Genetic evidence and integration of various data sources for classifying uncertain variants into a single model
| Autor: | Goldgar, D.E., Easton, D.F., Byrnes, G.B., Spurdle, A.B., Iversen, E.S., Greenblatt, M.S., Boffetta, P., Couch, F.J., Wind, N. de, Eccles, D., Foulkes, W.D., Genuardi, M., Hofstra, R.M., Hogervorst, F., Hoogerbrugge-van der Linden, N., Plon, S.E., Radice, P., Rasmussen, L., Sinilnikova, O.M., Tavtigian, S.V. |
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| Rok vydání: | 2008 |
| Předmět: |
Genotype
Genetics and epigenetic pathways of disease [NCMLS 6] Process (engineering) Bayesian probability Posterior probability Computational biology Biology Models Biological Sensitivity and Specificity Article Molecular epidemiology [NCEBP 1] Bayes' theorem Neoplastic Syndromes Hereditary Risk Factors Translational research [ONCOL 3] Prior probability Genetics medicine Humans Genetic Predisposition to Disease Genetic Testing Genetics (clinical) Genetic testing Likelihood Functions Data collection medicine.diagnostic_test Hereditary cancer and cancer-related syndromes [ONCOL 1] Data Collection Uncertainty Genetic Variation Bayes Theorem Mixture model Phenotype Genetic defects of metabolism [UMCN 5.1] Case-Control Studies |
| Zdroj: | Human Mutation, 29, 11, pp. 1265-72 Human Mutation, 29, 1265-72 |
| ISSN: | 1059-7794 |
| Popis: | Contains fulltext : 70182.pdf (Publisher’s version ) (Closed access) Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process. |
| Databáze: | OpenAIRE |
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