Abstract Details
Activity Number:
|
91
|
Type:
|
Contributed
|
Date/Time:
|
Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #309646 |
Title:
|
Dominance Modeling for GWAS Hit Regions with Generalized Resample Model Averaging
|
Author(s):
|
Jeremy Sabourin*+ and Andrew Nobel and William Valdar
|
Companies:
|
UNC and UNC-CH and UNC-CH Genetics
|
Keywords:
|
GWAS ;
resampling ;
LASSO ;
dominance ;
hit region
|
Abstract:
|
Significance testing one SNP at a time has proven useful for identifying genomic regions that harbor variants affecting human disease. In theory, simultaneous modeling of multiple loci should help. However, they are typically applied in an ad hoc fashion: conditioning on the top SNPs, with limited exploration of the model space and no assessment of how sensitive model choice was to sampling variability. Formal alternatives exist but are seldom used. When considering complex traits in humans, the genetic model is most often assumed to be additive only SNP effects. When non-additive effects such as dominance or overdominance are present, additive only models can be underpowered. We present LLARRMA-dawg, a generalized resample model averaging based method using the group LASSO that allows for additive and non-additive SNP effects. It estimates for each SNP, the probability that it would be included in a multi- SNP model in alternative realizations of the data. We show that under simulations based on real GWAS data, that LLARRMA-dawg identifies a set of candidates that is enriched for causal loci relative to single locus analysis.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education 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.