Project Example: Network-based kernel for the analysis of genome-wide association studies

Research Objective

The analysis of genome-wide association studies is the standard tool to identify a genetic causes of chronic diseases. In this project, a network-based kernel was developed that converts the genomic information of two individuals into a quantitative value reflecting their genetic similarity. The kernel integrates the network structure of biological pathways as prior knowledge into the analysis. Here, a pathway is defined as a network of interacting genes responsible for achieving a specific cell function or regulation. The benefit is the potential interpretation of the disease association in a biological context and reduction of a number of statistical problems. The approach is exemplified to genome-wide association case-control data on lung cancer and rheumatoid arthritis. Some promising new pathways associated with these diseases are identified, which may improve our current understanding of the genetic mechanisms.

Statistical Methodology


Open-source software as R package kangar00 (Kernel Approaches for Non-linear Genetic Association Regression) is available on request. The package allows parallel computing of kernel matrices using the power of graphics processing unit (GPU).

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