Journal article
Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling
Genetic epidemiology, Vol.35(5), pp.389-397
07/2011
DOI: 10.1002/gepi.20587
PMCID: PMC3111904
PMID: 21520273
Abstract
Current evidence suggests that the genetic risk of breast cancer may be caused primarily by rare variants. However, while classification of protein-truncating mutations as deleterious is relatively straightforward, distinguishing as deleterious or neutral the large number of rare missense variants is a difficult on-going task. In this article, we present one approach to this problem, hierarchical statistical modeling of data observed in a case-control study of contralateral breast cancer (CBC) in which all the participants were genotyped for variants in BRCA1 and BRCA2. Hierarchical modeling permits leverage of information from observed correlations of characteristics of groups of variants with case-control status to infer with greater precision the risks of individual rare variants. A total of 181 distinct rare missense variants were identified among the 705 cases with CBC and the 1,398 controls with unilateral breast cancer. The model identified three bioinformatic hierarchical covariates, align-GV, align-GD, and SIFT scores, each of which was modestly associated with risk. Collectively, the 11 variants that were classified as adverse on the basis of all the three bioinformatic predictors demonstrated a stronger risk signal. This group included five of six missense variants that were classified as deleterious at the outset by conventional criteria. The remaining six variants can be considered as plausibly deleterious, and deserving of further investigation (BRCA1 R866C; BRCA2 G1529R, D2665G, W2626C, E2663V, and R3052W). Hierarchical modeling is a strategy that has promise for interpreting the evidence from future association studies that involve sequencing of known or suspected cancer genes.
Details
- Title: Subtitle
- Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling
- Creators
- Marinela Capanu - Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USAPatrick ConcannonRobert W HaileLeslie BernsteinKathleen E MaloneCharles F LynchXiaolin LiangSharon N TeraokaAnh T DiepDuncan C ThomasJonine L BernsteinColin B BeggWECARE Study Collaborative Group
- Resource Type
- Journal article
- Publication Details
- Genetic epidemiology, Vol.35(5), pp.389-397
- DOI
- 10.1002/gepi.20587
- PMID
- 21520273
- PMCID
- PMC3111904
- NLM abbreviation
- Genet Epidemiol
- ISSN
- 0741-0395
- eISSN
- 1098-2272
- Publisher
- United States
- Grant note
- R01 CA129639 / NCI NIH HHS R01 CA131010-03 / NCI NIH HHS CA083178 / NCI NIH HHS U01 CA083178 / NCI NIH HHS R01 AG014358 / NIA NIH HHS CA131010 / NCI NIH HHS R01 CA097397 / NCI NIH HHS CA097397 / NCI NIH HHS R01 ES019876 / NIEHS NIH HHS R01 CA131010 / NCI NIH HHS
- Language
- English
- Date published
- 07/2011
- Academic Unit
- Epidemiology
- Record Identifier
- 9983995031902771
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