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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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BayesMendel: an R Environment for Mendelian Risk Prediction

Sining Chen1 / Wenyi Wang2 / Karl W Broman3 / Hormuzd A Katki4 / Giovanni Parmigiani5

1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University

2The Johns Hopkins Bloomberg School of Public Health

3The Johns Hopkins Bloomberg School of Public Health

4Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS

5The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 3, Issue 1, Pages 1–19, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1063, September 2004

Publication History

Published Online:

Several important syndromes are caused by deleterious germline mutations of individual genes. In both clinical and research applications it is useful to evaluate the probability that an individual carries an inherited genetic variant of these genes, and to predict the risk of disease for that individual, using information on his/her family history. Mendelian risk prediction models accomplish these goals by integrating Mendelian principles and state-of-the-art statistical models to describe phenotype/genotype relationships. Here we introduce an R library called BayesMendel that allows implementation of Mendelian models in research and counseling settings. BayesMendel is implemented in an object-oriented structure in the language R and distributed freely as an open source library. In its first release, it includes two major cancer syndromes: the breast-ovarian cancer syndrome and the hereditary non-polyposis colorectal cancer syndrome, along with up-to-date estimates of penetrance and prevalence for the corresponding genes. Input genetic parameters can be easily modified by users. BayesMendel can also serve as a generic tool for genetic epidemiologists to flexibly implement their own Mendelian models for novel syndromes and local subpopulations, without reprogramming complex statistical analyses and prediction tools.

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