Genetic evidence and integration of various data sources for classifying uncertain variants into a single model.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Goldgar, DE; Easton, DF; Byrnes, GB; Spurdle, AB; Iversen, ES; Greenblatt, MS; IARC Unclassified Genetic Variants Working Group,

Published Date

  • November 2008

Published In

Volume / Issue

  • 29 / 11

Start / End Page

  • 1265 - 1272

PubMed ID

  • 18951437

Pubmed Central ID

  • 18951437

Electronic International Standard Serial Number (EISSN)

  • 1098-1004

International Standard Serial Number (ISSN)

  • 1059-7794

Digital Object Identifier (DOI)

  • 10.1002/humu.20897

Language

  • eng