Source:http://linkedlifedata.com/resource/pubmed/id/20976795
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
7
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| pubmed:dateCreated |
2010-10-26
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| pubmed:abstractText |
To detect genetic association with common and complex diseases, many statistical tests have been proposed for candidate gene or genome-wide association studies with the case-control design. Due to linkage disequilibrium (LD), multi-marker association tests can gain power over single-marker tests with a Bonferroni multiple testing adjustment. Among many existing multi-marker association tests, most target to detect only one of many possible aspects in distributional differences between the genotypes of cases and controls, such as allele frequency differences, while a few new ones aim to target two or three aspects, all of which can be implemented in logistic regression. In contrast to logistic regression, a genomic distance-based regression (GDBR) approach aims to detect some high-order genotypic differences between cases and controls. A recent study has confirmed the high power of GDBR tests. At this moment, the popular logistic regression and the emerging GDBR approaches are completely unrelated; for example, one has to choose between the two. In this article, we reformulate GDBR as logistic regression, opening a venue to constructing other powerful tests while overcoming some limitations of GDBR. For example, asymptotic distributions can replace time-consuming permutations for deriving P-values and covariates, including gene-gene interactions, can be easily incorporated. Importantly, this reformulation facilitates combining GDBR with other existing methods in a unified framework of logistic regression. In particular, we show that Fisher's P-value combining method can boost statistical power by incorporating information from allele frequencies, Hardy-Weinberg disequilibrium, LD patterns, and other higher-order interactions among multi-markers as captured by GDBR.
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| pubmed:grant | |
| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:chemical | |
| pubmed:status |
MEDLINE
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| pubmed:month |
Nov
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| pubmed:issn |
1098-2272
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| pubmed:author | |
| pubmed:copyrightInfo |
© 2010 Wiley-Liss, Inc.
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| pubmed:issnType |
Electronic
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| pubmed:volume |
34
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
680-8
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| pubmed:meshHeading |
pubmed-meshheading:20976795-Alleles,
pubmed-meshheading:20976795-Amyotrophic Lateral Sclerosis,
pubmed-meshheading:20976795-Computer Simulation,
pubmed-meshheading:20976795-Gene Frequency,
pubmed-meshheading:20976795-Genetic Markers,
pubmed-meshheading:20976795-Genome-Wide Association Study,
pubmed-meshheading:20976795-Humans,
pubmed-meshheading:20976795-Linkage Disequilibrium,
pubmed-meshheading:20976795-Logistic Models,
pubmed-meshheading:20976795-Models, Genetic,
pubmed-meshheading:20976795-Molecular Epidemiology,
pubmed-meshheading:20976795-Polymorphism, Single Nucleotide,
pubmed-meshheading:20976795-Regression Analysis
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| pubmed:year |
2010
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| pubmed:articleTitle |
Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression.
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| pubmed:affiliation |
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455–0392, USA.
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| pubmed:publicationType |
Journal Article,
Research Support, N.I.H., Extramural
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