Sequential Imputation for Multilocus Linkage Analysis
Mark Irwin, Nancy Cox, Augustine Kong
Proceedings of National Academy of Sciences (USA),
91, 11684-11688 (November 1994)
Abstract
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 155520 haplotypes is used for illustration.
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