In the past, most studies focused on directly associating genetic variants to phenotypes without considering the intermediate layers. With recent advances in acquisition technology, we can now measure the genome, epigenome, and transcriptome among other genomic layers. Combining these data types bring about new statistical challenges. The shear dimensionality of the data introduces a serious multiple testing problem with conventional univariate analysis, especially if one is to examine the interactions between genomic layers. In this talk, we will discuss how kernel machines can be applied to reduce the data dimensionality in a biologically meaningful way. We will also describe how kernel machines can be used to analyze interactions between genomic layers. Further, we will present the concept of multikernel machines and how it enables mediation analysis to be performed.
Speaker Bio: Bernard Ng is a postdoctoral fellow under the Department of Statistics at the University of British Columbia. Prior to his current position, he was a joint postdoctoral fellow at Stanford University and INRIA. His research focuses on statistical method development for neuroimaging and genomics applications. Bernard completed his MASc and PhD in Electrical Engineering at the University of British Columbia and his BASc in Electronics Engineering at Simon Fraser University.