| Date |
July 2, 2012 |
| Speaker |
Dr. Luis Carvalho, Assistant Professor, Boston University, USA
|
| Title |
Graph-regularized centroid estimation on a hierarchical Bayesian model for Genome-Wide Association Studies
|
| Abstract |
Genome-wide association studies (GWAS) attempt to determine which genomic
markers (SNPs) are predictors of genetic traits, most commonly human diseases.
In practice, despite the extreme imbalance of having millions of markers
recorded for only a few thousand individuals, it is of great interest to glean
as much information as possible from this type of data. To this end, we
propose a novel Bayesian statistical model that exploits a hierarchical
structure between markers and genes to leverage information between levels and
alleviate the "large p small n" regimen while still attaining a reasonably
complex and realistic model. We further describe a collapsed Gibbs sampler
that takes advantage of particular features of the resulting graphical model
to obtain an efficient sampling procedure. We conduct inference on which
SNPs and genes are associated with the studied trait using graph-regularized
centroid estimation. Finally, we illustrate the proposed model and estimation
procedure on simulated data and offer initial results on real-world data.
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