|
Activity Number:
|
136
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 4, 2008 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistics in Epidemiology
|
| Abstract - #300949 |
|
Title:
|
Functional Genetic Models for Unraveling Path from Genomic Information to Complex Phenotypes
|
|
Author(s):
|
Li Luo*+ and John Reveille and Momiao Xiong+
|
|
Companies:
|
The University of Texas School of Public Health and The University of Texas Medical School and The University of Texas School of Public Health
|
|
Address:
|
1200 Herman Pressler, Houston, TX, 77030, 1200 Herman Pressler, Houston, TX, 77030,
|
|
Keywords:
|
functional genetic model ; functional data analysis ; quantitative trait ; genome-wide association studies ; genetic epidemiology ; complex diseases
|
|
Abstract:
|
Traditional quantitative genetics has primarily studied traits as isolated and static variables. Less attention is paid to dynamic behaviors of the traits. However, in real biologic world, many quantities often change over time. Great conceptual and statistical challenges are how to characterize and investigate not only steady-state, but also dynamic behaviors of the biological processes. To meet these challenges, we propose to develop functional genetic models which take traits as random functions and to address the issues about how to develop functional genetic models that use longitudinal intermediate traits as response and predictor variables? The proposed functional genetic models were applied to genome-wide association studies of ankylosing spondylitis (AS) which successfully leads to identification of SNPs, genes and biological networks associated with AS.
|
