Journal article
A Powerful Variant-Set Association Test Based on Chi-Square Distribution
Genetics (Austin), Vol.207(3), pp.903-910
11/2017
DOI: 10.1534/genetics.117.300287
PMCID: PMC5669628
PMID: 28912342
Abstract
Detecting the association between a set of variants and a given phenotype has attracted a large amount of attention in the scientific community, although it is a difficult task. Recently, several related statistical approaches have been proposed in the literature; powerful statistical tests are still highly desired and yet to be developed in this area. In this paper, we propose a powerful test that combines information from each individual single nucleotide polymorphism (SNP) based on principal component analysis without relying on the eigenvalues associated with the principal components. We compare the proposed approach with some popular tests through a simulation study and real data applications. Our results show that, in general, the new test is more powerful than its competitors considered in this study; the gain in detecting power can be substantial in many situations.
Details
- Title: Subtitle
- A Powerful Variant-Set Association Test Based on Chi-Square Distribution
- Creators
- Zhongxue Chen - Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Indiana 47405 zc3@indiana.eduTong Lin - The Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University, Beijing 100871, ChinaKai Wang - Department of Biostatistics, College of Public Health, University of Iowa, Iowa 52242
- Resource Type
- Journal article
- Publication Details
- Genetics (Austin), Vol.207(3), pp.903-910
- Publisher
- United States
- DOI
- 10.1534/genetics.117.300287
- PMID
- 28912342
- PMCID
- PMC5669628
- ISSN
- 1943-2631
- eISSN
- 1943-2631
- Grant note
- P30 ES005605 / NIEHS NIH HHS
- Language
- English
- Date published
- 11/2017
- Academic Unit
- Biostatistics
- Record Identifier
- 9983997469302771
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