Linked Life Data

19479748

Source:http://linkedlifedata.com/resource/pubmed/id/19479748

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-12-21
pubmed:abstractText
In population genetics, it is common to represent the haplotype diversity at a genomic region between multiple populations using well-constructed visual representations. This typically requires the chromosomes from these populations to be grouped according to some definition of haplotypic similarity. Here, we introduce a novel algorithm for clustering haplotypes with the specific aim of addressing haplotype diversity within or between populations. The algorithm allows for missing data in the haplotypes and appropriately downweighs single nucleotide polymorphisms with higher extent of missingness. By identifying the canonical haplotypes in a genomic region, defined as the haplotype forms, which most chromosomes are similar to, the algorithm maps each chromosome to either a unique canonical haplotype or as a mosaic of the identified canonical haplotypes. This mapping can subsequently be utilized for producing graphical visualizations of the haplotype clustering for understanding the extent of haplotype diversity in the region. The clustering application has been implemented in R for distribution as haplosim, and we also provide a visualization script hapvisual for graphical display of the clustering results. The outcome of such analysis can be informative in understanding the extent of haplotype diversity between populations, in addressing the reproducibility of established association signals across multiple populations, and also in the investigation of positive selection in the human genome.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1098-2272
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
34-41
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A novel method for haplotype clustering and visualization.
pubmed:affiliation
Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. teo@well.ox.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't