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: 1.13). 02/2004; 3(1):Article21. DOI: 10.2202/1544-6115.1063
Source: PubMed


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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Available from: Giovanni Parmigiani, Oct 08, 2015
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    • "The trouble of using software is often worthwhile because simple risk tables often do not provide as accurate a carrier probability estimate as computing the carrier probability from a Mendelian model [4]. The freely-available software package BayesMendel [5] allows anyone to implement a Mendelian model and is the computational engine behind BRCAPRO [6,7] and MMRPRO [8]. BRCAPRO estimates the probability that a consultand carries a deleterious mutation in the BRCA1 [MIM 113705] and BRCA2 [MIM 600185] genes, based on family history of breast and ovarian cancer, while MMRPRO computes the probability of carrying a mutation in the DNA mismatch repair genes MLH1 [MIM 120436], MSH2 [MIM 609309], and MSH6 [MIM 600678] given family history of colorectal and endometrial cancer. "
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    ABSTRACT: Mendelian models for predicting who may carry an inherited deleterious mutation of known disease genes based on family history are used in a variety of clinical and research activities. People presenting for genetic counseling are increasingly reporting risk-reducing medical interventions in their family histories because, recently, a slew of prophylactic interventions have become available for certain diseases. For example, oophorectomy reduces risk of breast and ovarian cancers, and is now increasingly being offered to women with family histories of breast and ovarian cancer. Mendelian models should account for medical interventions because interventions modify mutation penetrances and thus affect the carrier probability estimate. We extend Mendelian models to account for medical interventions by accounting for post-intervention disease history through an extra factor that can be estimated from published studies of the effects of interventions. We apply our methods to incorporate oophorectomy into the BRCAPRO model, which predicts a woman's risk of carrying mutations in BRCA1 and BRCA2 based on her family history of breast and ovarian cancer. This new BRCAPRO is available for clinical use. We show that accounting for interventions undergone by family members can seriously affect the mutation carrier probability estimate, especially if the family member has lived many years post-intervention. We show that interventions have more impact on the carrier probability as the benefits of intervention differ more between carriers and non-carriers. These findings imply that carrier probability estimates that do not account for medical interventions may be seriously misleading and could affect a clinician's recommendation about offering genetic testing. The BayesMendel software, which allows one to implement any Mendelian carrier probability model, has been extended to allow medical interventions, so future Mendelian models can easily account for interventions.
    BMC Medical Genetics 02/2007; 8(1):13. DOI:10.1186/1471-2350-8-13 · 2.08 Impact Factor
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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 · 1.57 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 · 2.65 Impact Factor
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