Hierarchical Modeling for Estimating Relative Risks of Rare Genetic Variants: Properties of the Pseudo-Likelihood Method

• Activity Number: 268
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Marinela Capanu*+ and Colin B Begg
• Companies: Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center
• Keywords: rare variants ; hierarchical models ; pseudo-likelihood ; Bayesian ; genetic risk

Abstract

Many major genes have been identified that strongly influence the risk of cancer. It is critical to identify which specific mutations occurring in a gene are harmful, and which ones are harmless, so that individuals who learn from genetic testing that they have a mutation can be appropriately counseled. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little evidence available about each individual mutation. In an earlier article (Capanu et al. 2008) we demonstrated the feasibility of using hierarchical modeling based on pseudo-likelihood estimation to obtain the relative risks of the individual rare variants using data from a case-control study. In this talk we use simulations to study in detail the properties of the pseudo-likelihood method as well as of a hybrid pseudo-likelihood approach with Bayesian estimation of the variance component. The results indicate that the hybrid approach is promising, with low bias and with coverage rates for the individual estimates falling under 90% only for the more sparse variants in models with larger residual variance.