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We present an approach, called the Shadow Method, for the identification of disease loci from dense genetic marker maps in complex, potentially incomplete pedigrees. Shadow is a simple method based on an analysis of the patterns of obligate meiotic recombination events in genotypic data. This method can be applied to any high density marker map and was specifically designed to explore the fact that extremely dense marker maps are becoming more readily available. We also describe how to interpret and associated meaningful P-Values to the results. Shadow has significant advantages over traditional parametric linkage analysis methods in that it can be readily applied even in cases in which the topology of a pedigree or pedigrees can only be partially determined. In addition, Shadow is robust to variability in a range of parameters and in particular does not require prior knowledge of mode of inheritance, penetrance, or clinical misdiagnosis rate. Shadow can be used for any SNP data, but is especially effective when applied to dense samplings. Our primary example uses data from Affymetrix 100k SNPChip samples in which we illustrate our approach by analyzing simulated data as well as genome-wide SNP data from two pedigrees with inherited forms of kidney failure, one of which is compared with a typical LOD score analysis.
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Gregory Leibon, Dan Rockmore, and Martin R. Pollak, "A simple computational method for the identification of disease-associated loci in complex, incomplete pedigrees." Dartmouth Computer Science Technical Report TR2006-573, March 2006.
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