JSM 2015 Online Program

Online Program Home
My Program

Abstract Details

Activity Number: 420
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #316299 View Presentation
Title: Analysis of Genomic Data via Likelihood Ratio Test in Composite Kernel Machine Regression
Author(s): Ni Zhao* and Michael C. Wu
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Keywords: likelihood ratio test ; kernel machine regression ; multiple kernels

Semiparametric kernel machine regression has emerged as a powerful and flexible tool in genomic studies in which genetic variants are grouped into biologically meaningful entities for association testing. Recent advances have expanded the method to test for the effect of multiple groups of genomic features via a composite kernel that is constructed as a weighted average of multiple kernels. Variance component testing is used to evaluate the significance but requires fixing the weighting parameters or perturbation. In this paper, we focus on the (restricted) likelihood ratio test for kernel machine regression with composite kernels where instead of fixing the weighting parameter, we estimate the weighting parameter by maximizing the likelihood functions through the linear mixed model with multiple variance components. We derive the spectral representation of (R)LRT in linear mixed models with multiple variance components to obtain their finite sample distribution. We conduct extensive simulations to evaluate the power and type I error. Finally, we applied to proposed (R)LRT method to a real study to illustrate our methodology

Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home