| Title |
Development of sequence-based markers for seed protein content in pigeonpea
|
|---|---|
| Published in |
Molecular Genetics and Genomics, September 2018
|
| DOI | 10.1007/s00438-018-1484-8 |
| Pubmed ID | |
| Authors |
Jimmy Obala, Rachit K. Saxena, Vikas K. Singh, C. V. Sameer Kumar, K. B. Saxena, Pangirayi Tongoona, Julia Sibiya, Rajeev K. Varshney |
| Abstract |
Pigeonpea is an important source of dietary protein to over a billion people globally, but genetic enhancement of seed protein content (SPC) in the crop has received limited attention for a long time. Use of genomics-assisted breeding would facilitate accelerating genetic gain for SPC. However, neither genetic markers nor genes associated with this important trait have been identified in this crop. Therefore, the present study exploited whole genome re-sequencing (WGRS) data of four pigeonpea genotypes (~ 12X coverage) to identify sequence-based markers and associated candidate genes for SPC. By combining a common variant filtering strategy on available WGRS data with knowledge of gene functions in relation to SPC, 108 sequence variants from 57 genes were identified. These genes were assigned to 19 GO molecular function categories with 56% belonging to only two categories. Furthermore, Sanger sequencing confirmed presence of 75.4% of the variants in 37 genes. Out of 30 sequence variants converted into CAPS/dCAPS markers, 17 showed high level of polymorphism between low and high SPC genotypes. Assay of 16 of the polymorphic CAPS/dCAPS markers on an F2 population of the cross ICP 5529 (high SPC) × ICP 11605 (low SPC), resulted in four of the CAPS/dCAPS markers significantly (P < 0.05) co-segregated with SPC. In summary, four markers derived from mutations in four genes will be useful for enhancing/regulating SPC in pigeonpea crop improvement programs. |
X Demographics
The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 6 | 33% |
| United States | 2 | 11% |
| United Kingdom | 1 | 6% |
| Argentina | 1 | 6% |
| Germany | 1 | 6% |
| Australia | 1 | 6% |
| Unknown | 6 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 56% |
| Scientists | 6 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
| Unknown | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 23% |
| Student > Master | 2 | 9% |
| Researcher | 2 | 9% |
| Professor | 1 | 5% |
| Other | 1 | 5% |
| Other | 2 | 9% |
| Unknown | 9 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 50% |
| Arts and Humanities | 1 | 5% |
| Immunology and Microbiology | 1 | 5% |
| Chemistry | 1 | 5% |
| Unknown | 8 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 24 January 2019.
All research outputs
#2,837,032
of 25,385,509 outputs
Outputs from Molecular Genetics and Genomics
#43
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Outputs of similar age
#56,155
of 345,739 outputs
Outputs of similar age from Molecular Genetics and Genomics
#1
of 22 outputs
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