You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genome-wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm
|
|---|---|
| Published in |
Theoretical and Applied Genetics, July 2015
|
| DOI | 10.1007/s00122-015-2559-0 |
| Pubmed ID | |
| Authors |
Manje Gowda, Biswanath Das, Dan Makumbi, Raman Babu, Kassa Semagn, George Mahuku, Michael S. Olsen, Jumbo M. Bright, Yoseph Beyene, Boddupalli M. Prasanna |
| Abstract |
Genome-wide association analysis in tropical and subtropical maize germplasm revealed that MLND resistance is influenced by multiple genomic regions with small to medium effects. The maize lethal necrosis disease (MLND) caused by synergistic interaction of Maize chlorotic mottle virus and Sugarcane mosaic virus, and has emerged as a serious threat to maize production in eastern Africa since 2011. Our objective was to gain insights into the genetic architecture underlying the resistance to MLND by genome-wide association study (GWAS) and genomic selection. We used two association mapping (AM) panels comprising a total of 615 diverse tropical/subtropical maize inbred lines. All the lines were evaluated against MLND under artificial inoculation. Both the panels were genotyped using genotyping-by-sequencing. Phenotypic variation for MLND resistance was significant and heritability was moderately high in both the panels. Few promising lines with high resistance to MLND were identified to be used as potential donors. GWAS revealed 24 SNPs that were significantly associated (P < 3 × 10(-5)) with MLND resistance. These SNPs are located within or adjacent to 20 putative candidate genes that are associated with plant disease resistance. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed higher prediction accuracy for IMAS-AM panel (0.56) over DTMA-AM (0.36) panel. The prediction accuracy for both within and across panels is promising; inclusion of MLND resistance associated SNPs into the prediction model further improved the accuracy. Overall, the study revealed that resistance to MLND is controlled by multiple loci with small to medium effects and the SNPs identified by GWAS can be used as potential candidates in MLND resistance breeding program. |
X Demographics
The data shown below were collected from the profiles of 3 X users 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 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 3 | 1% |
| France | 1 | <1% |
| Germany | 1 | <1% |
| Brazil | 1 | <1% |
| Italy | 1 | <1% |
| Unknown | 197 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 46 | 23% |
| Researcher | 42 | 21% |
| Student > Ph. D. Student | 36 | 18% |
| Student > Doctoral Student | 14 | 7% |
| Student > Postgraduate | 8 | 4% |
| Other | 26 | 13% |
| Unknown | 32 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 127 | 62% |
| Biochemistry, Genetics and Molecular Biology | 23 | 11% |
| Nursing and Health Professions | 2 | <1% |
| Engineering | 2 | <1% |
| Computer Science | 2 | <1% |
| Other | 5 | 2% |
| Unknown | 43 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 December 2015.
All research outputs
#3,100,947
of 23,794,258 outputs
Outputs from Theoretical and Applied Genetics
#322
of 3,565 outputs
Outputs of similar age
#39,880
of 263,717 outputs
Outputs of similar age from Theoretical and Applied Genetics
#1
of 45 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,565 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 90% 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 263,717 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.