One for all and all for One: Improving replication of genetic studies through network diffusion.

Abstract

Improving accuracy in genetic studies would greatly accelerate understanding the genetic basis of complex diseases. One approach to achieve such an improvement for risk variants identified by the genome wide association study (GWAS) approach is to incorporate previously known biology when screening variants across the genome. We developed a simple approach for improving the prioritization of candidate disease genes that incorporates a network diffusion of scores from known disease genes using a protein network and a novel integration with GWAS risk scores, and tested this approach on a large Alzheimer disease (AD) GWAS dataset. Using a statistical bootstrap approach, we cross-validated the method and for the first time showed that a network approach improves the expected replication rates in GWAS studies. Several novel AD genes were predicted including CR2, SHARPIN, and PTPN2. Our re-prioritized results are enriched for established known AD-associated biological pathways including inflammation, immune response, and metabolism, whereas standard non-prioritized results were not. Our findings support a strategy of considering network information when investigating genetic risk factors.

• Authors: Daniel Lancour, Adam Naj, Richard Mayeux, Jonathan L Haines, Margaret A Pericak-Vance, Gerard D Schellenberg, Mark Crovella, Lindsay A Farrer, Simon Kasif
• Journal: PLoS genetics (PLoS Genet) Vol. 14 Issue 4 Pg. e1007306 (04 2018) ISSN: 1553-7404 [Electronic] United States
• PMID: 29684019 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Topics

• Alzheimer Disease (genetics, metabolism)
• Datasets as Topic
• Genome-Wide Association Study
• Humans
• Protein Interaction Maps
• Reproducibility of Results
• Risk Factors
• Support Vector Machine