@article{LancourEtAl:PLoSGenetics18,
  author	= {Lancour, Daniel and Naj, Adam and Mayeux, Richard and Haines, Jonathan L. and Pericak-Vance, Margaret A. and Schellenberg, Gerard C. and Crovella, Mark and Farrer, Lindsay A. and Kasif, Simon},
  journal	= {PLoS Genetics},
  publisher	= {Public Library of Science},
  title		= {One for all and all for One: Improving replication of genetic studies through network diffusion},
  year		= {2018},
  month		= {04},
  volume	= {14},
  URL		= {https://doi.org/10.1371/journal.pgen.1007306},
  pages		= {1--20},
  abstract	= {Integrating multiple types of -omics data is a rapidly growing research area due in part to the increasing amount of diverse and publicly accessible data. In this study, we demonstrated that integration of genetic association and protein interaction data using a network diffusion approach measurably improves reproducibility of top candidate genes. Application of this approach to Alzheimer disease (AD) using a large dataset assembled by the Alzheimer's Disease Genetics Consortium identified several novel candidate AD genes that are supported by pre-existing knowledge of AD pathobiology. Our findings support a strategy of considering network information when investigating genetic risk factors. Finally, we developed a transparent and easy-to-use R package that can facilitate the extension of our methodology to other phenotypes for which genetic data are available.},
  number	= {4},
  doi		= {10.1371/journal.pgen.1007306},
}