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X Demographics
Mendeley readers
Attention Score in Context
| Title |
QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding
|
|---|---|
| Published in |
Frontiers in Plant Science, December 2016
|
| DOI | 10.3389/fpls.2016.01852 |
| Pubmed ID | |
| Authors |
Giriraj Kumawat, Sanjay Gupta, Milind B. Ratnaparkhe, Shivakumar Maranna, Gyanesh K. Satpute |
| Abstract |
Food legumes play an important role in attaining both food and nutritional security along with sustainable agricultural production for the well-being of humans globally. The various traits of economic importance in legume crops are complex and quantitative in nature, which are governed by quantitative trait loci (QTLs). Mapping of quantitative traits is a tedious and costly process, however, a large number of QTLs has been mapped in soybean for various traits albeit their utilization in breeding programmes is poorly reported. For their effective use in breeding programme it is imperative to narrow down the confidence interval of QTLs, to identify the underlying genes, and most importantly allelic characterization of these genes for identifying superior variants. In the field of functional genomics, especially in the identification and characterization of gene responsible for quantitative traits, soybean is far ahead from other legume crops. The availability of genic information about quantitative traits is more significant because it is easy and effective to identify homologs than identifying shared syntenic regions in other crop species. In soybean, genes underlying QTLs have been identified and functionally characterized for phosphorous efficiency, flowering and maturity, pod dehiscence, hard-seededness, α-Tocopherol content, soybean cyst nematode, sudden death syndrome, and salt tolerance. Candidate genes have also been identified for many other quantitative traits for which functional validation is required. Using the sequence information of identified genes from soybean, comparative genomic analysis of homologs in other legume crops could discover novel structural variants and useful alleles for functional marker development. The functional markers may be very useful for molecular breeding in soybean and harnessing benefit of translational research from soybean to other leguminous crops. Thus, soybean crop can act as a model crop for translational genomics and breeding of quantitative traits in legume crops. In this review, we summarize current status of identification and characterization of genes underlying QTLs for various quantitative traits in soybean and their significance in translational genomics and breeding of other legume crops. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 17% |
| Switzerland | 1 | 17% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 1% |
| Unknown | 90 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 25% |
| Researcher | 19 | 21% |
| Student > Master | 9 | 10% |
| Student > Doctoral Student | 8 | 9% |
| Other | 3 | 3% |
| Other | 10 | 11% |
| Unknown | 19 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 49 | 54% |
| Biochemistry, Genetics and Molecular Biology | 10 | 11% |
| Computer Science | 2 | 2% |
| Chemistry | 2 | 2% |
| Business, Management and Accounting | 1 | 1% |
| Other | 5 | 5% |
| Unknown | 22 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 17 January 2017.
All research outputs
#6,940,575
of 22,931,367 outputs
Outputs from Frontiers in Plant Science
#4,087
of 20,355 outputs
Outputs of similar age
#128,776
of 420,838 outputs
Outputs of similar age from Frontiers in Plant Science
#87
of 499 outputs
Altmetric has tracked 22,931,367 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 20,355 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 420,838 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 499 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.