title: Rapid gene identification in sugar beet using deep sequencing of DNA from phenotypic pools selected from breeding panels.
abstract: BACKGROUND
  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).
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: CeBiTec & Department of Biology, Bielefeld University, D-33594, Bielefeld, Germany.
authors.forename: David
authors.initials: D
authors.lastname: Ries
authors.affiliation: CeBiTec & Department of Biology, Bielefeld University, D-33594, Bielefeld, Germany.
authors.forename: Daniela
authors.initials: D
authors.lastname: Holtgräwe
authors.affiliation: CeBiTec & Department of Biology, Bielefeld University, D-33594, Bielefeld, Germany.
authors.forename: Prisca
authors.initials: P
authors.lastname: Viehöver
authors.affiliation: CeBiTec & Department of Biology, Bielefeld University, D-33594, Bielefeld, Germany. bernd.weisshaar@uni-bielefeld.de.
authors.forename: Bernd
authors.identifier: http://orcid.org/0000-0002-7635-3473
authors.initials: B
authors.lastname: Weisshaar
mesh_terms: D000483:Alleles; D027461:Beta vulgaris / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D003116:Color; D018744:DNA, Plant / Q000235:genetics; D005787:Gene Frequency; D017343:Genes, Plant; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D018546:Hypocotyl / Q000235:genetics; D010641:Phenotype; D000069600:Plant Breeding; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D018744:DNA, Plant
keywords: Allele frequency; Beta vulgaris; Gene identification; Mapping by sequencing; Phenotypic pools; R locus; SNP detection; Sugar beet