Jack A. Kosmicki

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human ‘knockout’ variants in protein-coding genes.

A framework for the interpretation of de novo mutation in human disease

Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population

Almost all genetic risk factors for autism spectrum disorders (ASDs) can be found in the general population, but the effects of this risk are unclear in people not ascertained for neuropsychiatric symptoms. Using several large ASD consortium and population-based resources (total n > 38,000), we find genome-wide genetic links between ASDs and typical variation in social behavior and adaptive functioning. This finding is evidenced through both LD score correlation and de novo variant analysis, indicating that multiple types of genetic risk for ASDs influence a continuum of behavioral and developmental traits, the severe tail of which can result in diagnosis with an ASD or other neuropsychiatric disorder. A continuum model should inform the design and interpretation of studies of neuropsychiatric disease biology.

Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples

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 Consortiums 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.

Use of machine learning to shorten observation-based screening and diagnosis of autism

The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.

Autism spectrum disorder severity reflects the average contribution of de novo and familial influences

Significance Autism spectrum disorder (ASD) research is complicated by heterogeneity. There are several types of genetic risk factors for ASDs, and that diversity may be reflected in case presentation. This study presents evidence for systematic variation in the genetic architecture of ASDs in which higher functioning cases, defined through cognitive and behavioral assessments, are more likely to manifest familial influences. This finding suggests that genetic and neurobiological research into ASDs and other neuropsychiatric disorders may be pursued more efficiently through greater phenotypic characterization. Autism spectrum disorders (ASDs) are a highly heterogeneous group of conditions—phenotypically and genetically—although the link between phenotypic variation and differences in genetic architecture is unclear. This study aimed to determine whether differences in cognitive impairment and symptom severity reflect variation in the degree to which ASD cases reflect de novo or familial influences. Using data from more than 2,000 simplex cases of ASD, we examined the relationship between intelligence quotient (IQ), behavior and language assessments, and rate of de novo loss of function (LOF) mutations and family history of broadly defined psychiatric disease (depressive disorders, bipolar disorder, and schizophrenia; history of psychiatric hospitalization). Proband IQ was negatively associated with de novo LOF rate (P = 0.03) and positively associated with family history of psychiatric disease (P = 0.003). Female cases had a higher frequency of sporadic genetic events across the severity distribution (P = 0.01). High rates of LOF mutation and low frequencies of family history of psychiatric illness were seen in individuals who were unable to complete a traditional IQ test, a group with the greatest degree of language and behavioral impairment. These analyses provide strong evidence that familial risk for neuropsychiatric disease becomes more relevant to ASD etiology as cases become higher functioning. The findings of this study reinforce that there are many routes to the diagnostic category of autism and could lead to genetic studies with more specific insights into individual cases.

Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways.

Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning

Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in the United States remains close to 4—well past the developmental window when early intervention has the largest gains. This emphasizes the importance of developing accurate methods to detect risk faster than the current standards of care. In the present study, we used machine learning to evaluate one of the best and most widely used instruments for clinical assessment of ASD, the Autism Diagnostic Observation Schedule (ADOS) to test whether only a subset of behaviors can differentiate between children on and off the autism spectrum. ADOS relies on behavioral observation in a clinical setting and consists of four modules, with module 2 reserved for individuals with some vocabulary and module 3 for higher levels of cognitive functioning. We ran eight machine learning algorithms using stepwise backward feature selection on score sheets from modules 2 and 3 from 4540 individuals. We found that 9 of the 28 behaviors captured by items from module 2, and 12 of the 28 behaviors captured by module 3 are sufficient to detect ASD risk with 98.27% and 97.66% accuracy, respectively. A greater than 55% reduction in the number of behaviorals with negligible loss of accuracy across both modules suggests a role for computational and statistical methods to streamline ASD risk detection and screening. These results may help enable development of mobile and parent-directed methods for preliminary risk evaluation and/or clinical triage that reach a larger percentage of the population and help to lower the average age of detection and diagnosis.

Testing the accuracy of an observation-based classifier for rapid detection of autism risk.

Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=−0.814) and ADOS-2 (r=−0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=−0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible.

A Potential Contributory Role for Ciliary Dysfunction in the 16p11.2 600 kb BP4-BP5 Pathology

The 16p11.2 600 kb copy-number variants (CNVs) are associated with mirror phenotypes on BMI, head circumference, and brain volume and represent frequent genetic lesions in autism spectrum disorders (ASDs) and schizophrenia. Here we interrogated the transcriptome of individuals carrying reciprocal 16p11.2 CNVs. Transcript perturbations correlated with clinical endophenotypes and were enriched for genes associated with ASDs, abnormalities of head size, and ciliopathies. Ciliary gene expression was also perturbed in orthologous mouse models, raising the possibility that ciliary dysfunction contributes to 16p11.2 pathologies. In support of this hypothesis, we found structural ciliary defects in the CA1 hippocampal region of 16p11.2 duplication mice. Moreover, by using an established zebrafish model, we show genetic interaction between KCTD13, a key driver of the mirrored neuroanatomical phenotypes of the 16p11.2 CNV, and ciliopathy-associated genes. Overexpression of BBS7 rescues head size and neuroanatomical defects of kctd13 morphants, whereas suppression or overexpression of CEP290 rescues phenotypes induced by KCTD13 under- or overexpression, respectively. Our data suggest that dysregulation of ciliopathy genes contributes to the clinical phenotypes of these CNVs.