| Title |
Genomic prediction for grain zinc and iron concentrations in spring wheat
|
|---|---|
| Published in |
Theoretical and Applied Genetics, May 2016
|
| DOI | 10.1007/s00122-016-2726-y |
| Pubmed ID | |
| Authors |
Govindan Velu, Jose Crossa, Ravi P. Singh, Yuanfeng Hao, Susanne Dreisigacker, Paulino Perez-Rodriguez, Arun K. Joshi, Ravish Chatrath, Vikas Gupta, Arun Balasubramaniam, Chhavi Tiwari, Vinod K. Mishra, Virinder Singh Sohu, Gurvinder Singh Mavi |
| Abstract |
Predictability estimated through cross-validation approach showed moderate to high level; hence, genomic selection approach holds great potential for biofortification breeding to enhance grain zinc and iron concentrations in wheat. Wheat (Triticum aestivum L.) is a major staple crop, providing 20 % of dietary energy and protein consumption worldwide. It is an important source of mineral micronutrients such as zinc (Zn) and iron (Fe) for resource poor consumers. Genomic selection (GS) approaches have great potential to accelerate development of Fe- and Zn-enriched wheat. Here, we present the results of large-scale genomic and phenotypic data from the HarvestPlus Association Mapping (HPAM) panel consisting of 330 diverse wheat lines to perform genomic predictions for grain Zn (GZnC) and Fe (GFeC) concentrations, thousand-kernel weight (TKW) and days to maturity (DTM) in wheat. The HPAM lines were phenotyped in three different locations in India and Mexico in two successive crop seasons (2011-12 and 2012-13) for GZnC, GFeC, TKW and DTM. The genomic prediction models revealed that the estimated prediction abilities ranged from 0.331 to 0.694 for Zn and from 0.324 to 0.734 for Fe according to different environments, whereas prediction abilities for TKW and DTM were as high as 0.76 and 0.64, respectively, suggesting that GS holds great potential in biofortification breeding to enhance grain Zn and Fe concentrations in bread wheat germplasm. |
X Demographics
The data shown below were collected from the profiles of 10 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 | 1 | 10% |
| Mexico | 1 | 10% |
| Australia | 1 | 10% |
| United Kingdom | 1 | 10% |
| Unknown | 6 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 3 | 30% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 2 | 2% |
| Denmark | 1 | <1% |
| Unknown | 99 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 26% |
| Student > Ph. D. Student | 16 | 16% |
| Student > Master | 11 | 11% |
| Other | 6 | 6% |
| Student > Doctoral Student | 5 | 5% |
| Other | 13 | 13% |
| Unknown | 24 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 64 | 63% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Environmental Science | 2 | 2% |
| Arts and Humanities | 2 | 2% |
| Mathematics | 1 | <1% |
| Other | 3 | 3% |
| Unknown | 27 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 18 September 2016.
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#5,198,321
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Outputs from Theoretical and Applied Genetics
#737
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Outputs of similar age
#78,303
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Outputs of similar age from Theoretical and Applied Genetics
#12
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