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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays
|
|---|---|
| Published in |
Genome Biology, September 2015
|
| DOI | 10.1186/s13059-015-0716-z |
| Pubmed ID | |
| Authors |
Matteo Dell’Acqua, Daniel M. Gatti, Giorgio Pea, Federica Cattonaro, Frederik Coppens, Gabriele Magris, Aye L. Hlaing, Htay H. Aung, Hilde Nelissen, Joke Baute, Elisabetta Frascaroli, Gary A. Churchill, Dirk Inzé, Michele Morgante, Mario Enrico Pè |
| Abstract |
Maize (Zea mays) is a globally produced crop with broad genetic and phenotypic variation. New tools that improve our understanding of the genetic basis of quantitative traits are needed to guide predictive crop breeding. We have produced the first balanced multi-parental population in maize, a tool that provides high diversity and dense recombination events to allow routine quantitative trait loci (QTL) mapping in maize. We produced 1,636 MAGIC maize recombinant inbred lines derived from eight genetically diverse founder lines. The characterization of 529 MAGIC maize lines shows that the population is a balanced, evenly differentiated mosaic of the eight founders, with mapping power and resolution strengthened by high minor allele frequencies and a fast decay of linkage disequilibrium. We show how MAGIC maize may find strong candidate genes by incorporating genome sequencing and transcriptomics data. We discuss three QTL for grain yield and three for flowering time, reporting candidate genes. Power simulations show that subsets of MAGIC maize might achieve high-power and high-definition QTL mapping. We demonstrate MAGIC maize's value in identifying the genetic bases of complex traits of agronomic relevance. The design of MAGIC maize allows the accumulation of sequencing and transcriptomics layers to guide the identification of candidate genes for a number of maize traits at different developmental stages. The characterization of the full MAGIC maize population will lead to higher power and definition in QTL mapping, and lay the basis for improved understanding of maize phenotypes, heterosis included. MAGIC maize is available to researchers. |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 4 | 33% |
| United States | 1 | 8% |
| United Kingdom | 1 | 8% |
| Tunisia | 1 | 8% |
| Unknown | 5 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 58% |
| Scientists | 3 | 25% |
| Science communicators (journalists, bloggers, editors) | 2 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 314 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 3 | <1% |
| United States | 3 | <1% |
| Germany | 2 | <1% |
| Belgium | 2 | <1% |
| United Kingdom | 1 | <1% |
| Japan | 1 | <1% |
| Chile | 1 | <1% |
| Unknown | 301 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 85 | 27% |
| Researcher | 71 | 23% |
| Student > Master | 33 | 11% |
| Student > Bachelor | 16 | 5% |
| Student > Doctoral Student | 13 | 4% |
| Other | 43 | 14% |
| Unknown | 53 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 198 | 63% |
| Biochemistry, Genetics and Molecular Biology | 35 | 11% |
| Computer Science | 4 | 1% |
| Social Sciences | 3 | <1% |
| Unspecified | 2 | <1% |
| Other | 7 | 2% |
| Unknown | 65 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 69. 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 19 January 2017.
All research outputs
#615,960
of 25,374,917 outputs
Outputs from Genome Biology
#377
of 4,467 outputs
Outputs of similar age
#8,310
of 280,196 outputs
Outputs of similar age from Genome Biology
#11
of 84 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 91% 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 280,196 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.