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.

ABSTRACT While over a hundred genes are significantly associated with autism, little is known about the prevalence of variants affecting them in the general population. Nor do we fully appreciate the phenotypic diversity beyond the formal autism diagnosis. Using data from more than 13,000 autistic individuals and 210,000 undiagnosed individuals, we provide a gene-level map of the odds ratio for autism associated to rare loss-of-function (LoF) variants in 185 genes robustly associated with autism, alongside 2,492 genes displaying intolerance to LoF variants. In contrast to autism-centric approaches, we investigated the phenotype of undiagnosed individuals heterozygous for such variants and show that they exhibit a decrease in fluid intelligence, qualification level and income, and an increase in material deprivation. These effects were larger for LoFs in autism-associated genes than in other LoF-intolerant genes and appeared largely independent of sex and polygenic scores for autism. Using brain imaging data from 21,049 UK-Biobank individuals, we provide evidence for smaller cortical surface area and volume among carriers of LoFs in genes with high odds ratios for autism. Our gene-level map is a key resource to distinguish genes with high and low odds ratio for autism, and highlights the importance of including quantitative data on both diagnosed and undiagnosed individuals to better delineate the effect of genetic variants beyond the categorical diagnosis. Data are available at https://genetrek.pasteur.fr/ .

Ischemic stroke (IS) is a complex and devastating vascular disease that has become one of the leading causes of disability and mortality worldwide. Several studies have shown the association between matrix metalloproteinase (MMP) family gene polymorphisms and IS. However, the results have been indecisive.To investigate the association between Matrix Metalloproteinase gene polymorphisms and risk of IS.A literature search for eligible candidate gene studies published before, 28 June 2017, was conducted in the PubMed, EMBASE, Cochrane and Google Scholar databases. The following combinations of main keywords were used: (‘Matrix Metalloproteinase’ or ‘MMP’ or ‘Stromelysin-1’ or ‘Gelatinase b’) AND (‘ischemic stroke’ or ‘IS’) AND (‘single nucleotide polymorphism’ or ‘gene polymorphism’ or ‘SNP’). Fixed or random effects models were used to estimate the Pooled Odds ratio (OR) and 95% confidence interval (CI). Statistical analysis was carried out by using STATA version 13.0 software.Total 29 studies were included in our meta-analysis. A significant association was observed for MMP-9 (−1562C/T) (OR 1.27; 95% CI 1.06 to 1.53; p value = 0.01) and MMP-12 (−1082 A/G) (OR 2.55; 95% CI 1.75 to 3.71; p value<0.001) gene polymorphisms and risk of IS. No significant association was found for any of the MMP-1(−1607 1G/2G), MMP-2 (−1306C/T) & (−735C/T) and MMP-3 (−1612 5A/6A) gene polymorphisms with the risk of IS.Our meta-analysis suggests that MMP-9 (−1562C/T) and MMP-12 (−1082 A/G) gene polymorphisms could be a risk factor for IS while MMP-1 (−1607 1G/2G), MMP-2 (−1306C/T) & (−735C/T) and MMP-3 (−1612 5A/6A) have no association with the risk of causing IS. However, large prospective studies with sufficient power are required to validate our findings.

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.

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.

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.

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.

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 .

Scores of ASD candidate genes using the scoring algorithm. Total gene scores were determined from the total score of all rare variants (RVS) and the total score of all common variants (CVS) for a given candidate gene. The coding sequence length of a candidate gene, the number of publications from which rare and common variants were extracted (Pub_Rare and Pub_Common, respectively), and the number of scored rare and common variants (Var_Rare and Var_Comm, respectively) are included for each gene. (XLSX 33 kb)

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.

The Simons Foundation launched its autism research initiative (SFARI; ) in 2003 to generate new insight into the causes of autism spectrum disorder, and to advance diagnosis and treatment. For readers of this journal, perhaps the most relevant foundation project is SFARI Gene (<