the corresponding regions of the apple genome sequence underlying the QTLs. Then some of the candidate genes were validated using real-time PCR. A high-density genetic map with 3441 SNP markers from 297 individuals was generated. Of the 3441 markers, 2017 were mapped to 'Jonathan' with a length of 1343.4 cM and the average distance between markers was 0.67 cM, 1932 were mapped to 'Golden Delicious' with a length of 1516.0 cM and the average distance between markers was 0.78 cM. Twelve significant QTLs linked to the control of fruit weight, fruit firmness, sugar content and fruit acidity were mapped to seven linkage groups. Based on gene annotation, 80, 64 and 17 genes related to fruit weight, fruit firmness and fruit acidity, respectively, were analyzed.Among the 17 candidate genes associated with control of fruit acidity, changes in the expression of MDP0000582174 (MdMYB4) were in agreement with the pattern of changes in malic acid content in apple during ripening, and the relative expression of MDP0000239624 (MdME) was significantly correlated withfruit acidity. We demonstrated the construction of a dense SNP genetic map in apple using next generation sequencing and that the increased resolution enabled the detection of narrow interval QTLs linked to the three fruit quality traits assessed. The candidate genes MDP0000582174 and MDP0000239624 were found to be related to fruit acidity regulation. We conclude that application of RADseq for genetic map construction improved the precision of QTL detection and should be utilized in future studies on the regulatory mechanisms of important fruit traits in apple. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 1% |
| United States | 1 | 1% |
| Poland | 1 | 1% |
| Unknown | 78 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 15 | 19% |
| Researcher | 14 | 17% |
| Student > Ph. D. Student | 14 | 17% |
| Student > Bachelor | 5 | 6% |
| Student > Doctoral Student | 4 | 5% |
| Other | 11 | 14% |
| Unknown | 18 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 44 | 54% |
| Biochemistry, Genetics and Molecular Biology | 10 | 12% |
| Medicine and Dentistry | 3 | 4% |
| Nursing and Health Professions | 1 | 1% |
| Immunology and Microbiology | 1 | 1% |
| Other | 3 | 4% |
| Unknown | 19 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 October 2015.
All research outputs
#18,428,159
of 22,829,683 outputs
Outputs from BMC Genomics
#8,183
of 10,655 outputs
Outputs of similar age
#199,641
of 277,499 outputs
Outputs of similar age from BMC Genomics
#320
of 361 outputs
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