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    Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes

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    Type
    Article
    Authors
    Chatterjee, Nilanjan
    Chen, Yi-Hau
    Luo, Sheng
    Carroll, Raymond J.
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2009-11
    Permanent link to this record
    http://hdl.handle.net/10754/597553
    
    Metadata
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    Abstract
    Although prospective logistic regression is the standard method of analysis for case-control data, it has been recently noted that in genetic epidemiologic studies one can use the "retrospective" likelihood to gain major power by incorporating various population genetics model assumptions such as Hardy-Weinberg-Equilibrium (HWE), gene-gene and gene-environment independence. In this article we review these modern methods and contrast them with the more classical approaches through two types of applications (i) association tests for typed and untyped single nucleotide polymorphisms (SNPs) and (ii) estimation of haplotype effects and haplotype-environment interactions in the presence of haplotype-phase ambiguity. We provide novel insights to existing methods by construction of various score-tests and pseudo-likelihoods. In addition, we describe a novel two-stage method for analysis of untyped SNPs that can use any flexible external algorithm for genotype imputation followed by a powerful association test based on the retrospective likelihood. We illustrate applications of the methods using simulated and real data. © Institute of Mathematical Statistics, 2009.
    Citation
    Chatterjee N, Chen Y-H, Luo S, Carroll RJ (2009) Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes. Statist Sci 24: 489–502. Available: http://dx.doi.org/10.1214/09-STS297.
    Sponsors
    Chatterjee's research was supported by a gene environment initiative grant from the National Heart Lung and Blood Institute (RO1HL091172-01) and by the Intramural Research Program of the National Cancer Institute. Chen's research was supported by the National Science Council of ROC (NSC 95-2118-M-001-022-MY3). Carroll's research was supported by a grant from the National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Institute of Mathematical Statistics
    Journal
    Statistical Science
    DOI
    10.1214/09-STS297
    ae974a485f413a2113503eed53cd6c53
    10.1214/09-STS297
    Scopus Count
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