Linked Life Data

20515517

Source:http://linkedlifedata.com/resource/pubmed/id/20515517

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-6-2
pubmed:abstractText
A two-step procedure is presented for analysis of theta (FST) statistics obtained for a battery of loci, which eventually leads to a clustered structure of values. The first step uses a simple Bayesian model for drawing samples from posterior distributions of theta-parameters, but without constructing Markov chains. This step assigns a weakly informative prior to allelic frequencies and does not make any assumptions about evolutionary models. The second step regards samples from these posterior distributions as 'data' and fits a sequence of finite mixture models, with the aim of identifying clusters of theta-statistics. Hopefully, these would reflect different types of processes and would assist in interpreting results. Procedures are illustrated with hypothetical data, and with published allelic frequency data for type II diabetes in three human populations, and for 12 isozyme loci in 12 populations of the argan tree in Morocco.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1469-5073
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
92
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
141-55
pubmed:dateRevised
2010-12-28
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A two-step method for detecting selection signatures using genetic markers.
pubmed:affiliation
Department of Animal Sciences and Department of Dairy Science, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA. gianola@ansci.wisc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't