Statistical algorithms for the analysis of deleterious genetic mutations

Abstract

We present algorithms for model selection and parameter estimation concerning deleterious genetic mutations. Three models are considered: single gene mutation, double cross-effect mutations or no genetic cause. Each of these models include unknown parameters that must be estimated simultaneously. Available data are phenotypes along family pedigrees but no genotypic data. We compare classical fit methods based on statistical summaries of the data and a neural network approach. We show the performance of our algorithms on simulated datasets of reasonable size. We also consider real data concerning breast/ovarian cancer.