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Mark Daly and colleagues use population reference samples to refine the role of de novo protein-truncating variants in neurodevelopmental disorders. They show that variants independently observed in population reference samples do not contribute substantively to neurodevelopmental risk, and they use a loss-of-function intolerance metric to identify a small subset of genes that contain the entire observed signal of associated de novo protein-truncating variants in these disorders. Recent research has uncovered an important role for de novo variation in neurodevelopmental disorders. Using aggregated data from 9,246 families with autism spectrum disorder, intellectual disability, or developmental delay, we found that ∼1/3 of de novo variants are independently present as standing variation in the Exome Aggregation Consortium's cohort of 60,706 adults, and these de novo variants do not contribute to neurodevelopmental risk. We further used a loss-of-function (LoF)-intolerance metric, pLI, to identify a subset of LoF-intolerant genes containing the observed signal of associated de novo protein-truncating variants (PTVs) in neurodevelopmental disorders. LoF-intolerant genes also carry a modest excess of inherited PTVs, although the strongest de novo –affected genes contribute little to this excess, thus suggesting that the excess of inherited risk resides in lower-penetrant genes. These findings illustrate the importance of population-based reference cohorts for the interpretation of candidate pathogenic variants, even for analyses of complex diseases and de novo variation.

Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD. Casting noncoding variation in the context of a de novo risk score across multiple annotation categories, however, did demonstrate association with mutations localized to promoter regions. We found that the strongest driver of this promoter signal emanates from evolutionarily conserved transcription factor binding sites distal to the transcription start site. These data suggest that de novo mutations in promoter regions, characterized by evolutionary and functional signatures, contribute to ASD.

Recent studies implicate chromatin modifiers in autism spectrum disorder (ASD) through the identification of recurrent de novo loss of function mutations in affected individuals. ASD risk genes are co-expressed in human midfetal cortex, suggesting that ASD risk genes converge in specific regulatory networks during neurodevelopment. To elucidate such networks, we identify genes targeted by CHD8, a chromodomain helicase strongly associated with ASD, in human midfetal brain, human neural stem cells (hNSCs) and embryonic mouse cortex. CHD8 targets are strongly enriched for other ASD risk genes in both human and mouse neurodevelopment, and converge in ASD-associated co-expression networks in human midfetal cortex. CHD8 knockdown in hNSCs results in dysregulation of ASD risk genes directly targeted by CHD8. Integration of CHD8-binding data into ASD risk models improves detection of risk genes. These results suggest loss of CHD8 contributes to ASD by perturbing an ancient gene regulatory network during human brain development. Autism genes converge in midfetal cortical co-expression networks, and chromatin regulators such as CHD8 are increasingly associated with autism spectrum disorder (ASD). Here the authors map CHD8 targets in developing brain, and find that CHD8 directly regulates other ASD risk genes during human neurodevelopment.

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.

De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA's integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.

Susceptibility to autism Several lines of evidence point to genetic involvement in autism spectrum disorders (ASDs), neurodevelopmental and neuropsychiatric disorders characterized by impaired verbal communication and social interaction. The clinical and genetic complexities of the condition make it difficult to identify susceptibility factors, but two related studies now present robust evidence for a genetic involvement. The first, a genome-wide association study, identifies six single-nucleotide polymorphisms strongly associated with autism. These variants lie between two genes encoding neuronal cell-adhesion molecules (cadherins 9 and 10), suggesting possible involvement in ASD pathogenesis. The second study used copy number variation screens to identify genetic variants in two major gene pathways in children with ASDs. The changes are in the ubiquitin pathway, which has previously been associated with neurological disease, and in genes for neuronal cell-adhesion molecules. Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by impairments in social and communication skills. Accumulating evidence suggests a genetic component to ASDs, and here a two-stage, genome-wide approach is used to identify candidate genomic loci enriched in ASD cases. The majority of these loci are found to be involved in neuronal adhesion and ubiquitin degradation, suggesting novel susceptibility mechanisms. Autism spectrum disorders (ASDs) are childhood neurodevelopmental disorders with complex genetic origins 1 , 2 , 3 , 4 . Previous studies focusing on candidate genes or genomic regions have identified several copy number variations (CNVs) that are associated with an increased risk of ASDs 5 , 6 , 7 , 8 , 9 . Here we present the results from a whole-genome CNV study on a cohort of 859 ASD cases and 1,409 healthy children of European ancestry who were genotyped with ∼550,000 single nucleotide polymorphism markers, in an attempt to comprehensively identify CNVs conferring susceptibility to ASDs. Positive findings were evaluated in an independent cohort of 1,336 ASD cases and 1,110 controls of European ancestry. Besides previously reported ASD candidate genes, such as NRXN1 (ref. 10 ) and CNTN4 (refs 11 , 12 ), several new susceptibility genes encoding neuronal cell-adhesion molecules, including NLGN1 and ASTN2 , were enriched with CNVs in ASD cases compared to controls ( P = 9.5 × 10 -3 ). Furthermore, CNVs within or surrounding genes involved in the ubiquitin pathways, including UBE3A , PARK2 , RFWD2 and FBXO40 , were affected by CNVs not observed in controls ( P = 3.3 × 10 -3 ). We also identified duplications 55 kilobases upstream of complementary DNA AK123120 ( P = 3.6 × 10 -6 ). Although these variants may be individually rare, they target genes involved in neuronal cell-adhesion or ubiquitin degradation, indicating that these two important gene networks expressed within the central nervous system may contribute to the genetic susceptibility of ASD.

Background Elevated maternal pre-pregnancy body mass index (BMI) has been suggested to increase risk of offspring autism spectrum disorder (ASD) but evidence is mixed across heterogeneous studies and robust estimates spanning the full BMI range are lacking. This study examined the association between maternal BMI and offspring ASD in a harmonized, two-nation study and across the full BMI range. Methods We included all singleton children born in Denmark 2004–2018 and Sweden 1998–2019 to parents of Nordic origin ( n  = 2,072,445), with follow-up from age 2 until 31 December 2021, or 2022, respectively. Maternal BMI recorded at the first antenatal visit was obtained from the Swedish and Danish Medical Birth Registers and was analyzed as a continuous variable and in World Health Organization-defined categories of underweight (BMI < 18.5), normal weight (18.5–24.9), overweight (25–29.9), obese class I (30–34.9), and obese class II–III (≥ 35). The relative risk of ASD was estimated as hazard ratios (HR) from Cox regression models, adjusted for birth year and parental age, educational level, income, and psychiatric history at time of childbirth, using data from national health and population registers. Both country-specific and pooled analyses were conducted. Subgroup and sensitivity analyses, including a sibling comparison, were performed to address the specificity and robustness of findings. Results A total of 58,416 (2.8%) children were diagnosed with ASD during follow-up. The risk of ASD exhibited a J-shaped association with BMI, which gradually increased for mothers with both lower and higher BMI compared to BMI 22 (mid-normal range) (HR = 1.16 [95% CI 1.06–1.27] for BMI 15, and HR = 1.50 [95% CI 1.46–1.53] for BMI 30 in the fully adjusted model). Adjustment for familial factors in a sibling comparison attenuated associations. Conclusions Both high and low maternal BMI are associated with an increased risk of ASD in the offspring. Familial factors, including genetic and environmental components consistent between siblings, may explain part of the association.

Background Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses. Methods Utilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein–protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an “ASD disrupted network.” Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner. Results Extending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types—excitatory, inhibitory, and neural progenitor—we implicate several hundred genes in risk (FDR  ≤ 0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system. Limitations Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional. Conclusions Disrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD.

DNA methylation (DNAm), the addition of a methyl group to a cytosine in DNA, plays an important role in the regulation of gene expression. Single-nucleotide polymorphisms (SNPs) associated with schizophrenia (SZ) by genome-wide association studies (GWAS) often influence local DNAm levels. Thus, DNAm alterations, acting through effects on gene expression, represent one potential mechanism by which SZ-associated SNPs confer risk. In this study, we investigated genome-wide DNAm in postmortem superior temporal gyrus from 44 subjects with SZ and 44 non-psychiatric comparison subjects using Illumina Infinium MethylationEPIC BeadChip microarrays, and extracted cell-type-specific methylation signals by applying tensor composition analysis. We identified SZ-associated differential methylation at 242 sites, and 44 regions containing two or more sites (FDR cutoff of q  = 0.1) and determined a subset of these were cell-type specific. We found mitotic arrest deficient 1-like 1 ( MAD1L1 ), a gene within an established GWAS risk locus, harbored robust SZ-associated differential methylation. We investigated the potential role of MAD1L1 DNAm in conferring SZ risk by assessing for colocalization among quantitative trait loci for methylation and gene transcripts (mQTLs and tQTLs) in brain tissue and GWAS signal at the locus using multiple-trait-colocalization analysis. We found that mQTLs and tQTLs colocalized with the GWAS signal (posterior probability >0.8). Our findings suggest that alterations in MAD1L1 methylation and transcription may mediate risk for SZ at the MAD1L1 -containing locus. Future studies to identify how SZ-associated differential methylation affects MAD1L1 biological function are indicated.

Background Genetic studies have implicated rare and common variations in liability for autism spectrum disorder (ASD). Of the discovered risk variants, those rare in the population invariably have large impact on liability, while common variants have small effects. Yet, collectively, common risk variants account for the majority of population-level variability. How these rare and common risk variants jointly affect liability for individuals requires further study. Methods To explore how common and rare variants jointly affect liability, we assessed two cohorts of ASD families characterized for rare and common genetic variations (Simons Simplex Collection and Population-Based Autism Genetics and Environment Study). We analyzed data from 3011 affected subjects, as well as two cohorts of unaffected individuals characterized for common genetic variation: 3011 subjects matched for ancestry to ASD subjects and 11,950 subjects for estimating allele frequencies. We used genetic scores, which assessed the relative burden of common genetic variation affecting risk of ASD (henceforth “burden”), and determined how this burden was distributed among three subpopulations: ASD subjects who carry a potentially damaging variant implicated in risk of ASD (“PDV carriers”); ASD subjects who do not (“non-carriers”); and unaffected subjects who are assumed to be non-carriers. Results Burden harbored by ASD subjects is stochastically greater than that harbored by control subjects. For PDV carriers, their average burden is intermediate between non-carrier ASD and control subjects. Both carrier and non-carrier ASD subjects have greater burden, on average, than control subjects. The effects of common and rare variants likely combine additively to determine individual-level liability. Limitations Only 305 ASD subjects were known PDV carriers. This relatively small subpopulation limits this study to characterizing general patterns of burden, as opposed to effects of specific PDVs or genes. Also, a small fraction of subjects that are categorized as non-carriers could be PDV carriers. Conclusions Liability arising from common and rare risk variations likely combines additively to determine risk of any individual diagnosed with ASD. On average, ASD subjects carry a substantial burden of common risk variation, even if they also carry a rare PDV affecting risk.