Article

BayesMendel: an R Environment for Mendelian Risk Prediction

The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, USA.
Statistical Applications in Genetics and Molecular Biology (Impact Factor: 1.52). 02/2004; 3(1):Article21. DOI: 10.2202/1544-6115.1063
Source: PubMed

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.

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    • "For the breast–ovarian cancer syndrome (Claus et al., 1996), the popular Mendelian model BRCAPRO estimates the probability that a consultand carries a deleterious mutation in the BRCA1 and BRCA2 genes, based on family history of breast and ovarian cancer (see Berry et al., 1997; Parmigiani, Berry, and Aguilar, 1998). Another example is CRCAPRO, which computes the probability of carrying a mutation in the genes MLH1 and MSH2 given family history of colorectal and endometrial cancer (Chen et al., 2004). "
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    ABSTRACT: People with familial history of disease often consult with genetic counselors about their chance of carrying mutations that increase disease risk. To aid them, genetic counselors use Mendelian models that predict whether the person carries deleterious mutations based on their reported family history. Such models rely on accurate reporting of each member's diagnosis and age of diagnosis, but this information may be inaccurate. Commonly encountered errors in family history can significantly distort predictions, and thus can alter the clinical management of people undergoing counseling, screening, or genetic testing. We derive general results about the distortion in the carrier probability estimate caused by misreported diagnoses in relatives. We show that the Bayes factor that channels all family history information has a convenient and intuitive interpretation. We focus on the ratio of the carrier odds given correct diagnosis versus given misreported diagnosis to measure the impact of errors. We derive the general form of this ratio and approximate it in realistic cases. Misreported age of diagnosis usually causes less distortion than misreported diagnosis. This is the first systematic quantitative assessment of the effect of misreported family history on mutation prediction. We apply the results to the BRCAPRO model, which predicts the risk of carrying a mutation in the breast and ovarian cancer genes BRCA1 and BRCA2.
    Biometrics 07/2006; 62(2):478-87. DOI:10.1111/j.1541-0420.2005.00488.x · 1.52 Impact Factor
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    • "In particular, knowing one's probability of carrying an inherited deleterious mutation on the MSH2 and MLH1 genes prior to genetic testing by mutation analysis is important for decision making about genetic testing, disease prophylaxis, family planning, and more. As many colorectal cancer patients undergo MSI testing before mutation analysis, pretest carrier probabilities need to incorporate information about MSI testing, as done in commonly used prediction software such as BayesMendel (Chen et al., 2004). This requires estimates of the sensitivity of MSI, defined as the probability of a subject's tumor sample being microsatellite unstable (MSI = 1) given he/she is carrying a deleterious germline mutation of MSH2 or MLH1 (Mut = 1), and of the specificity of MSI, defined as the probability of the subject's tumor sample being MSS (MSI = 0) given he/she is not carrying a mutation (Mut = 0). "
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    ABSTRACT: Microsatellite instability (MSI) testing is a common screening procedure used to identify families that may harbor mutations of a mismatch repair (MMR) gene and therefore may be at high risk for hereditary colorectal cancer. A reliable estimate of sensitivity and specificity of MSI for detecting germline mutations of MMR genes is critical in genetic counseling and colorectal cancer prevention. Several studies published results of both MSI and mutation analysis on the same subjects. In this article we perform a meta-analysis of these studies and obtain estimates that can be directly used in counseling and screening. In particular, we estimate the sensitivity of MSI for detecting mutations of MSH2 and MLH1 to be 0.81 (0.73-0.89). Statistically, challenges arise from the following: (a) traditional mutation analysis methods used in these studies cannot be considered a gold standard for the identification of mutations; (b) studies are heterogeneous in both the design and the populations considered; and (c) studies may include different patterns of missing data resulting from partial testing of the populations sampled. We address these challenges in the context of a Bayesian meta-analytic implementation of the Hui-Walter design, tailored to account for various forms of incomplete data. Posterior inference is handled via a Gibbs sampler.
    Biostatistics 08/2005; 6(3):450-64. DOI:10.1093/biostatistics/kxi021 · 2.24 Impact Factor
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    ABSTRACT: Two major types of modelling approaches for risk prediction are \regression-based" and \Mendelian". Regression- based approaches use regression techniques directly on the pedigree data. The test results and specic pedigree features are extracted to constitute the response variables based on clinical and epidemiological knowledge. There exist potential problems such as incomplete usage of biological evidence, restricted application to families meeting the same requirements, and extrapolation based on dicult-to-test assumptions such as linearity and additivity of the predictor terms. In contrast, Mendelian approaches rst estimate \genetic parameters" of the mutations| population prevalence and penetrance, including modiers; they then apply a well-established Bayesian prediction technique to transform the genetic parameters into carrier probabilities. Hereditary Nonpoliposis Colorectal Cancer (HNPCC), or the Lynch syndrome, can be caused by germline mutations on the DNA mismatch repair genes, mainly MLH1 and MSH2. We present a model called CRCAPRO that evaluates the probability that an individual carries a mutation in the MLH1 or MSH2 genes based on his or her detailed family history of colorectal and endometrial cancers, and results of microsatellite instability testing if available. The model makes use of Bayes rules, is built on the principle of Mendelian inheritance of autosomal dominant genes, and assumes known mutation frequencies and penetrances and known MSI test accuracy. As compared to existing guidelines and empirical models, CRCAPRO provides more precise probabilities and higher resolution that will be especially helpful to families of moderate risks, of small sizes, or for which cancer history of some members is dicult to obtain; it also makes possible interpretation of testing results, especially for families testing negative despite a strong cancer history. The model also predicts the risk of developing cancer for unaected individuals. We evaluate the performance of the carrier probability prediction with a validation study. The study has shown that CRCAPRO out-performs the existing regression- based model by Wijnen in terms of renemen t, calibration, and root mean squared error. Software is available at http://astor.som.jhmi.edu/BayesMendel.
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