Technical Report CS-2004-12

Title: A Bayesian LOD Score for Linkage Analysis of Complex Diseases
Authors: Fishelson, Rusakov, and Geiger
Abstract: The emphasis of genetic linkage analysis is rapidly shifting from the task of locating genes responsible for mendelian diseases, for which one major gene causes the disease, to identifying a collection of genes and their interactions which govern what is termed complex diseases. We propose a Bayesian averaging approach based on a known method called MMLS-C. The difference is in the way the likelihood of data, given a specific position of a disease gene, is computed. Given a certain inheritance model, either recessive or dominant, the likelihood of data is computed by averaging the likelihood of data given this model over all penetrance values, using a flat prior. We have implemented this approach, called Maximizing Bayesian LOD score (MBLOD), within Superlink, and demonstrated via simulations its advantages.
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