Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling

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 in which all 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 contralateral breast cancer and the 1398 controls with unilateral breast cancer. The model identified 3 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 three bioinformatic predictors demonstrated a stronger risk signal. This group included 5 of 6 missense variants that were classified as deleterious at the outset by conventional criteria. The remaining 6 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.

A Powerful and Adaptive Association Test for Rare Variants

“…However, we emphasize that, in analysis of multiple RVs, there exist nonassociated RVs. For example, in cancer research it has been observed that the vast majority of RVs do not appear to confer risk (Capanu et al 2011). Hence, it is important to assess the performance of a test in the presence of nonassociated RVs in the group of the RVs to be tested.…”

“…Hence, developing new association tests tailored to RVs has been an active research area in the past few years. Due to low MAFs of RVs, to achieve practically meaningful power, the majority of existing approaches focus on testing on a group of RVs, rather than on each individual RV (Capanu et al 2011); the main idea is to boost power through aggregating information across multiple RVs in an analysis unit, such as a gene (e.g., Morgenthaler and Thilly 2007;Li and Leal 2008;Madsen and Browning 2009;Liu and Leal 2010;Han and Pan 2010;Hoffmann et al 2010;Li et al 2010;Price et al 2010;Zhang et al 2010;Zhu et al 2010;Luo et al 2011;Neale et al 2011;Ionita-Laza et al 2011;Feng et al 2011;Basu and Pan 2011;Gordon et al 2011;Wu et al 2011;Fan et al 2013). As theoretically shown (Cox and Hinkley 1974) and demonstrated in our simulations, there is no uniformly most-power test for this purpose, which means that, depending on the unknown truth, including specific association effect directions and sizes, a given and fixed test may or may not be powerful.…”

Incorporating model uncertainty in detecting rare variants: the Bayesian risk index

“…Previously, we have shown that when rare sequence variants known to have a deleterious effect are combined, the risk of developing a second primary breast cancer increased over fourfold [Malone et al, 2010]. In more recent work using established bioinformatic prediction tools and hierarchical modeling, we have identified a set of individual rare BRCA1 and BRCA2 missense variants likely to be deleterious [Capanu et al, 2011]. To demonstrate the use of the Bayesian risk index methodology with the aim of identifying additional individual rare variants while also investigating the aggregate effect on risk of developing contralateral breast cancer, below we investigate a subset of 134 rare BRCA1 variants (MAF<0.05).…”

Commercial Opportunities and Ethical Pitfalls in Personalized Medicine: A Myriad of Reasons to Revisit the Myriad Genetics Saga

“…Nevertheless, finding accurate and efficient methods to classify VUSs is becoming an important health issue, since their interpretation is necessary to guide genetic counsellors in identifying risk levels and choosing appropriate management strategies [32, 47, 50, 51]. This analysis typically requires knowledge of VUS frequencies in different populations and ethnicities, knowledge of, detailed pedigrees indicating co-segregation and co-occurrence in family members, knowledge about co-occurrence between VUSs and known pathogenic mutations, tumour immunohistochemistry, and functional analysis when possible [52].…”