Abstract
Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.
Highlights
Genome-wide association studies (GWAS) have greatly facilitated the identification of genes involved in complex phenotypes [1,2]
Summary of the statistical approach Network Interface Miner for Multigenic Interactions (NIMMI) is a network-based approach that relies on three basic assumptions: 1) Genes, rather than SNPs are the functional units in biology; 2) Genes do not work in isolation, genes whose protein products show more interactions with other proteins in the same network are more important than proteins with fewer interactions; and 3) genetic association results for a trait and protein interactions within a network are complementary forms of information, reflecting a role for that network in that trait [36,37,38]
We assumed that each gene corresponds to a single protein and used human protein-protein interaction (PPI) data to build the networks, but in principle any data that relates one gene to another could be used
Summary
Genome-wide association studies (GWAS) have greatly facilitated the identification of genes involved in complex phenotypes [1,2]. Many studies have shown that there is a strong relationship between gene function and phenotype, and that functionally-related genes are more likely to interact [7,8,9,10,11,12,13,14,15,16,17,18] Inspired by this insight, we undertook a systems-biology approach to identify and prioritize groups of functionally-related genes that are enriched for genetic variants associated with a trait, what we call ‘trait prioritized sub-networks.’. Since signals with small effects not detectable at conventional levels of significance may account for substantial heritability [34], methods that can include all signals without arbitrary thresholds of statistical significance are needed Such methods should extract more information from GWAS data by identifying susceptibility genes that have functional similarity. Replicated findings are more likely to generate sound biological hypotheses for subsequent laboratory studies
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