A bayesian spatial multimarker genetic random-effect model for fine-scale mapping

M. Y. Tsai, C. K. Hsiao, Shu Hui Wen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)658-669
Number of pages12
JournalAnnals of Human Genetics
Volume72
Issue number5
DOIs
Publication statusPublished - 2008 Aug 21

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Markov Chains
Inborn Genetic Diseases
Genetic Models
Linkage Disequilibrium
Confidence Intervals
Genes
Datasets

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Cite this

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abstract = "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.",
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A bayesian spatial multimarker genetic random-effect model for fine-scale mapping. / Tsai, M. Y.; Hsiao, C. K.; Wen, Shu Hui.

In: Annals of Human Genetics, Vol. 72, No. 5, 21.08.2008, p. 658-669.

Research output: Contribution to journalArticle

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