Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity
Publication: Journal of Computational Biology
Volume 15, Issue Number 9
Abstract
The presence of genotyping errors can invalidate statistical tests for linkage and disease association, particularly for methods based on haplotype analysis. Becker et al. have recently proposed a simple likelihood ratio approach for detecting errors in trio genotype data. Under this approach, a SNP genotype is flagged as a potential error if the likelihood associated with the original trio genotype data increases by a multiplicative factor exceeding a user selected threshold when the SNP genotype under test is deleted. In this article we give improved error detection methods using the likelihood ratio test approach in conjunction with likelihood functions that can be efficiently computed based on a Hidden Markov Model of haplotype diversity in the population under study. Experimental results on both simulated and real datasets show that proposed methods have highly scalable running time and achieve significantly improved detection accuracy compared to previous methods.
Get full access to this article
View all available purchase options and get full access to this article.
Information & Authors
Information
Published In
Journal of Computational Biology
Volume 15 • Issue Number 9 • November 2008
Pages: 1155 - 1171
PubMed: 18973433
Copyright
Mary Ann Liebert, Inc.
History
Published in print: November 2008
Published online: 30 October 2008
Topics
Authors
Metrics & Citations
Metrics
Citations
Export Citation
Export citation
Select the format you want to export the citations of this publication.
View Options
Get Access
Access content
To read the fulltext, please use one of the options below to sign in or purchase access.⚠ Society Access
If you are a member of a society that has access to this content please log in via your society website and then return to this publication.