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Quantitative trait loci in hop (Humulus lupulus L.) reveal complex genetic architecture underlying variation in sex, yield and cone chemistry

Overview of attention for article published in BMC Genomics, January 2013
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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1 Facebook page

Citations

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26 Dimensions

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73 Mendeley
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Title
Quantitative trait loci in hop (Humulus lupulus L.) reveal complex genetic architecture underlying variation in sex, yield and cone chemistry
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-360
Pubmed ID
Authors

Erin L McAdam, Jules S Freeman, Simon P Whittock, Emily J Buck, Jernej Jakse, Andreja Cerenak, Branka Javornik, Andrzej Kilian, Cai-Hong Wang, Dave Andersen, René E Vaillancourt, Jason Carling, Ron Beatson, Lawrence Graham, Donna Graham, Peter Darby, Anthony Koutoulis

Abstract

Hop (Humulus lupulus L.) is cultivated for its cones, the secondary metabolites of which contribute bitterness, flavour and aroma to beer. Molecular breeding methods, such as marker assisted selection (MAS), have great potential for improving the efficiency of hop breeding. The success of MAS is reliant on the identification of reliable marker-trait associations. This study used quantitative trait loci (QTL) analysis to identify marker-trait associations for hop, focusing on traits related to expediting plant sex identification, increasing yield capacity and improving bittering, flavour and aroma chemistry. QTL analysis was performed on two new linkage maps incorporating transferable Diversity Arrays Technology (DArT) markers. Sixty-three QTL were identified, influencing 36 of the 50 traits examined. A putative sex-linked marker was validated in a different pedigree, confirming the potential of this marker as a screening tool in hop breeding programs. An ontogenetically stable QTL was identified for the yield trait dry cone weight; and a QTL was identified for essential oil content, which verified the genetic basis for variation in secondary metabolite accumulation in hop cones. A total of 60 QTL were identified for 33 secondary metabolite traits. Of these, 51 were pleiotropic/linked, affecting a substantial number of secondary metabolites; nine were specific to individual secondary metabolites. Pleiotropy and linkage, found for the first time to influence multiple hop secondary metabolites, have important implications for molecular selection methods. The selection of particular secondary metabolite profiles using pleiotropic/linked QTL will be challenging because of the difficulty of selecting for specific traits without adversely changing others. QTL specific to individual secondary metabolites, however, offer unequalled value to selection programs. In addition to their potential for selection, the QTL identified in this study advance our understanding of the genetic control of traits of current economic and breeding significance in hop and demonstrate the complex genetic architecture underlying variation in these traits. The linkage information obtained in this study, based on transferable markers, can be used to facilitate the validation of QTL, crucial to the success of MAS.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
France 1 1%
Unknown 70 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 27%
Student > Ph. D. Student 16 22%
Student > Bachelor 10 14%
Student > Master 7 10%
Student > Doctoral Student 5 7%
Other 10 14%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 74%
Biochemistry, Genetics and Molecular Biology 5 7%
Chemistry 5 7%
Computer Science 1 1%
Social Sciences 1 1%
Other 2 3%
Unknown 5 7%

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 26 June 2013.
All research outputs
#2,330,083
of 6,521,832 outputs
Outputs from BMC Genomics
#2,167
of 5,128 outputs
Outputs of similar age
#34,759
of 101,853 outputs
Outputs of similar age from BMC Genomics
#75
of 165 outputs
Altmetric has tracked 6,521,832 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 5,128 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 55% 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 101,853 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 65% of its contemporaries.
We're also able to compare this research output to 165 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 53% of its contemporaries.