Getting to the Cores of Autism

Abstract

The genetic architecture of autism spectrum disorder (ASD) is itself a diverse allelic spectrum that consists of rare de novo or inherited variants in hundreds of genes and common polygenic risk at thousands of loci. ASD susceptibility genes are interconnected at the level of transcriptional and protein networks, and many function as genetic regulators of neurodevelopment or synaptic proteins that regulate neural activity. So that the core underlying neuropathologies can be further elucidated, we emphasize the importance of first defining subtypes of ASD on the basis of the phenotypic signatures of genes in model systems and humans.

Figure 1.. Components of the genetic basis of ASD.

(A) Genetic studies have found conclusive evidence for three categories of genetic risk including polygenic variation that is common in the human population, rare variants which have occurred relatively recently in the population, and de novo mutations which occur spontaneously in offspring. We illustrate all three within a single pedigree, but this depiction does not necessarily represent a typical family, because the contributions of de novo, inherited and polygenic risk varies between individuals. We highlight two specific examples of complex genetic inheritance that have been documented: (B) cases in which risk is attributable to multiple rare variants, for example a rare gene variant (+/−) and a large duplication; and (C) ases in which risk is attributable to a de novo gene mutation (+/−) and an increased load of polygenic risk inherited from both parents. The contribution of each to risk in offspring is represented by line thickness. Seventy percent of de novo mutations originate in the paternal germline and the paternal contribution to de novo mutation is shown to be greater than the maternal contribution. Variability of ASD symptom severity in offspring is represented by the tone of shading of pedigree symbols.

Figure 2.. Forms of gene action in ASD.

Network graphs represent interactions within a gene regulatory networks that are impacted by causal variants. Red nodes represent genes impacted directly by a risk variant in an individual case of ASD (de novo mutations, CNVs or common variants). Pink nodes represent other ASD susceptibility genes that are not mutated in the same individual but do interact closely with the primary gene mutation(s) in the network. (A) A de novo mutation in a key regulatory gene. Many ASD genes encode regulatory proteins that that control the expression of genes in the developing brain. Those target genes include other monogenic ASD genes. Thus, the effect of a single gene mutation can fan out quite broadly through a gene regulatory network. (B) Large CNVs directly alter the dosage of dozens of genes. Thus the network level effects of a CNV are distributed more broadly than the example shown in A. (C) Polygenic risk is very broadly distributed across the genome and throughout gene regulatory networks.

Figure 3.. Points of convergence among ASD susceptibility genes.

Multiple ASD genes interact within the context of gene regulatory networks. These are highlighted as biological processes within a single neuron (cytoplasm in blue and nucleus in pink). Convergence is evident at multiple levels of interaction including DNA binding, RNA binding and Protein-Protein interactions. Biological processes that are associated with ASD genes include the regulation of gene expression and synaptic function. ASD genes are expressed preferentially in the developing brain. Rare gene mutations in ASD also converge upon specific signaling pathways involved in the regulation of ell proliferation and differentiation including mTOR, MAPK and Wnt signaling. [Note: the ideogram (white circle) in the center and the images depicting fetal brain development are stock photos and need to be replaced with original art]

Figure 4.. Defining core gene sets that regulate neurodevelopment based on trait correlations in human cell models.

We propose the characterization of genotype/trait correlations in cell based models as means to define sets of genes and CNVs that have common phenotype profiles which may reflect common effects on neuronal function. We illustrate an experimental pipeline where the effects of multiple gene mutations and CNVs are tested relative to isogenic controls across a series of cellular assays, Likewise, trait correlations with PRS could be tested in patient derived lines. In this manner genes, CNVs and common variants could be clustered into discrete groups. Comparing patterns of trait correlation in cell models and in clinical phenotype data (not shown) could help to identify clinical subtypes of ASD share common neuropathologies. (the heatmap in the lower right is a borrowed image. We need to create something similar from scratch and we need to increase the resolution of the 3^rd image).