Linked Life Data

16278945

Source:http://linkedlifedata.com/resource/pubmed/id/16278945

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2005-11-9
pubmed:abstractText
The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1021-38
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
A statistical framework for haplotype block inference.
pubmed:affiliation
Statistical Genetics and Bioinformatics Unit, Howard University, Washington, DC 20059, USA. ayuan@howard.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural