Linked Life Data

15389926

Source:http://linkedlifedata.com/resource/pubmed/id/15389926

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-10-11
pubmed:abstractText
Admixture mapping is potentially a powerful method for mapping genes for complex human diseases, when the disease frequency due to a particular disease-susceptible gene is different between founding populations of different ethnicity. The method tests for association of the allele ancestry with the disease. Since the markers used to define ancestral populations are not fully informative for the ancestry status, direct test of such association is not possible. In this report, we develop a unified hidden Markov model (HMM) framework for estimating the unobserved ancestry haplotypes across a chromosomal region based on marker haplotype or genotype data. The HMM efficiently utilizes all the marker data to infer the latent ancestry states at the putative disease locus. In this HMM modelling framework, we develop a likelihood test for association of allele ancestry and the disease risk based on case-control data. Existence of such association may imply linkage between the candidate locus and the disease locus. We evaluate by simulations how several factors affect the power of admixture mapping, including sample size, ethnicity relative risk, marker density, and the different admixture dynamics. Our simulation results indicate correct type 1 error rates of the proposed likelihood ratio tests and great impact of marker density on the power. The simulation results also indicate that the methods work well for the admixed populations derived from both hybrid-isolation and continuous gene-flowing models. Finally, we observed that the genotype-based HMM performs very similarly in power as the haplotype-based HMM when the haplotypes are known and the set of markers is highly informative.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0741-0395
pubmed:author
pubmed:issnType
Print
pubmed:volume
27
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
225-39
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
A hidden Markov modeling approach for admixture mapping based on case-control data.
pubmed:affiliation
Departments of Statistics and Medicine and Rowe Program in Human Genetics, University of California, Davis, California 95616, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.