Genetic variants improve breast cancer risk prediction on mammograms.

Journal Article (Journal Article)

Several recent genome-wide association studies have identified genetic variants associated with breast cancer. However, how much these genetic variants may help advance breast cancer risk prediction based on other clinical features, like mammographic findings, is unknown. We conducted a retrospective case-control study, collecting mammographic findings and high-frequency/low-penetrance genetic variants from an existing personalized medicine data repository. A Bayesian network was developed using Tree Augmented Naive Bayes (TAN) by training on the mammographic findings, with and without the 22 genetic variants collected. We analyzed the predictive performance using the area under the ROC curve, and found that the genetic variants significantly improved breast cancer risk prediction on mammograms. We also identified the interaction effect between the genetic variants and collected mammographic findings in an attempt to link genotype to mammographic phenotype to better understand disease patterns, mechanisms, and/or natural history.

Full Text

Duke Authors

Cited Authors

  • Liu, J; Page, D; Nassif, H; Shavlik, J; Peissig, P; McCarty, C; Onitilo, AA; Burnside, E

Published Date

  • 2013

Published In

Volume / Issue

  • 2013 /

Start / End Page

  • 876 - 885

PubMed ID

  • 24551380

Pubmed Central ID

  • PMC3900221

Electronic International Standard Serial Number (EISSN)

  • 1942-597X


  • eng

Conference Location

  • United States