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

Overview of attention for article published in BMC Genomics, May 2013
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Title
Quantitative trait loci in hop (Humulus lupulusL.) reveal complex genetic architecture underlying variation in sex, yield and cone chemistry
Published in
BMC Genomics, May 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.

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Geographical breakdown

Country Count As %
Germany 2 2%
France 1 1%
Unknown 87 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 27%
Student > Ph. D. Student 17 19%
Student > Bachelor 11 12%
Student > Master 9 10%
Student > Doctoral Student 5 6%
Other 14 16%
Unknown 10 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 67%
Biochemistry, Genetics and Molecular Biology 7 8%
Chemistry 5 6%
Computer Science 2 2%
Arts and Humanities 1 1%
Other 4 4%
Unknown 11 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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
#15,740,505
of 25,374,917 outputs
Outputs from BMC Genomics
#5,756
of 11,244 outputs
Outputs of similar age
#117,599
of 207,699 outputs
Outputs of similar age from BMC Genomics
#94
of 178 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 46th percentile – i.e., 46% 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 207,699 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.