Statistical modeling study reveals an importance sampling framework for pedigree genetic data, suggesting improved accuracy for linkage analysis and genetic mapping.
We propose a new method for calculating probabilities for pedigree genetic data that incorporates crossover interference using the chi-square models. Applications include relationship inference, genetic map construction, and linkage analysis. The method is based on importance sampling of unobserved inheritance patterns conditional on the observed genotype data and takes advantage of fast algorithms for no-interference models while using reweighting to allow for interference. We show that the method is effective for arbitrarily many markers with small pedigrees.
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Sharon R. Browning (2003) studied this question.
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