Linked Life Data

16092759

Source:http://linkedlifedata.com/resource/pubmed/id/16092759

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2005-8-11
pubmed:abstractText
Schizophrenia is a serious neuropsychiatric illness affecting about 1% of the world's population. It is considered a complex inheritance disorder. A number of genes are involved in combination in the etiology of the disorder. Evidence implicates the altered dopaminergic transmission in schizophrenia. In the present study, in order to identify susceptibility genes for schizophrenia in dopaminergic metabolism, we analyzed 59 single nucleotide polymorphisms (SNPs) in 24 genes of the dopaminergic pathway among 82 unrelated patients with schizophrenia and 108 matched normal controls. Considering that traditional single-locus association studies ignore the multigenic nature of complex diseases and do not take into account possible interactions between susceptibility genes, we proposed a multi-locus analysis method, using the posterior probability of morbidity as a measure of absolute disease risk for a multi-locus genotype combination, and developed an algorithm based on perturbation and average to detect the susceptibility multi-locus genotype combinations, as well as to repress noise and avoid false positive results at our best. A three-locus SNP genotype combination involved in the interactions of COMT and ALDH3B1 genes was detected to be significantly susceptible to schizophrenia.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1006-9305
pubmed:author
pubmed:issnType
Print
pubmed:volume
48
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
263-9
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Multi-locus association study of schizophrenia susceptibility genes with a posterior probability method.
pubmed:affiliation
Ministry of Education (MOE) Key Laboratory of Bioinformatics at Tsinghua University, Beijing 100084, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't