Journal of Biometrics & Biostatistics

Sophie G Zaloumis

Publications

• Research Article
  Specification of Generalized Linear Mixed Models for Family Data using Markov Chain Monte Carlo Methods
  Author(s): Kris M Jamsen, Sophie G Zaloumis, Katrina J Scurrah and Lyle C GurrinKris M Jamsen, Sophie G Zaloumis, Katrina J Scurrah and Lyle C Gurrin
  
  Statistical models imposed on family data can be used to partition phenotypic variation into components due to sharing of both genetic and environmental risk factors for disease. Generalized linear mixed models (GLMMs) are useful tools for the analysis of family data, but it is not always clear how to specify individual-level regression equations so that the resulting within-family variance-covariance matrix of the phenotype reflects the correlation implied by the relatedness of individuals within families. This is particularly challenging when families are of varying sizes and compositions. In this paper we propose a general approach to specifying GLMMs for family data that uses a decomposition of the within-family variance-covariance matrix of the phenotype to set up a series of regression equations with fixed and random effects that corresponds to an appropriate genetic model. This.. Read More»
  DOI: 10.4172/2155-6180.S1-003

  Abstract PDF