Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

@article{Friedrichs2017PathwayBasedKB,
  title={Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies},
  author={Stefanie Friedrichs and Juliane Manitz and Patricia Burger and Christopher I. Amos and Angela Risch and Jenny Chang-Claude and Heinz-Erich Wichmann and Thomas Kneib and Heike Bickeb{\"o}ller and Benjamin Hofner},
  journal={Computational and Mathematical Methods in Medicine},
  year={2017},
  volume={2017}
}
The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as baseโ€ฆย 
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