Presenter: Jennifer Clark
"Additive Least Squares Kernel Machine Methods for Analysis of Large Scale Genomic Data."
Recent advances in high-throughput biotechnology have culminated in the development of large scale,population based studies for identifying genomic features (e.g. genes, SNPs, CpGs) associated with complex diseases and traits. Understanding an individual’s genomic disposition for particular traits and diseases can provide information towards the development of individualized risk profiles and treatment regimes and simultaneously provides clues as to the underlying biological mechanisms. However, the high dimensionality of the feature space, the limited availability of samples, and our incomplete understanding of how features biologically influence disease impose a grand challenge in analyzing genomic data. To mitigate some of these challenges, we propose two new methods. First, we develop the additive least squares kernel machine approach for nonparametrically modeling and testing the cumulative effect of a group of features (such as multiple biologically related CpGs) while nonparametrically adjusting for complex, nonlinear covariates. Second, building on the additive least squares kernel machine, we develop a novel approach for testing for interactions between two different groups of (biologically related) features. By focusing on multi-feature testing, both approaches reduce the dimensionality of the data, and using the kernel machine framework allows for flexible, possibly nonparametric analysis which is important given our incomplete understanding of how features influence the outcome. The advantages of both of the proposed approaches will be madeevident via empirical investigations as well as real data applications.