Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes
KAUST Grant NumberKUS-CI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/597553
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AbstractAlthough 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.
CitationChatterjee 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.
SponsorsChatterjee'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).
PublisherInstitute of Mathematical Statistics