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Originally published as Genetics Published Articles Ahead of Print on September 15, 2004.
Genetics, Vol. 169, 427-439, January 2005, Copyright © 2005
doi:10.1534/genetics.104.032680
Bayesian Association-Based Fine Mapping in Small Chromosomal Segments
Mikko J. Sillanpää1 and Madhuchhanda Bhattacharjee
Rolf Nevanlinna Institute, University of Helsinki, FIN-00014 Helsinki, Finland
1 Corresponding author: Rolf Nevanlinna Institute, Department of Mathematics and Statistics, P.O. Box 68, University of Helsinki, FIN-00014 Helsinki, Finland.
E-mail: mjs{at}rolf.helsinki.fi
A Bayesian method for fine mapping is presented, which deals with multiallelic markers (with two or more 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 that 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 (MCMC) sampling and is implemented using WinBUGS. The model specification code is freely available for research purposes from http://www.rni.helsinki.fi/~mjs/.
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