Nonparametric Bayes Modeling for Jointly Analyzing Family and Unrelated Data

• Activity Number: 664
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Chuanhua Xing*+ and Andrew Allen and Yi-Ju Li
• Companies: Boston University and Duke University and Duke University
• Keywords: Nonparametric Bayes modeling ; Joint analysis of family and unrelated data ; Genetic risk prediction ; Sample size ; Correlation ; Population stratification

Abstract

The joint analysis of family-based and unrelated data enlarges the sample size and therefore secures the effective power for studies. Family-based data have the advantage of increasing the chance to detect true risk-variants but are limited due to the difficulty to recruit enough people. Such a problem can become even more serious in emerging next-generation sequencing data due to increased cost. We firstly propose a novel Bayesian model for the unified analysis of family-based and population-based unrelated data. We adopt a matched case-control design within a conditional likelihood framework to account for the ascertainment effect in family data and the population stratification inherent in unrelated data. Nonparametric Bayesian model does not rely any known information on parameters, but automatically adapts data to handle the family/stratum specific parameters. Our model can flexibly incorporate the correlation and variance components parameters into the analysis of any family structure. The studies indicate better estimates than family-based data only, and have much greater efficiency than conditional likelihood model.