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Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis

Overview of attention for article published in BMC Plant Biology, August 2017
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Title
Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis
Published in
BMC Plant Biology, August 2017
DOI 10.1186/s12870-017-1085-4
Pubmed ID
Authors

Kerstin Neumann, Yusheng Zhao, Jianting Chu, Jens Keilwagen, Jochen C. Reif, Benjamin Kilian, Andreas Graner

Abstract

Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass recently became feasible using non-destructive imaging. With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High heritabilities facilitated the temporal analysis of trait relationships and identification of quantitative trait loci (QTL). Biomass QTL tended to persist only a short period during early growth. More persistent QTL were detected around the booting stage. We identified seven major biomass QTL, which together explain 55% of the genetic variance at the seedling stage, and 43% at the booting stage. Three biomass QTL co-located with genes or QTL involved in phenology. The most important locus for biomass was independent from phenology and is located on chromosome 7HL at 141 cM. This locus explained ~20% of the genetic variance, was significant over a long period of time and co-located with HvDIM, a gene involved in brassinosteroid synthesis. Biomass is a dynamic trait and is therefore orchestrated by different QTL during early and late growth stages. Marker-assisted selection for high biomass at booting stage is most effective by also including favorable alleles from seedling biomass QTL. Selection for dynamic QTL may enhance genetic gain for complex traits such as biomass or, in the future, even grain yield.

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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 %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 20%
Researcher 12 17%
Student > Master 9 13%
Student > Bachelor 6 8%
Student > Doctoral Student 4 6%
Other 8 11%
Unknown 18 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 61%
Biochemistry, Genetics and Molecular Biology 4 6%
Environmental Science 1 1%
Neuroscience 1 1%
Engineering 1 1%
Other 0 0%
Unknown 21 30%