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题名: A Bayesian Spatial Multimarker Genetic Random-Effect Model for Fine-Scale Mapping
作者: Tsai, Miao-Yu;Hsiao, C. K.;Wen, S. H.
贡献者: 統計資訊研究所
关键词: Bayesian inference;Disease gene localization;Mixed effects model;MCMC;Spatial relation
日期: 2008-09
上传时间: 2012-10-25T09:03:30Z
出版者: Blackwell Publishing Ltd
摘要: Multiple markers in linkage disequilibrium (LD) are usually used to localize the disease gene location. These markers may
contribute to the disease etiology simultaneously. In contrast to the single-locus tests, we propose a genetic random effects
model that accounts for the dependence between loci via their spatial structures. In this model, the locus-specific random
effects measure not only the genetic disease risk, but also the correlations between markers. In other words, the model
incorporates this relation in both mean and covariance structures, and the variance components play important roles. We
consider two different settings for the spatial relations. The first is our proposal, relative distance function (RDF), which
is intuitive in the sense that markers nearby are likely to correlate with each other. The second setting is a common
exponential decay function (EDF). Under each setting, the inference of the genetic parameters is fully Bayesian with
Markov chain Monte Carlo (MCMC) sampling. We demonstrate the validity and the utility of the proposed approach
with two real datasets and simulation studies. The analyses show that the proposed model with either one of two spatial
correlations performs better as compared with the single locus analysis. In addition, under the RDF model, a more precise
estimate for the disease locus can be obtained even when the candidate markers are fairly dense. In all simulations, the
inference under the true model provides unbiased estimates of the genetic parameters, and the model with the spatial
correlation structure does lead to greater confidence interval coverage probabilities.
關聯: Annals of Human Genetics,72(5): 658-669
显示于类别:[統計資訊研究所] 期刊論文


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