Genetic Variance Components Estimation For Binary Traits Using Multiple Related Individuals

• Activity Number: 190
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Charalampos Papachristou*+ and Mark Abney and Carole Ober
• Companies: University of the Sciences and The University of Chicago and The University of Chicago
• Keywords: Binary Trait ; Genetic Variance Components ; GLMMs ; MCEM ; Diabetes ; Complex Pedigrees

Abstract

We propose a likelihood approach, developed in the context of generalized linear mixed models, for modeling dichotomous traits based on data from hundreds of individuals all of whom are potentially correlated through either a known pedigree, or an estimated covariance matrix. The advantage of our formulation is that it easily incorporates information from pertinent covariates as fixed effects and at the same time it takes into account the correlation between individuals that share genetic background or other random effects. The high dimensionality of the integration involved in the likelihood prohibits exact computations. Instead, an automated Monte Carlo expectation maximization algorithm is employed for obtaining the maximum likelihood estimates of the model parameters. Through a simulation study we demonstrate that our method can provide reliable estimates of the model parameters for sample sizes close to 500. Implementation of our method to data from a pedigree of 491 Hutterites evaluated for Type 2 diabetes (T2D) reveals evidence of a strong genetic component to T2D risk, particularly for younger and leaner cases.