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Genomic dissection and prediction of heading date in perennial ryegrass

Overview of attention for article published in BMC Genomics, November 2015
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Title
Genomic dissection and prediction of heading date in perennial ryegrass
Published in
BMC Genomics, November 2015
DOI 10.1186/s12864-015-2163-3
Pubmed ID
Authors

Dario Fè, Fabio Cericola, Stephen Byrne, Ingo Lenk, Bilal Hassan Ashraf, Morten Greve Pedersen, Niels Roulund, Torben Asp, Luc Janss, Christian Sig Jensen, Just Jensen

Abstract

Genomic selection (GS) has become a commonly used technology in animal breeding. In crops, it is expected to significantly improve the genetic gains per unit of time. So far, its implementation in plant breeding has been mainly investigated in species farmed as homogeneous varieties. Concerning crops farmed in family pools, only a few theoretical studies are currently available. Here, we test the opportunity to implement GS in breeding of perennial ryegrass, using real data from a forage breeding program. Heading date was chosen as a model trait, due to its high heritability and ease of assessment. Genome Wide Association analysis was performed to uncover the genetic architecture of the trait. Then, Genomic Prediction (GP) models were tested and prediction accuracy was compared to the one obtained in traditional Marker Assisted Selection (MAS) methods. Several markers were significantly associated with heading date, some locating within or proximal to genes with a well-established role in floral regulation. GP models gave very high accuracies, which were significantly better than those obtained through traditional MAS. Accuracies were higher when predictions were made from related families and from larger training populations, whereas predicting from unrelated families caused the variance of the estimated breeding values to be biased downwards. We have demonstrated that there are good perspectives for GS implementation in perennial ryegrass breeding, and that problems resulting from low linkage disequilibrium (LD) can be reduced by the presence of structure and related families in the breeding population. While comprehensive Genome Wide Association analysis is difficult in species with extremely low LD, we did identify variants proximal to genes with a known role in flowering time (e.g. CONSTANS and Phytochrome C).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Benin 1 1%
Unknown 70 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 24%
Student > Ph. D. Student 14 20%
Student > Master 10 14%
Student > Doctoral Student 4 6%
Student > Postgraduate 4 6%
Other 10 14%
Unknown 12 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 65%
Biochemistry, Genetics and Molecular Biology 4 6%
Engineering 2 3%
Environmental Science 1 1%
Immunology and Microbiology 1 1%
Other 3 4%
Unknown 14 20%
Attention Score in Context

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 13 November 2015.
All research outputs
#15,349,796
of 22,832,057 outputs
Outputs from BMC Genomics
#6,694
of 10,655 outputs
Outputs of similar age
#165,031
of 282,576 outputs
Outputs of similar age from BMC Genomics
#289
of 390 outputs
Altmetric has tracked 22,832,057 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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 282,576 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 390 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.