Linked Life Data

11119294

Source:http://linkedlifedata.com/resource/pubmed/id/11119294

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2001-1-26
pubmed:abstractText
Genetic epidemiological methodologies, such as linkage analysis, often require accurate estimates of allele frequencies. When studies involve multiple sub-populations with different evolutionary histories, accurate estimates can be difficult to obtain because the number of subjects per sub-population tends to be limited. Given allele counts for a collection of loci and sub-populations, we propose a Bayesian hierarchical model that extends existing empirical Bayesian approaches by allowing for explicit inclusion of prior information about both allele frequencies and inter-population divergence. We describe how such information can be derived from published data and then incorporated into the model via prior distributions for model parameters. By analysis of simulated data, we highlight how the hierarchical model, as implemented in the publicly available program AllDist, combines prior information with the observed data to refine allele frequency estimates.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0741-0395
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
17-33
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
A Bayesian hierarchical model for allele frequencies.
pubmed:affiliation
Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.