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

• Published in: PLoS genetics Vol. 14; no. 4; p. e1007306
• Main Authors: Lancour, Daniel, Naj, Adam, Mayeux, Richard, Haines, Jonathan L, Pericak-Vance, Margaret A, Schellenberg, Gerard D, Crovella, Mark, Farrer, Lindsay A, Kasif, Simon
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.04.2018; Public Library of Science (PLoS)
• Subjects: Acids; Aging; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer's disease; Bioinformatics; Biology and Life Sciences; Computer and Information Sciences; Consortia; Datasets; Datasets as Topic; Epidemiology; Funding; Genetic susceptibility; Genetics; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Genotype & phenotype; Health risk assessment; Humans; Immune response; Laboratories; Medicine; Medicine and Health Sciences; Methods; Neurodegenerative diseases; Protein Interaction Maps; Proteins; PTPN2 protein; Public health; Replication; Reproducibility of Results; Risk Factors; Studies; Supervision; Support Vector Machine; New York; United States > US; Massachusetts; Pennsylvania
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1007306

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.