Sleep and Activity Patterns in Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD. Methods: We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data. Results: This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives. Conclusions: The average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Background: Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. Methods: We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry data from three countries (Australia, the UK, and the USA). The model was validated within-cohort using subject-wise five-fold cross-validation for sleep-wake classification and in a three-class setting for sleep stage classification wake, rapid-eye-movement sleep (REM), non-rapid-eye-movement sleep (NREM) and by external validation. We assessed the face validity of our model for population inference by applying the model to the UK Biobank with 100,000 participants, each of whom wore a wristband for up to seven days. The derived sleep parameters were used in a Cox regression model to study the association of sleep duration and sleep efficiency with all-cause mortality. Findings: After exclusion, 1,448 participant nights of data were used to train the sleep classifier. The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. The derived sleep architecture estimate in the UK Biobank sample showed good face validity. Among 66,214 UK Biobank participants, 1,642 mortality events were observed. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration (6 to 7.9 hours), regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.69; 95% confidence intervals (CIs): 1.28 to 2.24 ) or high sleep efficiency (HRs: 1.42; 95% CIs: 1.14 to 1.77). Interpretation: Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Genome-wide significant risk loci for mood disorders in the Old Order Amish founder population.

Genome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants. We sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n = 1,672). Our analysis revealed four genome-wide significant risk loci, all of which were associated with >2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n = 314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors, CUX1 and CNOT1. Our findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies.

Allelic contribution of Nrxn1α to autism-relevant behavioral phenotypes in mice.

Copy number variations (CNVs) in the Neurexin 1 (NRXN1) gene, which encodes a presynaptic protein involved in neurotransmitter release, are some of the most frequently observed single-gene variants associated with autism spectrum disorder (ASD). To address the functional contribution of NRXN1 CNVs to behavioral phenotypes relevant to ASD, we carried out systematic behavioral phenotyping of an allelic series of Nrxn1 mouse models: one carrying promoter and exon 1 deletion abolishing Nrxn1α transcription, one carrying exon 9 deletion disrupting Nrxn1α protein translation, and one carrying an intronic deletion with no observable effect on Nrxn1α expression. We found that homozygous loss of Nrxn1α resulted in enhanced aggression in males, reduced affiliative social behaviors in females, and significantly altered circadian activities in both sexes. Heterozygous or homozygous loss of Nrxn1α affected the preference for social novelty in male mice, and notably, enhanced repetitive motor skills and motor coordination in both sexes. In contrast, mice bearing an intronic deletion of Nrxn1 did not display alterations in any of the behaviors assessed. These findings demonstrate the importance of Nrxn1α gene dosage in regulating social, circadian, and motor functions, and the variables of sex and genomic positioning of CNVs in the expression of autism-related phenotypes. Importantly, mice with heterozygous loss of Nrxn1, as found in numerous autistic individuals, show an elevated propensity to manifest autism-related phenotypes, supporting the use of models with this genomic architecture to study ASD etiology and assess additional genetic variants associated with autism.

Contrasting Views of Autism Spectrum Traits in Adults, Especially in Self-Reports vs. Informant-Reports for Women High in Autism Spectrum Traits.

There is uncertainty among researchers and clinicians about how to best measure autism spectrum dimensional traits in adults. In a sample of adults with high levels of autism spectrum traits and without intellectual disability (probands, n = 103) and their family members (n = 96), we sought to compare self vs. informant reports of autism spectrum-related traits and possible effects of sex on discrepancies. Using correlational analysis, we found poor agreement between self- and informant-report measures for probands, yet moderate agreement for family members. We found reporting discrepancy was greatest for female probands, often self-reporting more autism-related behaviors. Our findings suggest that autism spectrum traits are often underrecognized by informants, making self-report data important to collect in clinical and research settings.

Investigating the relationships between resilience, autism-related quantitative traits, and mental health outcomes among adults during the COVID-19 pandemic.

Resilience is a dynamic process through which people adjust to adversity and buffer anxiety and depression. The COVID-19 global pandemic has introduced a shared source of adversity for people across the world, with detrimental implications for mental health. Despite the pronounced vulnerability of autistic adults to anxiety and depression during the COVID-19 pandemic, relationships among autism-related quantitative traits, resilience, and mental health outcomes have not been examined. As such, we aimed to describe the relationships between these traits in a sample enriched in autism spectrum-related quantitative traits during the COVID-19 pandemic. We also aimed to investigate the impact of demographic and social factors on these relationships. Across three independent samples of adults, we assessed resilience factors, autism-related quantitative traits, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. One sample (recruited via the Autism Spectrum Program of Excellence, n = 201) was enriched for autism traits while the other two (recruited via Amazon Mechanical Turk, n = 624 and Facebook, n = 929) drew from the general population. We found resilience factors and quantitative autism-related traits to be inversely related, regardless of the resilience measure used. Additionally, we found that resilience factors moderate the relationship between autism-related quantitative traits and depression symptoms such that resilience appears to be protective. Across the neurodiversity spectrum, resilience factors may be targets to improve mental health outcomes. This approach may be especially important during the ongoing COVID-19 pandemic and in its aftermath.

Sufficient sleep duration in autistic children and the role of physical activity.

LAY ABSTRACT: Higher levels of physical activity may be associated with improved sleep in children, but this relationship is still being determined, especially in autistic children. In this study, we used existing data from the 2018 National Survey of Children's Health. Caregivers of children 6-17 years old, including caregivers of autistic children, completed a questionnaire that included questions about physical activity (days active in the past week) and sleep duration. We then determined if children were obtaining the recommended hours of sleep for their age (i.e. sufficient sleep). We found that higher physical activity levels were associated with sufficient sleep duration, but this finding was weaker in autistic children. In particular, this association was not observed in autistic children with more severe autism spectrum disorder, female autistic children, and autistic children 6-12 years old. In conclusion, physical activity is a promising approach to help children obtain sufficient sleep duration. However, more personalized approaches to improving sleep may be needed for certain groups of autistic children.

The relationship between autism spectrum and sleep-wake traits.

Autistic children and adults often have sleep disturbances, which may affect their and their family's quality of life. Yet, the relationship between sleep-wake patterns and autism spectrum traits is understudied. Identifying such relationships could lead to future research elucidating common mechanistic underpinnings. Thus, we aimed to determine whether sleep-wake patterns, specifically related to sleep, physical activity, and the daily sleep-wake rhythm (i.e., circadian rhythm), are associated with autism spectrum-related traits. Accelerometer-derived sleep-wake parameters were estimated in individuals with autistic spectrum traits and their family members (N = 267). We evaluated autism spectrum traits using the Social Responsiveness Scale (SRS) to assess the presence and severity of social impairment and the Behavior Rating Inventory of Executive Function (BRIEF) to assess executive function. The linear multivariate regression analysis (using SOLAR-Eclipse) showed that in adults, increased core autism spectrum traits and executive dysfunction were associated with disruption of several sleep-wake parameters, particularly related to the daily sleep-wake rhythm, and that executive dysfunction was associated with disrupted sleep quality and level of physical activity. We highlight the interplay between daytime function and disrupted sleep-wake patterns, specifically related to the daily sleep-wake rhythm, that could guide future research into common mechanisms. LAY SUMMARY: Autistic children and adults often report sleep disturbances. To dissect the relationship between a range of autism spectrum traits and sleep-wake patterns, we assessed social interaction and executive function in participants who also wore actimetry watches on their wrists to assess their sleep-wake patterns. We found that increased impairments in social and executive function occurred with increased sleep-wake disturbances, particularly those related to the circadian rhythm, suggesting that these perturbations/disruptions in the sleep-wake cycle could be connected to autism spectrum traits.

Heritability of quantitative autism spectrum traits in adults: A family-based study.

Autism spectrum disorder (ASD) comprises a multi-dimensional set of quantitative behavioral traits expressed along a continuum in autistic and neurotypical individuals. ASD diagnosis-a dichotomous trait-is known to be highly heritable and has been used as the phenotype for most ASD genetic studies. But less is known about the heritability of autism spectrum quantitative traits, especially in adults, an important prerequisite for gene discovery. We sought to measure the heritability of many autism-relevant quantitative traits in adults high in autism spectrum traits and their extended family members. Among adults high in autism spectrum traits (n = 158) and their extended family members (n = 245), we calculated univariate and bivariate heritability estimates for 19 autism spectrum traits across several behavioral domains. We found nearly all tested autism spectrum quantitative traits to be significantly heritable (h2  = 0.24-0.79), including overall ASD traits, restricted repetitive behaviors, broader autism phenotype traits, social anxiety, and executive functioning. The degree of shared heritability varied based on method and specificity of the assessment measure. We found high shared heritability for the self-report measures and for most of the informant-report measures, with little shared heritability among performance-based cognition tasks. These findings suggest that many autism spectrum quantitative traits would be good, feasible candidates for future genetics studies, allowing for an increase in the power of autism gene discovery. Our findings suggest that the degree of shared heritability between traits depends on the assessment method (self-report vs. informant-report vs. performance-based tasks), as well as trait-specificity. LAY SUMMARY: We found that the scores from questionnaires and tasks measuring different types of behaviors and abilities related to autism spectrum disorder (ASD) were heritable (strongly influenced by gene variants passed down through a family) among autistic adults and their family members. These findings mean that these scores can be used in future studies interested in identifying specific genes and gene variants that are associated with different behaviors and abilities related with ASD.

Ancestral haplotype reconstruction in endogamous populations using identity-by-descent.

In this work we develop a novel algorithm for reconstructing the genomes of ancestral individuals, given genotype or sequence data from contemporary individuals and an extended pedigree of family relationships. A pedigree with complete genomes for every individual enables the study of allele frequency dynamics and haplotype diversity across generations, including deviations from neutrality such as transmission distortion. When studying heritable diseases, ancestral haplotypes can be used to augment genome-wide association studies and track disease inheritance patterns. The building blocks of our reconstruction algorithm are segments of Identity-By-Descent (IBD) shared between two or more genotyped individuals. The method alternates between identifying a source for each IBD segment and assembling IBD segments placed within each ancestral individual. Unlike previous approaches, our method is able to accommodate complex pedigree structures with hundreds of individuals genotyped at millions of SNPs. We apply our method to an Old Order Amish pedigree from Lancaster, Pennsylvania, whose founders came to North America from Europe during the early 18th century. The pedigree includes 1338 individuals from the past 12 generations, 394 with genotype data. The motivation for reconstruction is to understand the genetic basis of diseases segregating in the family through tracking haplotype transmission over time. Using our algorithm thread, we are able to reconstruct an average of 224 ancestral individuals per chromosome. For these ancestral individuals, on average we reconstruct 79% of their haplotypes. We also identify a region on chromosome 16 that is difficult to reconstruct-we find that this region harbors a short Amish-specific copy number variation and the gene HYDIN. thread was developed for endogamous populations, but can be applied to any extensive pedigree with the recent generations genotyped. We anticipate that this type of practical ancestral reconstruction will become more common and necessary to understand rare and complex heritable diseases in extended families.

Polygenic Risk of Psychiatric Disorders Exhibits Cross-trait Associations in Electronic Health Record Data From European Ancestry Individuals.

BACKGROUND: Prediction of disease risk is a key component of precision medicine. Common traits such as psychiatric disorders have a complex polygenic architecture, making the identification of a single risk predictor difficult. Polygenic risk scores (PRSs) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but they require evaluation in a clinical setting. METHODS: We developed PRSs for 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, and anorexia nervosa) and 17 nonpsychiatric traits in more than 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We performed phenome-wide association analyses to test their association across disease categories. RESULTS: Four of the 6 psychiatric PRSs were associated with their primary phenotypes (odds ratios from 1.2 to 1.6). Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRSs for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder. CONCLUSIONS: We demonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses is similar to ascertained research cohorts from large consortia. Psychiatric PRSs are moderately associated with psychiatric diagnoses but are not yet clinically predictive in naïve patients. Cross-trait associations for these PRSs suggest a broader effect of genetic liability beyond traditional diagnostic boundaries. As identification of genetic markers increases, including PRSs alongside other clinical risk factors may enhance prediction of psychiatric disorders and associated conditions in clinical registries.

Transcriptomic signatures across human tissues identify functional rare genetic variation.

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

The Role of Synaptic Cell Adhesion Molecules and Associated Scaffolding Proteins in Social Affiliative Behaviors.

Social affiliative behaviors-engagement in positive (i.e., nonaggressive) social approach and reciprocal social interactions with a conspecific-comprise a construct within the National Institute of Mental Health Research Domain Criteria Social Processes Domain. These behaviors are disrupted in multiple human neurodevelopmental and neuropsychiatric disorders, such as autism, schizophrenia, social phobia, and others. Human genetic studies have strongly implicated synaptic cell adhesion molecules (sCAMs) in several such disorders that involve marked reductions, or other dysregulations, of social affiliative behaviors. Here, we review the literature on the role of sCAMs in social affiliative behaviors. We integrate findings pertaining to synapse structure and morphology, neurotransmission, postsynaptic signaling pathways, and neural circuitry to propose a multilevel model that addresses the impact of a diverse group of sCAMs, including neurexins, neuroligins, protocadherins, immunoglobulin superfamily proteins, and leucine-rich repeat proteins, as well as their associated scaffolding proteins, including SHANKs and others, on social affiliative behaviors. This review finds that the disruption of sCAMs often manifests in changes in social affiliative behaviors, likely through alterations in synaptic maturity, pruning, and specificity, leading to excitation/inhibition imbalance in several key regions, namely the medial prefrontal cortex, basolateral amygdala, hippocampus, anterior cingulate cortex, and ventral tegmental area. Unraveling the complex network of interacting sCAMs in glutamatergic synapses will be an important strategy for elucidating the mechanisms of social affiliative behaviors and the alteration of these behaviors in many neuropsychiatric and neurodevelopmental disorders.

Human and mouse essentiality screens as a resource for disease gene discovery.

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.

Variable neurodevelopmental and morphological phenotypes of carriers with 12q12 duplications.

BACKGROUND: Variable size deletions affecting 12q12 have been found in individuals with neurodevelopmental disorders (NDDs) and distinct facial and physical features. For many genetic loci affected by deletions in individuals with NDDs, reciprocal duplications have been described. However, for the 12q12 region, there are no detailed descriptions of duplication cases in the literature. METHODS: We report a phenotypic description of a family with monozygotic twins diagnosed with NDDs, carrying a 9 Mb duplication at 12q12, and five other individuals with overlapping duplications ranging from 4.54 Mb up to 15.16 Mb. RESULTS: The duplication carriers had language delays, cognitive delays, and were diagnosed with autism spectrum disorder. Additionally, distinct facial features (e.g., high foreheads, deeply set eyes, short palpebral fissures, small ears, high nasal bridges, abnormalities of the nose tip, thin lips), large feet, and abnormalities in the digits were noted. We also describe incomplete penetrance of the NDD phenotypes among the individuals with 12q12 duplication. CONCLUSION: This case series expands our knowledge on this rare genetic aberration and suggests that large 12q12 duplications may increase the risk for developing NDDs.

Twin-based heritability of actimetry traits.

There is a critical need for phenotypes with substantial heritability that can be used as endophenotypes in behavioral genetic studies. Activity monitoring, called actimetry, has potential as a means of assessing sleep and circadian rhythm traits that could serve as endophenotypes relevant to a range of psychopathologies. This study examined a range of actimetry traits for heritability using a classic twin design. The sample consisted of 195 subjects from 45 monozygotic (MZ) and 50 dizygotic (DZ) twin pairs aged 16-40 years. Subjects wore both a research-grade actimeter (GENEActiv) and a consumer-oriented device (FitBit) for 2 weeks. Sleep and circadian traits were extracted from GENEActiv data using PennZzz and ChronoSapiens software programs. Sleep statistics for a limited number of FitBit-collected traits were generated by its accompanying mobile app. Broad sense heritability was computed on a set of 33 MZ and 38 DZ twin pairs with complete data using both OpenMX and SOLAR software. These analyses yielded a large number of actimetry-derived traits, 20 of which showed high heritability (h2 > 0.6), seven of which remain significant after Bonferroni correction. These results indicate that actimetry enables assessing a range of phenotypes with substantial heritability that may be useful as endophenotypes for genetic studies.

Rediscovering the value of families for psychiatric genetics research.

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants.

PURPOSE: To assess the contribution of rare variants in the genetic background toward variability of neurodevelopmental phenotypes in individuals with rare copy-number variants (CNVs) and gene-disruptive variants. METHODS: We analyzed quantitative clinical information, exome sequencing, and microarray data from 757 probands and 233 parents and siblings who carry disease-associated variants. RESULTS: The number of rare likely deleterious variants in functionally intolerant genes ("other hits") correlated with expression of neurodevelopmental phenotypes in probands with 16p12.1 deletion (n=23, p=0.004) and in autism probands carrying gene-disruptive variants (n=184, p=0.03) compared with their carrier family members. Probands with 16p12.1 deletion and a strong family history presented more severe clinical features (p=0.04) and higher burden of other hits compared with those with mild/no family history (p=0.001). The number of other hits also correlated with severity of cognitive impairment in probands carrying pathogenic CNVs (n=53) or de novo pathogenic variants in disease genes (n=290), and negatively correlated with head size among 80 probands with 16p11.2 deletion. These co-occurring hits involved known disease-associated genes such as SETD5, AUTS2, and NRXN1, and were enriched for cellular and developmental processes. CONCLUSION: Accurate genetic diagnosis of complex disorders will require complete evaluation of the genetic background even after a candidate disease-associated variant is identified.