Arjun Krishnan | Search bioRxiv

bioRxiv2016

Integrative networks illuminate biological factors underlying gene-disease associations

A.Integrative networks combine multiple layers of biological data into a model of how genes work together to carry out cellular processes. Such networks become more valuable as they become more context specific, for example, by capturing how genes work together in a certain tissue or cell type. Once constructed, these networks provide the means to identify broad biological patterns underlying genes associated with complex traits and diseases. In this review, we discuss the different types of integrative networks that currently exist and how such networks that encompass multiple biological layers are constructed. We highlight how specificity can be incorporated into the reconstruction of different types of biomolecular interactions between genes, using tissue-specificity as a motivating example. We identify examples of cases where networks have been applied to study human diseases and discuss opportunities for new applications.

Bioinformatics↗

bioRxiv2016

Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder

Autism spectrum disorder (ASD) is a range of major neurodevelopmental disabilities with a strong genetic basis. Yet, owing to extensive genetic heterogeneity, multiple modes of inheritance and limited study sizes, sequencing and quantitative genetics approaches have had limited success in characterizing the complex genetics of ASD. Currently, only a small fraction of potentially causal genes--about 65 genes out of an estimated severalhundred--are known based on strong genetic evidence. Hence, there isa critical need for complementary approaches to further characterize the genetic basis of ASD, enabling development of better screening and therapeutics. Here, we use a machine-learning approach based on a human brain-specific functional gene interaction network to present a genome-wide prediction of autism-associated genes, including hundreds of candidate genes for which there is minimal or no prior genetic evidence. Our approach is validated in an independent case-control sequencing study of approximately 2,500families. Leveraging these genome-wide predictions and the brain-specificnetwork, we demonstrate that the large set of ASD genes converges on a smaller number of key cellular pathways and specific developmental stages of the brain. Specifically, integration with spatiotemporal transcriptome expression data implicates early fetal and midfetal stages of the developing human brain in ASD etiology. Likewise, analysis of the connectivity of topautism genes in the brain-specific interaction network reveals the breadthof autism-associated functional modules, processes, and pathways in the brain. Finally, we identify likely pathogenic genes within the most frequent autism-associated copy-number-variants (CNVs) and propose genes and pathways that are likely mediators of autism across multiple CNVs. All the predictions, interactions, and functional insights from this work are available to biomedical researchers at asd.princeton.edu.

Genomics↗