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Integrated QTL detection for key breeding traits in multiple peach progenies

Overview of attention for article published in BMC Genomics, June 2017
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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Title
Integrated QTL detection for key breeding traits in multiple peach progenies
Published in
BMC Genomics, June 2017
DOI 10.1186/s12864-017-3783-6
Pubmed ID
Authors

José R. Hernández Mora, Diego Micheletti, Marco Bink, Eric Van de Weg, Celia Cantín, Nelson Nazzicari, Andrea Caprera, Maria Teresa Dettori, Sabrina Micali, Elisa Banchi, José Antonio Campoy, Elisabeth Dirlewanger, Patrick Lambert, Thierry Pascal, Michela Troggio, Daniele Bassi, Laura Rossini, Ignazio Verde, Bénédicte Quilot-Turion, François Laurens, Pere Arús, Maria José Aranzana

Abstract

Peach (Prunus persica (L.) Batsch) is a major temperate fruit crop with an intense breeding activity. Breeding is facilitated by knowledge of the inheritance of the key traits that are often of a quantitative nature. QTLs have traditionally been studied using the phenotype of a single progeny (usually a full-sib progeny) and the correlation with a set of markers covering its genome. This approach has allowed the identification of various genes and QTLs but is limited by the small numbers of individuals used and by the narrow transect of the variability analyzed. In this article we propose the use of a multi-progeny mapping strategy that used pedigree information and Bayesian approaches that supports a more precise and complete survey of the available genetic variability. Seven key agronomic characters (data from 1 to 3 years) were analyzed in 18 progenies from crosses between occidental commercial genotypes and various exotic lines including accessions of other Prunus species. A total of 1467 plants from these progenies were genotyped with a 9 k SNP array. Forty-seven QTLs were identified, 22 coinciding with major genes and QTLs that have been consistently found in the same populations when studied individually and 25 were new. A substantial part of the QTLs observed (47%) would not have been detected in crosses between only commercial materials, showing the high value of exotic lines as a source of novel alleles for the commercial gene pool. Our strategy also provided estimations on the narrow sense heritability of each character, and the estimation of the QTL genotypes of each parent for the different QTLs and their breeding value. The integrated strategy used provides a broader and more accurate picture of the variability available for peach breeding with the identification of many new QTLs, information on the sources of the alleles of interest and the breeding values of the potential donors of such valuable alleles. These results are first-hand information for breeders and a step forward towards the implementation of DNA-informed strategies to facilitate selection of new cultivars with improved productivity and quality.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 1%
Netherlands 1 1%
Unknown 86 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 20%
Researcher 17 19%
Student > Master 11 13%
Student > Bachelor 7 8%
Other 5 6%
Other 14 16%
Unknown 16 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 66%
Biochemistry, Genetics and Molecular Biology 9 10%
Business, Management and Accounting 2 2%
Nursing and Health Professions 1 1%
Social Sciences 1 1%
Other 0 0%
Unknown 17 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 June 2017.
All research outputs
#12,749,177
of 22,979,862 outputs
Outputs from BMC Genomics
#4,376
of 10,687 outputs
Outputs of similar age
#145,764
of 317,261 outputs
Outputs of similar age from BMC Genomics
#92
of 218 outputs
Altmetric has tracked 22,979,862 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,687 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 58% 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 317,261 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 53% of its contemporaries.
We're also able to compare this research output to 218 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.