caa a22 4500 378925970 CHVBK 20180305123617.0 cr unu---uuuuu 161128e20040917xx s 000 0 eng 10.2202/1544-6115.1063 doi (NATIONALLICENCE)gruyter-10.2202/1544-6115.1063 BayesMendel: an R Environment for Mendelian Risk Prediction [Elektronische Daten] [Sining Chen, Wenyi Wang, Karl W Broman, Hormuzd A Katki, Giovanni Parmigiani] 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. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Genetics nationallicence Computation nationallicence Chen Sining The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University aut Wang Wenyi The Johns Hopkins Bloomberg School of Public Health aut Broman Karl W. The Johns Hopkins Bloomberg School of Public Health aut Katki Hormuzd A. Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS aut Parmigiani Giovanni The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University aut Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-09-17), 1-19 3:1<1 2004 3 sagmb https://doi.org/10.2202/1544-6115.1063 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1544-6115.1063 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Sining The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University aut NATIONALLICENCE 700 1- Wang Wenyi The Johns Hopkins Bloomberg School of Public Health aut NATIONALLICENCE 700 1- Broman Karl W. The Johns Hopkins Bloomberg School of Public Health aut NATIONALLICENCE 700 1- Katki Hormuzd A. Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS aut NATIONALLICENCE 700 1- Parmigiani Giovanni The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University aut NATIONALLICENCE 773 0- Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-09-17), 1-19 3:1<1 2004 3 sagmb CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter