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Inference of Haplotype Effects in Case‐Control Studies Using Unphased Genotype and Environmental Data
Author(s) -
Chen Xiaowu,
Li Zhaohai
Publication year - 2008
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200710396
Subject(s) - covariate , haplotype , statistics , expectation–maximization algorithm , inference , likelihood ratio test , sample size determination , haplotype estimation , type i and type ii errors , mathematics , econometrics , computer science , genotype , biology , genetics , maximum likelihood , artificial intelligence , gene
A retrospective likelihood‐based approach was proposed to test and estimate the effect of haplotype on disease risk using unphased genotype data with adjustment for environmental covariates. The proposed method was also extended to handle the data in which the haplotype and environmental covariates are not independent. Likelihood ratio tests were constructed to test the effects of haplotype and gene‐environment interaction. The model parameters such as haplotype effect size was estimated using an Expectation Conditional‐Maximization (ECM) algorithm developed by Meng and Rubin (1993). Model‐based variance estimates were derived using the observed information matrix. Simulation studies were conducted for three different genetic effect models, including dominant effect, recessive effect, and additive effect. The results showed that the proposed method generated unbiased parameter estimates, proper type I error, and true β coverage probabilities. The model performed well with small or large sample sizes, as well as short or long haplotypes. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)