The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations

Abstract

Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate the bias, global population studies, such as the 1,000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How much of these differences would attribute to the population-specific traits? To answer this question, the mutation data must be linked with the functional outcomes. A new "edgotype" concept has been proposed that emphasizes the interaction-specific, "edgetic", perturbations caused by mutations in the interacting proteins. In this work, we performed a systematic in-silico edgetic profiling of [~]50,000 non-synonymous SNVs (nsSNVs) from 1,000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with the interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from the healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in-silico edgetic study and reveals insights into the orchestrated play of the population-specific mutations in the human interactome.