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Biology subjects

Norman, U.

Publications and source records attributed to Norman, U..

2 recordsLinked to original sources

Novel genes for autism implicate both excitatory and inhibitory cell lineages in risk

We present the largest exome sequencing study to date focused on rare variation in autism spectrum disorder (ASD) (n=35,584). Integrating de novo and case-control variation with an enhanced Bayesian framework incorporating evolutionary constraint against mutation, we implicate 99 genes in ASD risk at a false discovery rate (FDR) [≤] 0.1. Of these 99 risk genes, 46 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 50 show higher frequencies in individuals ascertained for ASD, and comparing ASD cases with disruptive mutations in the two groups shows differences in phenotypic presentation. Expressed early in brain development, most of the risk genes have roles in neuronal communication or regulation of gene expression, and 12 fall within recurrent copy number variant loci. In human cortex single-cell gene expression data, expression of the 99 risk genes is also enriched in both excitatory and inhibitory neuronal lineages, implying that disruption of these genes alters the development of both neuron types. Together, these insights broaden our understanding of the neurobiology of ASD.

genetics↗

Spatio-Temporal Gene Discovery For Autism Spectrum Disorder

Whole exome sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited in power. Here, we present a spatio-temporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a variant of prize-collecting Steiner forest-based problem on coexpression networks to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of earlyand mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the prediction power: Predicted clusters are hit more and show higher enrichment in ASD-related functions compared to the state-of-the-art. Code is available at http://ciceklab.cs.bilkent.edu.tr/ST-Steiner/.

bioinformatics↗