Linked Life Data

18973433

Source:http://linkedlifedata.com/resource/pubmed/id/18973433

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
2008-10-31
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1557-8666
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1155-71
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Genotype error detection using Hidden Markov Models of haplotype diversity.
pubmed:affiliation
Computer Science and Engineering Department, University of Connecticut, Storrs, Connecticut 06269-2155, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.