Timothy J. Close, University of California - RiversideFollow
Prasanna R. Bhat, University of California - Riverside
Stefano Lonardi, University of California - RiversideFollow
Yonghui Wu, University of California - RiversideFollow
Nils Rostoks, Scottish Crop Research InstituteFollow
Luke Ramsay, Scottish Crop Research Institute
Arnis Druka, Scottish Crop Research Institute
Nils Stein, Leibniz Institute of Plant Genetics and Crop Plant ResearchFollow
Jan T. Svensson, University of CopenhagenFollow
Steve Wanamaker, University of California - Riverside
Serdar Bozdag, Marquette UniversityFollow
Mikeal L. Roose, University of California - Riverside
Matthew J. Moscou, University of California - RiversideFollow
Shiaoman Chao, USDA-ARS Biosciences Research Lab
Rajeev K. Varshney, Leibniz Institute of Plant Genetics and Crop Plant Research
Peter Szucs, Oregon State University
Kazuhiro Sato, Okayama University
Patrick M. Hayes, Oregon State University
David E. Matthews, Cornell UniversityFollow
Andris Kleinhofs, Washington State University
Gary J. Muehlbauer, University of Minnesota - St. Paul
Joseph DeYoung, University of California - Los Angeles
David F. Marshall, Scottish Crop Research InstituteFollow
Kavitha Madishetty, University of California - Riverside
Raymond D. Fenton, University of California - Riverside
Pascal Condamine, University of California - Riverside
Andreas Graner, Leibniz Institute of Plant Genetics and Crop Plant Research
Robbie Waugh, Scottish Crop Research Institute

Document Type




Publication Date



BioMed Central

Source Publication

BMC Genomics

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High density genetic maps of plants have, nearly without exception, made use of marker datasets containing missing or questionable genotype calls derived from a variety of genic and non-genic or anonymous markers, and been presented as a single linear order of genetic loci for each linkage group. The consequences of missing or erroneous data include falsely separated markers, expansion of cM distances and incorrect marker order. These imperfections are amplified in consensus maps and problematic when fine resolution is critical including comparative genome analyses and map-based cloning. Here we provide a new paradigm, a high-density consensus genetic map of barley based only on complete and error-free datasets and genic markers, represented accurately by graphs and approximately by a best-fit linear order, and supported by a readily available SNP genotyping resource.


Approximately 22,000 SNPs were identified from barley ESTs and sequenced amplicons; 4,596 of them were tested for performance in three pilot phase Illumina GoldenGate assays. Data from three barley doubled haploid mapping populations supported the production of an initial consensus map. Over 200 germplasm selections, principally European and US breeding material, were used to estimate minor allele frequency (MAF) for each SNP. We selected 3,072 of these tested SNPs based on technical performance, map location, MAF and biological interest to fill two 1536-SNP "production" assays (BOPA1 and BOPA2), which were made available to the barley genetics community. Data were added using BOPA1 from a fourth mapping population to yield a consensus map containing 2,943 SNP loci in 975 marker bins covering a genetic distance of 1099 cM.


The unprecedented density of genic markers and marker bins enabled a high resolution comparison of the genomes of barley and rice. Low recombination in pericentric regions is evident from bins containing many more than the average number of markers, meaning that a large number of genes are recombinationally locked into the genetic centromeric regions of several barley chromosomes. Examination of US breeding germplasm illustrated the usefulness of BOPA1 and BOPA2 in that they provide excellent marker density and sensitivity for detection of minor alleles in this genetically narrow material.


Published version. BMC Genomics, Vol. 10, No. 1 (2009). DOI.

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.