BayesMendel: An R Environment for Mendelian Risk Prediction

Sining Chen, Wenyi Wang, Karl Broman, Hormuzd Katki and Giovanni Parmigiani
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Sining Chen: The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University
Wenyi Wang: Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Karl Broman: Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Hormuzd Katki: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health & Division of Cancer Epidemiology and Genetics National Cancer Institute, NIH, DHHS
Giovanni Parmigiani: The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

No 1039, Johns Hopkins University Dept. of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: 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.

Date: 2004-08-26
Note: oai:bepress.com:jhubiostat-1039
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