A simple computational method for the identification of disease-associated loci in complex, incomplete pedigrees

TL;DR

The paper introduces the Shadow Method, a simple and robust approach for identifying disease loci in complex pedigrees using dense genetic marker data, overcoming limitations of traditional linkage analysis.

Contribution

It presents a novel, non-parametric method that works with incomplete pedigrees and does not require prior inheritance assumptions, suitable for high-density SNP data.

Findings

Effective in incomplete pedigrees and with unknown inheritance modes

Robust to parameter variability and clinical misdiagnosis

Validated on simulated and real SNP data from kidney failure pedigrees

Abstract

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 exploit the fact that extremely dense marker maps are becoming more readily available. We also describe how to interpret and associate 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.