Linked Life Data

17310128

Source:http://linkedlifedata.com/resource/pubmed/id/17310128

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3-4
pubmed:dateCreated
2007-3-21
pubmed:abstractText
The population-based case-control design is a powerful approach for detecting susceptibility markers of a complex disease. However, this approach may lead to spurious association when there is population substructure: population stratification (PS) or cryptic relatedness (CR). Two simple approaches to correct for the population substructure are genomic control (GC) and delta centralization (DC). GC uses the variance inflation factor to correct for the variance distortion of a test statistic, and the DC centralizes the non-central chi-square distribution of the test statistic. Both GC and DC have been studied for case-control association studies mainly under a specific genetic model (e.g. recessive, additive or dominant), under which an optimal trend test is available. The genetic model is usually unknown for many complex diseases. In this situation, we study the performance of three robust tests based on the GC and DC corrections in the presence of the population substructure. Our results show that, when the genetic model is unknown, the DC- (or GC-) corrected maximum and Pearson's association test are robust and have good control of Type I error and high power relative to the optimal trend tests in the presence of PS (or CR).
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0001-5652
pubmed:author
pubmed:issnType
Print
pubmed:volume
63
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
187-95
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Robust genomic control and robust delta centralization tests for case-control association studies.
pubmed:affiliation
Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, PR China.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't