ArticleStatistical applications in genetics and molecular biology2004
BayesMendel: an R environment for Mendelian risk prediction.
Article in Statistical applications in genetics and molecular biology, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 4 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Statistical approaches for meta-analysis of genetic mutation prevalence.Genetic epidemiology · 2021Pooled it
- Comparison of Practice Guidelines, BRCAPRO, and Genetic Counselor Estimates to Identify Germline BRCA1 and BRCA2 Mutations in Pancreatic Cancer.Journal of genetic counseling · 2018Guideline
- Meta-analysis of BRCA1 and BRCA2 penetrance.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2007Pooled it
- Accuracy of MSI testing in predicting germline mutations of MSH2 and MLH1: a case study in Bayesian meta-analysis of diagnostic tests without a gold standard.Biostatistics (Oxford, England) · 2005Pooled it
- Age-based risk estimates for C9orf72RE-related diseases: Theoretical developments and added value for genetic counseling.PLoS genetics · 2026Article
- Evaluating a Mendelian Risk Prediction Model That Aggregates Across Genes and Cancers.Genetic epidemiology · 2026Article
- Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.Genetic epidemiology · 2024Article
- Hereditary Cancer Genetic Testing: 30 Years of Impact on Cancer Care.Delaware journal of public health · 2024Article
- Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Combining Breast Cancer Risk Prediction Models.Cancers · 2023Article
- Statistical methods for Mendelian models with multiple genes and cancers.Genetic epidemiology · 2022Article
- A validation of models for prediction of pathogenic variants in mismatch repair genes.Genetics in medicine : official journal of the American College of Medical Genetics · 2022Article
- PREDICTION OF HEREDITARY CANCERS USING NEURAL NETWORKS.The annals of applied statistics · 2022Article
- Predicting cancer risk based on family history.eLife · 2021Article
- Article
- Practical implementation of frailty models in Mendelian risk prediction.Genetic epidemiology · 2020Article
- A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.Genome research · 2020Article
- Penetrance of Different Cancer Types in Families with Li-Fraumeni Syndrome: A Validation Study Using Multicenter Cohorts.Cancer research · 2020Article
- Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective.Cancer research · 2020Article
- Article
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Authors and funding
5 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.