Linked Life Data

18466467

Source:http://linkedlifedata.com/resource/pubmed/id/18466467

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-5-9
pubmed:abstractText
Given the increasing size of modern genetic data sets and, in particular, the move towards genome-wide studies, there is merit in considering analyses that gain computational efficiency by being more heuristic in nature. With this in mind, we present results of cladistic analyses methods on the Genetic Analysis Workshop 15 Problem 3 simulated data (answers known). Our analysis attempts to capture similarities between individuals using a series of trees, and then looks for regions in which mutations on those trees can successfully explain a phenotype of interest. Existing varieties of such algorithms assume haplotypes are known, or have been inferred, an assumption that is often unrealistic for genome-wide data. We therefore present an extension of these methods that can successfully analyze genotype, rather than haplotype, data.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1753-6561
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
1 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S125
pubmed:dateRevised
2010-9-22
pubmed:year
2007
pubmed:articleTitle
Cladistic analysis of genotype data-application to GAW15 Problem 3.
pubmed:affiliation
Keck School of Medicine, Preventive Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089-9011, USA. hsuanjun@usc.edu
pubmed:publicationType
Journal Article