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Mikko J. Sillanpaa
Madhuchhanda Bhattacharjee
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doi:10.1534/genetics.104.032680
A more recent version of this article appeared on January 1, 2005.
Regular Research Papers |
Bayesian association-based fine mapping in small chromosomal segments
Mikko J. Sillanpaa 1* and Madhuchhanda Bhattacharjee 2
1 Rolf Nevanlinna Inst./ University of Helsinki
2 Rolf Nevanlinna Institute / University of Helsinki
* To whom correspondence should be addressed. E-mail: mjs{at}rolf.helsinki.fi.
Submitted on June 22, 2004
Revised on August 25, 2004
Accepted on 12 September 2004
A Bayesian method for fine mapping is presented, which deals with multi-allelic markers (with 
2 alleles), unknown phase, missing data, multiple causal variants, and both continuous and binary phenotypes. We consider small chromosomal segments spanned by a dense set of closely linked markers and putative genes only at marker points. In the phenotypic model, locus-specific indicator variables are used to control inclusion in or exclusion from marker contributions. To account for covariance between consecutive loci and to control fluctuations in association signals along a candidate region we introduce a joint prior for the indicators which depends on genetic or physical map distances. The potential of the method, including posterior estimation of trait-associated loci, their effects, linkage disequilibrium pattern due to close linkage of loci, and the age of a causal variant (time to most recent common ancestor), is illustrated with the well known cystic fibrosis and Friedreich ataxia data sets by assuming that haplotypes were not available. In addition, simulation analysis with large genetic distances is shown. Estimation of model parameters is based on Markov chain Monte Carlo sampling and is implemented using WinBUGS. The model specification code is freely available for research purposes from the URL: http://www.rni.helsinki.fi/~mjs/.
Key Words: Association analysis, Bayesian inference, MCMC, fine-scale mapping, model selection
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