Phenotypic effects of genetic variants associated with autism.

While over 100 genes have been associated with autism, little is known about the prevalence of variants affecting them in individuals without a diagnosis of autism. Nor do we fully appreciate the phenotypic diversity beyond the formal autism diagnosis. Based on data from more than 13,000 individuals with autism and 210,000 undiagnosed individuals, we estimated the odds ratios for autism associated to rare loss-of-function (LoF) variants in 185 genes associated with autism, alongside 2,492 genes displaying intolerance to LoF variants. In contrast to autism-centric approaches, we investigated the correlates of these variants in individuals without a diagnosis of autism. We show that these variants are associated with a small but significant decrease in fluid intelligence, qualification level and income and an increase in metrics related to material deprivation. These effects were larger for autism-associated genes than in other LoF-intolerant genes. Using brain imaging data from 21,040 individuals from the UK Biobank, we could not detect significant differences in the overall brain anatomy between LoF carriers and non-carriers. Our results highlight the importance of studying the effect of the genetic variants beyond categorical diagnosis and the need for more research to understand the association between these variants and sociodemographic factors, to best support individuals carrying these variants.

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutations-ASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.

A systematic variant annotation approach for ranking genes associated with autism spectrum disorders.

BACKGROUND: The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance. METHODS: Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene. RESULTS: There was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (RSpearman = 0.40-0.63; p< 0.0001), providing support for our scoring algorithm. CONCLUSIONS: This new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance.

Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.

Neocortical neurogenesis and the etiology of autism spectrum disorder.

Researchers have now identified many highly penetrant genetic risk factors for autism spectrum disorder (ASD). Some of these genes encode synaptic proteins, lending support to the hypothesis that ASD is a disorder of synaptic homeostasis. Less attention, however, has been paid to the genetic risk factors that converge on events that precede synaptogenesis, including the proliferation of neural progenitor cells and the migration of neurons to the appropriate layers of the developing neocortex. Here I review this evidence, focusing on studies of mutant mouse phenotypes, human postmortem data, systems biological analyses, and non-genetic risk factors. These findings highlight embryonic neurogenesis as a potentially important locus of pathology in ASD. In some instances, this pathology may be driven by alterations in chromatin biology and canonical Wnt signaling, which in turn affect fundamental cellular processes such as cell-cycle length and cell migration. This view of ASD suggests the need for a better understanding of the relationship between variation in neuron number, laminar composition, and the neural circuitry most relevant to the disorder.

SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs).

New technologies enabling genome-wide interrogation have led to a large and rapidly growing number of autism spectrum disorder (ASD) candidate genes. Although encouraging, the volume and complexity of these data make it challenging for scientists, particularly non-geneticists, to comprehensively evaluate available evidence for individual genes. Described here is the Gene Scoring module within SFARI Gene 2.0 (https://gene.sfari.org/autdb/GS_Home.do), a platform developed to enable systematic community driven assessment of genetic evidence for individual genes with regard to ASD.

Targeted disruption of the mouse cis-retinol dehydrogenase gene: visual and nonvisual functions.

It has been proposed that cis-retinol dehydrogenase (cRDH) acts within the body to catalyze the oxidation of 9-cis-retinol, an oxidative step needed for 9-cis-retinoic acid synthesis, the oxidation of 11-cis-retinol [an oxidative step needed for 11-cis-retinal (visual chromophore) synthesis], and 3 alpha-hydroxysteroid transformations. To assess in vivo the physiological importance of each of these proposed actions of cRDH, we generated cRDH-deficient (cRDH-/-) mice. The cRDH-/- mice reproduce normally and appear to be normal. However, the mutant mice do have a mild visual phenotype of impaired dark adaptation. This phenotype is evidenced by electroretinagram analysis of the mice and by biochemical measures of eye levels of retinoid intermediates during recovery from an intense photobleach. Although it is thought that cRDH is expressed in the eye almost solely in retinal pigment epithelial cells, we detected cRDH expression in other retinal cells, including ganglion cells, amacrine cells, horizontal cells, and the inner segments of the rod photoreceptor cells. Aside from the eye, there are no marked differences in retinoid levels in other tissues throughout the body for cRDH-/- compared with cRDH+/+ mice. Moreover, we did not detect any non-visual phenotypic changes for cRDH-/- mice, suggesting that these mice do not have problems in metabolizing 3 alpha-hydroxysteroids.Thus, cRDH may act essentially in the visual cycle but is redundant for catalyzing 9-cis-retinoic acid formation and 3 alpha-hydroxysteroid metabolism.