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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.


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