Markov Chain Monte Carlo Linkage Analysis Methods
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As alluded to in the chapter “Linkage Analysis of Qualitative Traits”, neither the Elston–Steward algorithm nor the Lander–Green approach is amenable to genetic data from large complex pedigrees and a large number of markers. In such cases, Monte Carlo estimation methods provide a viable alternative to the exact solutions. Two types of Monte Carlo methods have been developed for linkage analysis, haplotype inference, and other kinds of genetic analysis. They are Markov chain Monte Carlo (MCMC) methods and Monte Carlo methods that are based on independent samples. Approaches based on Markov chain Monte Carlo methods are more widely applicable; there is practically no limit on the size or complexity of the pedigrees, nor on the number of markers to be considered simultaneously. In this chapter, we will review the basic principles of MCMC methods for multipoint linkage analysis with extended pedigrees. Both simulations and application to data from the Framingham study will be used to compare and contrast three MCMC software packages: LOKI, MORGAN, and SIMWALK.
KeywordsQuantitative Trait Locus Markov Chain Monte Carlo Linkage Analysis Binary Trait Continuous Trait
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We thank Joseph Rothstein, Yun Ju Sung, and Ellen Wijsman for their valuable advice. This work was supported in part by NSF grant DMS-0112050 and NIH grants R01-HG002657 and RO1-HG003054. GAW is supported by NIH grant R01 GM031575. Some of the results of this paper were obtained by using the program package S.A.G.E., which is supported by a U.S. Public Health Service Resource Grant (RR03655) from the National Center for Research Resources. We also acknowledge the Framingham Heart Study for their permission to use the GAW 13 data, and we thank the Framingham Heart Study Investigators for their contributions and the NHLBI for collection of the Framingham Study data.
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