Sequential imputation for multilocus linkage analysis.
نویسندگان
چکیده
منابع مشابه
Sequential imputation for multilocus linkage analysis.
A Monte Carlo method called sequential imputation is proposed for multilocus likelihood computations. This method is most useful in mapping situations where the data consist of large pedigrees with substantial missing information and it is desirable to perform linkage analysis utilizing data from many polymorphic markers simultaneously. A pedigree example with 155 individuals, 9 loci, and 155,5...
متن کاملLinkage analysis with sequential imputation.
Multilocus calculations, using all available information on all pedigree members, are important for linkage analysis. Exact calculation methods in linkage analysis are limited in either the number of loci or the number of pedigree members they can handle. In this article, we propose a Monte Carlo method for linkage analysis based on sequential imputation. Unlike exact methods, sequential imputa...
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A novel Monte Carlo method for linkage analyses involving large pedigrees and many polymorphic loci is introduced. Issues related to the efficiency of the method are discussed.
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The increasing number of DNA polymorphisms characterized in humans will soon allow the construction of fine genetic maps of human chromosomes. This advance calls for a reexamination of current methodologies for linkage analysis by the family method. We have investigated the relative efficiency of two-point and three-point linkage tests for the detection of linkage and the estimation of recombin...
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OBJECTIVE The conventional affected sib pair methods evaluate the linkage information at a locus by considering only marginal information. We describe a multilocus linkage method that uses both the marginal information and information derived from the possible interactions among several disease loci, thereby increasing the significance of loci with modest effects. METHODS Our method is based ...
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ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 1994
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.91.24.11684