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Quantitative trait loci associated with different polar metabolites in perennial ryegrass - providing scope for breeding towards increasing certain polar metabolites

Overview of attention for article published in BMC Genomic Data, October 2017
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Title
Quantitative trait loci associated with different polar metabolites in perennial ryegrass - providing scope for breeding towards increasing certain polar metabolites
Published in
BMC Genomic Data, October 2017
DOI 10.1186/s12863-017-0552-0
Pubmed ID
Authors

Alexandre Foito, Christine Anne Hackett, Derek Stewart, Janaki Velmurugan, Dan Milbourne, Stephen L. Byrne, Susanne Barth

Abstract

Recent advances in the mapping of biochemical traits have been reported in Lolium perenne. Although the mapped traits, including individual sugars and fatty acids, contribute greatly towards ruminant productivity, organic acids and amino acids have been largely understudied despite their influence on the ruminal microbiome. In this study, we used a targeted gas-chromatography mass spectrometry (GC-MS) approach to profile the levels of 25 polar metabolites from different classes (sugars, amino acids, phenolic acids, organic acids and other nitrogen-containing compounds) present in a L. perenne F2 population consisting of 325 individuals. A quantitative trait (QTL) mapping approach was applied and successfully identified QTLs regulating seven of those polar metabolites (L-serine, L-leucine, glucose, fructose, myo-inositol, citric acid and 2, 3-hydroxypropanoic acid).Two QTL mapping approaches were carried out using SNP markers on about half of the population only and an imputation approach using SNP and DArT markers on the entire population. The imputation approach confirmed the four QTLs found in the SNP-only analysis and identified a further seven QTLs. These results highlight the potential of utilising molecular assisted breeding in perennial ryegrass to modulate a range of biochemical quality traits with downstream effects in livestock productivity and ruminal digestion.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Researcher 5 23%
Student > Master 2 9%
Lecturer 1 5%
Professor > Associate Professor 1 5%
Other 1 5%
Unknown 6 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 50%
Biochemistry, Genetics and Molecular Biology 2 9%
Earth and Planetary Sciences 1 5%
Medicine and Dentistry 1 5%
Chemistry 1 5%
Other 0 0%
Unknown 6 27%
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 12 October 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from BMC Genomic Data
#1,008
of 1,204 outputs
Outputs of similar age
#293,008
of 333,631 outputs
Outputs of similar age from BMC Genomic Data
#11
of 13 outputs
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