A novel data-driven approach reveals gene networks and biological processes underlying autism

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Leonardo Emberti GialloretiPaolo Curatolo

Abstract

Background: Developments in gene-hunting techniques identified several ASD associated genes. The considerable significance of cluster analysis associated with gene network studies has led to reveal many disrupted key pathways in ASD, even if its genetic underpinnings remain a challenging task. This study aims to determine, through a novel data-driven approach, how networks of mutated genes impact biological processes underlying autism. Methods: We analyzed the VariCarta dataset, which presents more than 200,000 genomic variant events collected from 13,069 people with ASD. Firstly, we created a whole-genome and an exome sequencing subset. Then, for each subset we compared pairwise patients of each group to build “patient similarity matrices”. Hierarchical-agglomerative-clustering and heatmap were performed to identify clusters of patients with common occurrences of gene networks within these matrices. The subsequent enrichment analysis (EA) highlighted biological processes that might be impacted by the mutated genes of each subgroup. Results: Considering the whole-genome matrix, we identified three main genetic clusters of ASD patients, each one characterized by a network of shared genetic variants. We isolated 11,609 genetic va...Continue Reading

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