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Genomic prediction of crown rust resistance in Lolium perenne

Overview of attention for article published in BMC Genomic Data, May 2018
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Title
Genomic prediction of crown rust resistance in Lolium perenne
Published in
BMC Genomic Data, May 2018
DOI 10.1186/s12863-018-0613-z
Pubmed ID
Authors

Sai Krishna Arojju, Patrick Conaghan, Susanne Barth, Dan Milbourne, Michael D. Casler, Trevor R. Hodkinson, Thibauld Michel, Stephen L. Byrne

Abstract

Genomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability. Using these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set. Using a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Student > Master 4 14%
Student > Doctoral Student 2 7%
Researcher 2 7%
Other 2 7%
Other 4 14%
Unknown 9 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 52%
Biochemistry, Genetics and Molecular Biology 2 7%
Social Sciences 1 3%
Medicine and Dentistry 1 3%
Unknown 10 34%
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 31 May 2018.
All research outputs
#20,663,600
of 25,382,440 outputs
Outputs from BMC Genomic Data
#861
of 1,204 outputs
Outputs of similar age
#268,723
of 344,685 outputs
Outputs of similar age from BMC Genomic Data
#11
of 15 outputs
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We're also able to compare this research output to 15 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.