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Rapid gene identification in sugar beet using deep sequencing of DNA from phenotypic pools selected from breeding panels

Overview of attention for article published in BMC Genomics, March 2016
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Title
Rapid gene identification in sugar beet using deep sequencing of DNA from phenotypic pools selected from breeding panels
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2566-9
Pubmed ID
Authors

David Ries, Daniela Holtgräwe, Prisca Viehöver, Bernd Weisshaar

Abstract

The combination of bulk segregant analysis (BSA) and next generation sequencing (NGS), also known as mapping by sequencing (MBS), has been shown to significantly accelerate the identification of causal mutations for species with a reference genome sequence. The usual approach is to cross homozygous parents that differ for the monogenic trait to address, to perform deep sequencing of DNA from F2 plants pooled according to their phenotype, and subsequently to analyze the allele frequency distribution based on a marker table for the parents studied. The method has been successfully applied for EMS induced mutations as well as natural variation. Here, we show that pooling genetically diverse breeding lines according to a contrasting phenotype also allows high resolution mapping of the causal gene in a crop species. The test case was the monogenic locus causing red vs. green hypocotyl color in Beta vulgaris (R locus). We determined the allele frequencies of polymorphic sequences using sequence data from two diverging phenotypic pools of 180 B. vulgaris accessions each. A single interval of about 31 kbp among the nine chromosomes was identified which indeed contained the causative mutation. By applying a variation of the mapping by sequencing approach, we demonstrated that phenotype-based pooling of diverse accessions from breeding panels and subsequent direct determination of the allele frequency distribution can be successfully applied for gene identification in a crop species. Our approach made it possible to identify a small interval around the causative gene. Sequencing of parents or individual lines was not necessary. Whenever the appropriate plant material is available, the approach described saves time compared to the generation of an F2 population. In addition, we provide clues for planning similar experiments with regard to pool size and the sequencing depth required.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Netherlands 1 2%
Unknown 44 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Ph. D. Student 10 21%
Student > Master 7 15%
Student > Bachelor 6 13%
Student > Doctoral Student 3 6%
Other 7 15%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 53%
Biochemistry, Genetics and Molecular Biology 11 23%
Engineering 2 4%
Computer Science 1 2%
Chemical Engineering 1 2%
Other 2 4%
Unknown 5 11%
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 16 March 2016.
All research outputs
#20,315,221
of 22,856,968 outputs
Outputs from BMC Genomics
#9,285
of 10,661 outputs
Outputs of similar age
#253,212
of 299,392 outputs
Outputs of similar age from BMC Genomics
#200
of 214 outputs
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