Linked Life Data

16877472

Source:http://linkedlifedata.com/resource/pubmed/id/16877472

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
pubmed:issue
3
pubmed:dateCreated
2006-9-11
pubmed:abstractText
With the availability of the HapMap--a resource which describes common patterns of linkage disequilibrium (LD) in four different human population samples, we now have a powerful tool to help dissect the role of genetic variation in the biology of the genome. HapMap is entirely complimentary to the human genome map and so it is particularly fitting that it should be viewed in a full genomic context. However, characterization of high resolution LD across the genome can be a challenging task, owing in part to the sheer volume of data and the inherent dimensionality that its analysis entails. However, a number of tools are now available to make this task easier for researchers. This review will examine tools for viewing and analysing haplotype and LD data, enabling a number of tasks; including identification of optimal sets of haplotype tagging single nucleotide polymorphisms (SNPs); drawing links between associated SNPs and putative causal alleles; or simply viewing LD and haplotypes across a gene or region of interest. The data generated by the HapMap also has other important applications, informing, for example, on the demographic history and evidence of selection in human populations and on previously undetected regulatory relationships and gene networks. All of these properties make the HapMap no less an important resource than the human genome sequence itself and so this makes it essential viewing for all in the field of human biology.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1467-5463
pubmed:author
pubmed:issnType
Print
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
211-24
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Navigating the HapMap.
pubmed:affiliation
Molecular Genetic Informatics, Discovery and Analysis Bioinformatics, GlaxoSmithKline Pharmaceuticals, Harlow, Essex, UK. Michael.R.Barnes@gsk.com
pubmed:publicationType
Journal Article, Review