A Novel Pairwise Conditional Likelihood Ratio Test in a Semiparametric Model for VQTL Mapping

• Activity Number: 334
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Chuan Hong*+ and Yong Chen and Yang Ning and Peng Wei
• Companies: University of Texas School of Public Health and University of Texas School of Public Health and University of Waterloo and University of Texas School of Public Health
• Keywords: Composite likelihood ; Conditional likelihood ; Exponential tilt model ; Pseudolikelihood ; Semiparametric model ; vQTL

Abstract

Current tests for single locus association with quantitative traits aim at looking for the mean difference and assume equal variances between genotypes or alleles. However, recent research has revealed functional genetic loci that affect the variance of traits, known as variability-controlling quantitative trait locus (vQTL). In addition, it has been suggested that many genotypes have both mean and variance effects, while some of the mean or variance effects alone would not be strong enough to be detected. A novel pairwise conditional likelihood ratio test is proposed to identify both mean and variance effects. By the conditioning technique, the baseline density function is eliminated in the constructed pairwise likelihood function. Hence the impact of unknown baseline density function is minimized. We show that the proposed test has a simple asymptotic chi-square distribution. Simulation studies show that the proposed test performs well in controlling Type I errors and is powerful and robust to model mis-specification. The proposed test is illustrated by an application to Genetic Analysis Workshop (GAW18) data.