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Mendeley readers
Attention Score in Context
| Title |
Genome-wide association analysis on pre-harvest sprouting resistance and grain color in U.S. winter wheat
|
|---|---|
| Published in |
BMC Genomics, October 2016
|
| DOI | 10.1186/s12864-016-3148-6 |
| Pubmed ID | |
| Authors |
Meng Lin, Dadong Zhang, Shubing Liu, Guorong Zhang, Jianming Yu, Allan K. Fritz, Guihua Bai |
| Abstract |
Pre-harvest sprouting (PHS) in wheat can cause substantial reduction in grain yield and end-use quality. Grain color (GC) together with other components affect PHS resistance. Several quantitative trait loci (QTL) have been reported for PHS resistance, and two of them on chromosome 3AS (TaPHS1) and 4A have been cloned. To determine genetic architecture of PHS and GC and genetic relationships of the two traits, a genome-wide association study (GWAS) was conducted by evaluating a panel of 185 U.S. elite breeding lines and cultivars for sprouting rates of wheat spikes and GC in both greenhouse and field experiments. The panel was genotyped using the wheat 9K and 90K single nucleotide polymorphism (SNP) arrays. Four QTL for GC on four chromosomes and 12 QTL for PHS resistance on 10 chromosomes were identified in at least two experiments. QTL for PHS resistance showed varied effects under different environments, and those on chromosomes 3AS, 3AL, 3B, 4AL and 7A were the more frequently identified QTL. The common QTL for GC and PHS resistance were identified on the long arms of the chromosome 3A and 3D. Wheat grain color is regulated by the three known genes on group 3 chromosomes and additional genes from other chromosomes. These grain color genes showed significant effects on PHS resistance in some environments. However, several other QTL that did not affect grain color also played a significant role on PHS resistance. Therefore, it is possible to breed PHS-resistant white wheat by pyramiding these non-color related QTL. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 69 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 25% |
| Researcher | 12 | 17% |
| Student > Doctoral Student | 9 | 13% |
| Professor | 5 | 7% |
| Student > Master | 5 | 7% |
| Other | 7 | 10% |
| Unknown | 14 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 43 | 62% |
| Biochemistry, Genetics and Molecular Biology | 7 | 10% |
| Engineering | 3 | 4% |
| Business, Management and Accounting | 1 | 1% |
| Psychology | 1 | 1% |
| Other | 1 | 1% |
| Unknown | 13 | 19% |
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 02 September 2017.
All research outputs
#19,292,491
of 23,881,329 outputs
Outputs from BMC Genomics
#8,378
of 10,793 outputs
Outputs of similar age
#246,189
of 322,664 outputs
Outputs of similar age from BMC Genomics
#174
of 246 outputs
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