Abstract Details
Activity Number:
|
84
|
Type:
|
Contributed
|
Date/Time:
|
Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Statistics in Epidemiology
|
Abstract #312367
|
|
Title:
|
General Framework for Rare Variant Analysis: Gene-Environment Interaction and Multiple Trait Analysis in Family Samples
|
Author(s):
|
Wei Gao*+ and George T. O'Connor and Josée Dupuis
|
Companies:
|
and Boston University School of Medicine and Boston University School of Public Health
|
Keywords:
|
rare variant analysis ;
multiple trait analysis ;
repeated measures analysis ;
gene-environment interaction ;
family samples ;
linear mixed effects model
|
Abstract:
|
There are many statistical methods for rare variant association analysis. The sequence kernel association test (SKAT) has been shown to be powerful in various scenarios. The family-based SKAT (famSKAT) is an extension of SKAT to account for familial correlation. We developed a general SKAT framework that is applicable to family sample, repeated phenotype measurements and multiple traits. The general SKAT (genSKAT) takes correlations between multiple measurements into consideration in addition to accounting for family structures. SKAT and famSKAT have inflated type I error if correlations between repeated phenotype measurements or multiple traits are inappropriately ignored. In contrast, genSKAT has the correct type I error. We further discussed gene-environment interaction tests under the genSKAT framework. In some studies, each individual has more than one measurement. For complex diseases, the environment exposure and its interaction with the genes may affect more than one trait. The gene effects may also vary over time. The genSKAT takes advantage of multiple traits/measurements and possible gene-environment interactions, and improves power to detect genetic associations.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please 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.
Copyright © American Statistical Association.