Towards linkage analysis with markers in linkage disequilibrium by graphical modelling.
We review recent developments of MCMC integration methods for computations on graphical models for two applications in statistical genetics: modelling allelic association and pedigree based linkage analysis. We discuss and illustrate estimation of graphical models from haploid and diploid genotypes, and the importance of MCMC updating schemes beyond what is strictly necessary for irreducibility. We then outline an approach combining these methods to compute linkage statistics when alleles at the marker loci are in linkage disequilibrium. Other extensions suitable for analysis of SNP genotype data in pedigrees are also discussed and programs that implement these methods, and which are available from the author's web site, are described. We conclude with a discussion of how this still experimental approach might be further developed.
Thomas
Alun
A
Department of Biomedical Informatics, Genetic Epidemiology, University of Utah, Salt Lake City, Utah 84108, USA. alun@genepi.med.utah.edu
D002648:Child; D004171:Diploidy; D005260:Female; D005819:Genetic Markers; D006801:Humans; D015810:Linkage Disequilibrium; D008297:Male; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide
D005819:Genetic Markers