Single-feature polymorphism discovery by computing probe affinity shape powers

Wayne W. Xu, Seungho Cho, S. Samuel Yang, Yung Tsi Bolon, Hatice Bilgic, Haiyan Jia, Yanwen Xiong, Gary J Muehlbauer

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Background: Single-feature polymorphism (SFP) discovery is a rapid and cost-effective approach to identify DNA polymorphisms. However, high false positive rates and/or low sensitivity are prevalent in previously described SFP detection methods. This work presents a new computing method for SFP discovery. Results: The probe affinity differences and affinity shape powers formed by the neighboring probes in each probe set were computed into SFP weight scores. This method was validated by known sequence information and was comprehensively compared with previously-reported methods using the same datasets. A web application using this algorithm has been implemented for SFP detection. Using this method, we identified 364 SFPs in a barley near-isogenic line pair carrying either the wild type or the mutant uniculm2 (cul2) allele. Most of the SFP polymorphisms were identified on chromosome 6H in the vicinity of the Cul2 locus. Conclusion: This SFP discovery method exhibits better performance in specificity and sensitivity over previously-reported methods. It can be used for other organisms for which GeneChip technology is available. The web-based tool will facilitate SFP discovery. The 364 SFPs discovered in a barley near-isogenic line pair provide a set of genetic markers for fine mapping and future map-based cloning of the Cul2 locus.

Original languageEnglish (US)
Article number48
Pages (from-to)48
Number of pages1
JournalBMC genetics
Volume10
DOIs
StatePublished - Aug 26 2009

Bibliographical note

Funding Information:
We would like to thank Dr. X. Cui for providing the PAOP source code. We would also like to thank the two anonymous reviewers for their suggestions that have improved this paper. We are grateful for resources from the University of Minnesota Supercomputing Institute for Advanced Computational Research. This research was supported by grants to GJM from the United States Department of Agriculture-CSREES-NRI Plant Growth and Development program grant #2004-03440, from the U.S. Wheat and Barley Scab Initiative, from the Minnesota Small Grains Initiative, and from the Minnesota Soybean Research and Promotion Council. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.

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