Effect of Misreported Family History on Mendelian Mutation Prediction Models

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\u27s 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 vs. 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

BayesMendel: An R Environment for Mendelian Risk Prediction

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

Nonparametric Adjustment for Measurement Error in Time to Event Data

Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our adjusted method mitigates the effects of measurement error mainly by improving calibration and by improving total accuracy. In some scenarios discrimination is also improved

Extending Mendelian Risk Prediction Models to Handle Misreported Family History

Mendelian risk prediction models calculate the probability of a proband being a mutation carrier based on family history and known mutation prevalence and penetrance. Family history in this setting, is self-reported and is often reported with error. Various studies in the literature have evaluated misreporting of family history. Using a validation data set which includes both error-prone self-reported family history and error-free validated family history, we propose a method to adjust for misreporting of family history. We estimate the measurement error process in a validation data set (from University of California at Irvine (UCI)) using nonparametric smoothed Kaplan-Meier estimators, and use Monte Carlo integration to implement the adjustment. In this paper, we extend BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancers, to adjust for misreporting in family history. We apply the extended model to data from the Cancer Genetics Network (CGN)

Chemokine CCL2 and its receptor CCR2 are increased in the hippocampus following pilocarpine-induced status epilepticus

<p>Abstract</p> <p>Background</p> <p>Neuroinflammation occurs after seizures and is implicated in epileptogenesis. CCR2 is a chemokine receptor for CCL2 and their interaction mediates monocyte infiltration in the neuroinflammatory cascade triggered in different brain pathologies. In this work CCR2 and CCL2 expression were examined following status epilepticus (SE) induced by pilocarpine injection.</p> <p>Methods</p> <p>SE was induced by pilocarpine injection. Control rats were injected with saline instead of pilocarpine. Five days after SE, CCR2 staining in neurons and glial cells was examined using imunohistochemical analyses. The number of CCR2 positive cells was determined using stereology probes in the hippocampus. CCL2 expression in the hippocampus was examined by molecular assay.</p> <p>Results</p> <p>Increased CCR2 was observed in the hippocampus after SE. Seizures also resulted in alterations to the cell types expressing CCR2. Increased numbers of neurons that expressed CCR2 was observed following SE. Microglial cells were more closely apposed to the CCR2-labeled cells in SE rats. In addition, rats that experienced SE exhibited CCR2-labeling in populations of hypertrophied astrocytes, especially in CA1 and dentate gyrus. These CCR2+ astroctytes were not observed in control rats. Examination of CCL2 expression showed that it was elevated in the hippocampus following SE.</p> <p>Conclusion</p> <p>The data show that CCR2 and CCL2 are up-regulated in the hippocampus after pilocarpine-induced SE. Seizures also result in changes to CCR2 receptor expression in neurons and astrocytes. These changes might be involved in detrimental neuroplasticity and neuroinflammatory changes that occur following seizures.</p

Incorporating medical interventions into carrier probability estimation for genetic counseling

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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

Oral leukoplakia and risk of progression to oral cancer: A population-based cohort study

BACKGROUND: The optimal clinical management of oral precancer remains uncertain. We investigated the natural history of oral leukoplakia, the most common oral precancerous lesion, to estimate the relative and absolute risks of progression to cancer, the predictive accuracy of a clinician\u27s decision to biopsy a leukoplakia vis-à-vis progression, and histopathologic predictors of progression. METHODS: We conducted a retrospective cohort study (1996-2012) of patients with oral leukoplakia (n = 4886), identified using electronic medical records within Kaiser Permanente Northern California. Among patients with leukoplakia who received a biopsy (n = 1888), we conducted a case-cohort study to investigate histopathologic predictors of progression. Analyses included indirect standardization and unweighted or weighted Cox regression. RESULTS: Compared with the overall Kaiser Permanente Northern California population, oral cancer incidence was substantially elevated in oral leukoplakia patients (standardized incidence ratio = 40.8, 95% confidence interval [CI] = 34.8 to 47.6; n = 161 cancers over 22 582 person-years). Biopsied leukoplakias had a higher oral cancer risk compared with those that were not biopsied (adjusted hazard ratio = 2.38, 95% CI = 1.73 to 3.28). However, to identify a prevalent or incident oral cancer, the biopsy decision had low sensitivity (59.6%), low specificity (62.1%), and moderate positive-predictive value (5.1%). Risk of progression to oral cancer statistically significantly increased with the grade of dysplasia; 5-year competing risk-adjusted absolute risks were: leukoplakia overall = 3.3%, 95% CI = 2.7% to 3.9%; no dysplasia = 2.2%, 95% CI = 1.5% to 3.1%; mild-dysplasia = 11.9%, 95% CI = 7.1% to 18.1%; moderate-dysplasia = 8.7%, 95% CI = 3.2% to 17.9%; and severe dysplasia = 32.2%, 95% CI = 8.1%-60.0%. Yet 39.6% of cancers arose from biopsied leukoplakias without dysplasia. CONCLUSIONS: The modest accuracy of the decision to biopsy a leukoplakia vis-à-vis presence or eventual development of oral cancer highlights the need for routine biopsy of all leukoplakias regardless of visual or clinical impression. Leukoplakia patients, particularly those with dysplasia, need to be closely monitored for signs of early cancer

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA