The Autism Genome Project: goals and strategies

The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism

Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.

A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.

Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review

Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.

A Role for Gene-Environment Interactions in Autism Spectrum Disorder Is Supported by Variants in Genes Regulating the Effects of Exposure to Xenobiotics

Heritability estimates support the contribution of genetics and the environment to the etiology of Autism Spectrum Disorder (ASD), but a role for gene-environment interactions is insufficiently explored. Genes involved in detoxification pathways and physiological permeability barriers (e.g., blood-brain barrier, placenta and respiratory airways), which regulate the effects of exposure to xenobiotics during early stages of neurodevelopment when the immature brain is extremely vulnerable, may be particularly relevant in this context. Our objective was to identify genes involved in the regulation of xenobiotic detoxification or the function of physiological barriers (the XenoReg genes) presenting predicted damaging variants in subjects with ASD, and to understand their interaction patterns with ubiquitous xenobiotics previously implicated in this disorder. We defined a panel of 519 XenoReg genes through literature review and database queries. Large ASD datasets were inspected for in silico predicted damaging Single Nucleotide Variants (SNVs) (N = 2,674 subjects) or Copy Number Variants (CNVs) (N = 3,570 subjects) in XenoReg genes. We queried the Comparative Toxicogenomics Database (CTD) to identify interaction pairs between XenoReg genes and xenobiotics. The interrogation of ASD datasets for variants in the XenoReg gene panel identified 77 genes with high evidence for a role in ASD, according to pre-specified prioritization criteria. These include 47 genes encoding detoxification enzymes and 30 genes encoding proteins involved in physiological barrier function, among which 15 are previous reported candidates for ASD. The CTD query revealed 397 gene-environment interaction pairs between these XenoReg genes and 80% (48/60) of the analyzed xenobiotics. The top interacting genes and xenobiotics were, respectively, CYP1A2, ABCB1, ABCG2, GSTM1, and CYP2D6 and benzo-(a)-pyrene, valproic acid, bisphenol A, particulate matter, methylmercury, and perfluorinated compounds. Individuals carrying predicted damaging variants in high evidence XenoReg genes are likely to have less efficient detoxification systems or impaired physiological barriers. They can therefore be particularly susceptible to early life exposure to ubiquitous xenobiotics, which elicit neuropathological mechanisms in the immature brain, such as epigenetic changes, oxidative stress, neuroinflammation, hypoxic damage, and endocrine disruption. As exposure to environmental factors may be mitigated for individuals with risk variants, this work provides new perspectives to personalized prevention and health management policies for ASD.

A bibliometrics analysis and visualization of autism spectrum disorder

BACKGROUND

The prevalence of autism spectrum disorder (ASD) increased rapidly in the last 20 years. Although related research has developed rapidly, little is known about its etiology, diagnostic marker, or drug treatment, which forces researchers to review and summarize its development process and look for the future development direction.

METHODS

We used bibliometrics to analyze papers of ASD in the Web of Science from 1998 to 2021, to draw the network of authors, institutions, countries, and keywords in the ASD field, and visualize the results.

RESULTS

A total of 40,597 papers were included with a continually increasing trend. It turns out that the research on ASD is mainly concentrated in universities. The United States has the largest number of ASD studies, followed by England and Canada. The quality of papers related to ASD is generally high, which shows that ASD research has become a hot spot of scientific research. The keywords of ASD etiology and diagnostic markers can be classified into at least 7 aspects. The detection of keywords shows that ASD research is mostly based on its subtypes, takes children as the study population, focuses on neurodevelopmental imaging or genetics, and pays attention to individual differences. And ASD research has changed greatly under the impact of Corona Virus Disease 2019 in the past 2 years.

CONCLUSION

We consider the future development direction should be based on the improvement of case identification, accurate clinical phenotype, large-scale cohort study, the discovery of ASD etiology and diagnostic markers, drug randomized controlled trials, and telehealth.

Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry

Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automated classification of medical conditions. In this review, we summarize and categorize various data-driven methods for diagnostic classification. In particular, we focus on autism as an example of a challenging disorder due to its highly heterogeneous nature. We begin by describing the frontier of data science methods for the neuropsychiatry of autism. We discuss early signs of autism as defined by existing pen-and-paper-based diagnostic instruments and describe data-driven feature selection techniques for determining the behaviors that are most salient for distinguishing children with autism from neurologically typical children. We then describe data-driven detection techniques, particularly computer vision and eye tracking, that provide a means of quantifying behavioral differences between cases and controls. We also describe methods of preserving the privacy of collected videos and prior efforts of incorporating humans in the diagnostic loop. Finally, we summarize existing digital therapeutic interventions that allow for data capture and longitudinal outcome tracking as the diagnosis moves along a positive trajectory. Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field.

Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization

BACKGROUND

There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother-offspring pairs.

METHODS

We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown.

RESULTS

We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes.

CONCLUSIONS

We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.

The promise of autism genetics: Still waiting

The condition presently known as autism spectrum disorder was first described in the literature nearly 80 years ago. Since then, the research community has mounted a quest to discover the etiology of the condition in hopes of discovering prevention, treatment, and cures for this developmental disorder. The field of autism genetics has made progress in understanding what genes are and what are not associated with autism. This article provides a brief overview of the evolution of autism genetics and describes the current state of science in realizing genetically informed prevention and treatments.

Childhood disintegrative disorder with seasonal total mutism: A rare clinical presentation

Childhood disintegrative disorder (CDD) is a rare autistic-like clinical condition with unknown etiology, in that previously acquired age-appropriate language, social and adaptive abilities deteriorate significantly in 2-10-year-old healthy children, although physical and neurological evaluations display no observable abnormality. Our case is a 22-year-old female born of a consanguineous marriage, with the appearance of CDD symptoms in her fifth year of age following normal mental and physical development during her initial four years of life. Without any precipitating factor, she gradually lost her language abilities, social relational skills, affectionate behavior, adaptive capacities, peer play and meaningful interest in her surrounding, friends and family members over a period of 4 years, reaching a plateau in her ninth year of age. The unique special clinical symptom in this case is a seasonal total mutism, which after the beginning of her CDD symptoms is revealing every year covering the spring. As no additional physical or psychological change accompanies her total seasonal speech loss, it cannot be attributed to any mental condition known as having a seasonal pattern. Because in the literature CDD is presented mostly as case reports with lacking of advanced research data, describing any new case is recommended to improve the knowledge about this rare condition, especially if it displays some new unusual signs, not reported till now.

The phenotypic manifestations of rare genic CNVs in autism spectrum disorder

Significant evidence exists for the association between copy number variants (CNVs) and Autism Spectrum Disorder (ASD); however, most of this work has focused solely on the diagnosis of ASD. There is limited understanding of the impact of CNVs on the 'sub-phenotypes' of ASD. The objective of this paper is to evaluate associations between CNVs in differentially brain expressed (DBE) genes or genes previously implicated in ASD/intellectual disability (ASD/ID) and specific sub-phenotypes of ASD. The sample consisted of 1590 cases of European ancestry from the Autism Genome Project (AGP) with a diagnosis of an ASD and at least one rare CNV impacting any gene and a core set of phenotypic measures, including symptom severity, language impairments, seizures, gait disturbances, intelligence quotient (IQ) and adaptive function, as well as paternal and maternal age. Classification analyses using a non-parametric recursive partitioning method (random forests) were employed to define sets of phenotypic characteristics that best classify the CNV-defined groups. There was substantial variation in the classification accuracy of the two sets of genes. The best variables for classification were verbal IQ for the ASD/ID genes, paternal age at birth for the DBE genes and adaptive function for de novo CNVs. CNVs in the ASD/ID list were primarily associated with communication and language domains, whereas CNVs in DBE genes were related to broader manifestations of adaptive function. To our knowledge, this is the first study to examine the associations between sub-phenotypes and CNVs genome-wide in ASD. This work highlights the importance of examining the diverse sub-phenotypic manifestations of CNVs in ASD, including the specific features, comorbid conditions and clinical correlates of ASD that comprise underlying characteristics of the disorder.

Genetically meaningful phenotypic subgroups in autism spectrum disorders

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong evidence for genetic susceptibility. However, the effect sizes for implicated chromosomal loci are small, hard to replicate and current evidence does not explain the majority of the estimated heritability. Phenotypic heterogeneity could be one phenomenon complicating identification of genetic factors. We used data from the Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule, Vineland Adaptive Behavior Scales, head circumferences, and ages at exams as classifying variables to identify more clinically similar subgroups of individuals with ASD. We identified two distinct subgroups of cases within the Autism Genetic Resource Exchange dataset, primarily defined by the overall severity of evaluated traits. In addition, there was significant familial clustering within subgroups (odds ratio, OR ≈ 1.38-1.42, P < 0.00001), and genotypes were more similar within subgroups compared to the unsubgrouped dataset (Fst = 0.17 ± 0.0.0009). These results suggest that the subgroups recapitulate genetic etiology. Using the same approach in an independent dataset from the Autism Genome Project, we similarly identified two distinct subgroups of cases and confirmed this severity-based dichotomy. We also observed evidence for genetic contributions to subgroups identified in the replication dataset. Our results provide more effective methods of phenotype definition that should increase power to detect genetic factors influencing risk for ASD.

Diagnostics for personalized medicine: what will change in the era of large-scale genomics studies?

The era of personalized medicine is upon us and it is being fueled by large available data sets of many types that are setting the foundation for the development of more precise diagnostic tools and targeted therapies, which are improving patient outcomes. Technology innovation and concomitant price decreases in molecular scanning technologies are at the heart of this change, both accelerating at a rate that has exceeded Moore’s law. This technology trend is enabling the research community to generate, and make publicly available, massive amounts of genomic data. These data come in the form not only of contextual information about the structure and function of the genome, but also in the form of variants that are correlated with human disease. Coupled with this molecular information, we are making dramatic inroads into capturing and making available high-resolution phenotypic and environmental exposure data through both incentives to physicians to migrate electronic medical records and to adoption of consumer-facing data collection and aggregation technologies. These large-scale genomic, environmental and phenotypic data together allow us to provide a multitude of new diagnostic correlations across the spectrum of possible clinical indications. To fully leverage the data foundation that will lead us to precise diagnostics and truly move the needle in outcome improvement, we need to achieve a culture shift as to how to apply this new personalized and probabilistic diagnostic information to better practice the art of medicine.

Reduced effect of stimulation of AMPA receptors on cerebral O₂ consumption in a rat model of autism

Previous work demonstrated that basal alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor activity did not contribute to the elevated regional cerebral O₂ consumption in the brains of Eker rat (an autism-tuberous sclerosis model). We tested the hypothesis that increased stimulation of AMPA receptors also would not augment cerebral O₂ consumption in the Eker rat. Three cortical sites were prepared for administration of saline, 10⁻⁴ and 10⁻³ M AMPA in young (4 weeks) male control Long Evans and Eker rats (70-100 g). Cerebral blood flow (¹⁴C-iodoantipyrine) and O₂ consumption (cryomicrospectrophotometry) were determined in isoflurane anesthetized rats. Receptor levels were studied through Western analysis of the GLuR1 subunit of the AMPA receptor. We found significantly increased cortical O₂ consumption (+33%) after 10⁻⁴ M AMPA in control rats. The higher dose of AMPA did not further increase consumption. In the Eker rats, neither dose led to a significant increase in cortical O₂ consumption. Regional blood flow followed a similar pattern to oxygen consumption but cortical O₂ extraction did not differ. Cortical AMPA receptor protein levels were significantly reduced (-21%) in the Eker compared to control rats. Both O₂ consumption and blood flow were significantly elevated in the pons of the Eker rats compared to control. These data demonstrate a reduced importance of AMPA receptors in the control of cortical metabolism, related to reduced AMPA receptor protein, in the Eker rat. This suggests that increasing AMPA receptor activity may not be an effective treatment for children with autism spectrum disorders as they also have reduced AMPA receptor number.

Using global unique identifiers to link autism collections

OBJECTIVE

To propose a centralized method for generating global unique identifiers to link collections of research data and specimens.

DESIGN

The work is a collaboration between the Simons Foundation Autism Research Initiative and the National Database for Autism Research. The system is implemented as a web service: an investigator inputs identifying information about a participant into a client application and sends encrypted information to a server application, which returns a generated global unique identifier. The authors evaluated the system using a volume test of one million simulated individuals and a field test on 2000 families (over 8000 individual participants) in an autism study.

MEASUREMENTS

Inverse probability of hash codes; rate of false identity of two individuals; rate of false split of single individual; percentage of subjects for which identifying information could be collected; percentage of hash codes generated successfully.

RESULTS

Large-volume simulation generated no false splits or false identity. Field testing in the Simons Foundation Autism Research Initiative Simplex Collection produced identifiers for 96% of children in the study and 77% of parents. On average, four out of five hash codes per subject were generated perfectly (only one perfect hash is required for subsequent matching).

DISCUSSION

The system must achieve balance among the competing goals of distinguishing individuals, collecting accurate information for matching, and protecting confidentiality. Considerable effort is required to obtain approval from institutional review boards, obtain consent from participants, and to achieve compliance from sites during a multicenter study.

CONCLUSION

Generic unique identifiers have the potential to link collections of research data, augment the amount and types of data available for individuals, support detection of overlap between collections, and facilitate replication of research findings.

Genetische Analysen zur Identifizierung molekularer Mechanismen bei Autismus-Spektrum-Störungen

Autismus-Spektrum-Störungen (ASS) sind neuronale Entwicklungsstörungen mit Auswirkung auf Kommunikation, Sprachentwicklung und Verhalten. Der komplexe Phänotyp und die starke klinische Heterogenität lassen bei erhöhter Disposition von ASS unter Geschwistern auf einen multifaktoriellen genetischen Hintergrund schließen. Neben einzelnen seltenen Mutationen werden auch Genkopie-Varianten und Einzelnukleotid-Polymorphismen immer mehr als Risikofaktoren in Betracht gezogen. Zur Identifizierung zentraler Schlüsselmechanismen werden im Rahmen von Konsortien Kopplungsanalysen und genomweite Assoziationsstudien durchgeführt. Außer polygenen bzw. genetisch komplexen Modellen, denen ASS zugrunde liegt, gibt es auch monogenetisch bedingte Formen. Dabei kommt es durch Aberrationen an einzelnen Genen zu einem autistischen Phänotyp, wie z. B. beim Fragilen-X-Syndrom. Knockout-Tiermodelle für monogenetischen Autismus wie FMRP–/– oder für neurodegenerative Erkrankungen wie MeCP2–/– werden häufig zur Untersuchung der molekularen Mechanismen herangezogen, welche bei ASS gestört sein könnten. Hier geben wir einen Einblick in den Stand der aktuellen Forschung im Bereich der Genomanalyse und beschreiben die wichtigsten Mausmodelle im Hinblick auf die Erkenntnisse bei poly- und monogenetischem Autismus. Grundsätzlich kann man erkennen, dass die meisten assoziierten Genomregionen und Gene im Zusammenhang mit der Ausbildung des synaptischen Spalts, der korrekten Sekretion von Oberflächenmolekülen oder der Translation stehen. Dies lässt vermuten, dass der Phänotyp bei ASS vorrangig durch eine Störung der translationsabhängigen Zell-Zell-Konnektivität und synaptischen Plastizität hervorgerufen wird.

High-density SNP association study of the 17q21 chromosomal region linked to autism identifies CACNA1G as a novel candidate gene

Chromosome 17q11-q21 is a region of the genome likely to harbor susceptibility to autism (MIM(209850)) based on earlier evidence of linkage to the disorder. This linkage is specific to multiplex pedigrees containing only male probands (MO) within the Autism Genetic Resource Exchange (AGRE). Earlier, Stone et al.(1) completed a high-density single nucleotide polymorphism association study of 13.7 Mb within this interval, but common variant association was not sufficient to account for the linkage signal. Here, we extend this single nucleotide polymorphism-based association study to complete the coverage of the two-LOD support interval around the chromosome 17q linkage peak by testing the majority of common alleles in 284 MO trios. Markers within an interval containing the gene, CACNA1G, were found to be associated with Autism Spectrum Disorder at a locally significant level (P=1.9 × 10(-5)). While establishing CACNA1G as a novel candidate gene for autism, these alleles do not contribute a sufficient genetic effect to explain the observed linkage, indicating that there is substantial genetic heterogeneity despite the clear linkage signal. The region thus likely harbors a combination of multiple common and rare alleles contributing to the genetic risk. These data, along with earlier studies of chromosomes 5 and 7q3, suggest few if any major common risk alleles account for Autism Spectrum Disorder risk under major linkage peaks in the AGRE sample. This provides important evidence for strategies to identify Autism Spectrum Disorder genes, suggesting that they should focus on identifying rare variants and common variants of small effect.

Savant: genome browser for high-throughput sequencing data

MOTIVATION

The advent of high-throughput sequencing (HTS) technologies has made it affordable to sequence many individuals' genomes. Simultaneously the computational analysis of the large volumes of data generated by the new sequencing machines remains a challenge. While a plethora of tools are available to map the resulting reads to a reference genome, and to conduct primary analysis of the mappings, it is often necessary to visually examine the results and underlying data to confirm predictions and understand the functional effects, especially in the context of other datasets.

RESULTS

We introduce Savant, the Sequence Annotation, Visualization and ANalysis Tool, a desktop visualization and analysis browser for genomic data. Savant was developed for visualizing and analyzing HTS data, with special care taken to enable dynamic visualization in the presence of gigabases of genomic reads and references the size of the human genome. Savant supports the visualization of genome-based sequence, point, interval and continuous datasets, and multiple visualization modes that enable easy identification of genomic variants (including single nucleotide polymorphisms, structural and copy number variants), and functional genomic information (e.g. peaks in ChIP-seq data) in the context of genomic annotations.

AVAILABILITY

Savant is freely available at http://compbio.cs.toronto.edu/savant.

Consanguinity mapping of congenital heart disease in a South Indian population

BACKGROUND

Parental consanguinity is a risk factor for congenital heart disease (CHD) worldwide, suggesting that a recessive inheritance model may contribute substantially to CHD. In Bangalore, India, uncle-niece and first cousin marriages are common, presenting the opportunity for an international study involving consanguinity mapping of structural CHD. We sought to explore the recessive model of CHD by conducting a genome-wide linkage analysis utilizing high-density oligonucleotide microarrays and enrolling 83 CHD probands born to unaffected consanguineous parents.

METHODOLOGY/PRINCIPAL FINDINGS

In this linkage scan involving single nucleotide polymorphism (SNP) markers, the threshold for genome-wide statistical significance was set at the standard log-of-odds (LOD) score threshold of 3.3, corresponding to 1995ratio1 odds in favor of linkage. We identified a maximal single-point LOD score of 3.76 (5754ratio1 odds) implicating linkage of CHD with the major allele (G) of rs1055061 on chromosome 14 in the HOMEZ gene, a ubiquitously expressed transcription factor containing leucine zipper as well as zinc finger motifs. Re-sequencing of HOMEZ exons did not reveal causative mutations in Indian probands. In addition, genotyping of the linked allele (G) in 325 U.S. CHD cases revealed neither genotypic nor allele frequency differences in varied CHD cases compared to 605 non-CHD controls.

CONCLUSIONS/SIGNIFICANCE

Despite the statistical power of the consanguinity mapping approach, no single gene of major effect could be convincingly identified in a clinically heterogeneous sample of Indian CHD cases born to consanguineous parents. However, we are unable to exclude the possibility that noncoding regions of HOMEZ may harbor recessive mutations leading to CHD in the Indian population. Further research involving large multinational cohorts of patients with specific subtypes of CHD is needed to attempt replication of the observed linkage peak on chromosome 14. In addition, we anticipate that a targeted re-sequencing approach may complement linkage analysis in future studies of recessive mutation detection in CHD.

Translational disease interpretation with molecular networks

The integration of medical information into gene and protein networks could lead to a better understanding of complex diseases.

Molecular networks are being used to reconcile genotypes and phenotypes by integrating medical information. In this context, networks will be instrumental for the interpretation of disease at the personalized medicine level.

Genetic testing in autism: how much is enough?

PURPOSE

To evaluate the yield of genetic testing in children with autism spectrum disorders.

METHODS

We performed a retrospective chart review of 71 unrelated patients with a diagnosis of an isolated autism spectrum disorder seen in a genetics clinic over a period of 14 months. For most, referrals occurred after evaluation by a developmental pediatrician and/or psychologist to establish the diagnosis. Tiered laboratory testing for the majority of the patients followed a guideline that was developed in collaboration with clinicians at The Autism Center at Children's Hospital, Columbus, OH.

RESULTS

The patients included 57 males and 14 females; 57 met DSM-IV criteria for autism, with the rest being Asperger or pervasive developmental disorder not otherwise specified. Macrocephaly [head circumference (HC) >or=95%] was present in 19 (27%). Two children had visible chromosome abnormalities (47,XYY; 48,XY + 2mar/49,XY + 3mar). Two patients with autism and macrocephaly had heterozygous mutations in the PTEN tumor suppressor gene. Three females had Rett syndrome, each confirmed by DNA sequencing of the MECP2 gene. Extensive metabolic testing produced no positive results, nor did fragile X DNA testing.

CONCLUSION

The overall diagnostic yield was 10% (7/71). PTEN gene sequencing should be considered in any child with macrocephaly and autism or developmental delay. Metabolic screening may not be warranted in autism spectrum disorders without more specific indications or additional findings.

Unraveling autism

In this issue of AJHG, Alarcón et al.,(1) Arking et al.,(2) and Bakkaloglu et al.(3) identify a series of functional variants in the CNTNAP2 gene that unequivocally implicate this gene as causing Type 1 autism in the general population.

Agenesis of the corpus callosum: genetic, developmental and functional aspects of connectivity

Agenesis of the corpus callosum (AgCC), a failure to develop the large bundle of fibres that connect the cerebral hemispheres, occurs in 1:4000 individuals. Genetics, animal models and detailed structural neuroimaging are now providing insights into the developmental and molecular bases of AgCC. Studies using neuropsychological, electroencephalogram and functional MRI approaches are examining the resulting impairments in emotional and social functioning, and have begun to explore the functional neuroanatomy underlying impaired higher-order cognition. The study of AgCC could provide insight into the integrated cerebral functioning of healthy brains, and may offer a model for understanding certain psychiatric illnesses, such as schizophrenia and autism.

The ups and downs of Wnt signaling in prevalent neurological disorders

In order to function properly, the brain must be wired correctly during critical periods in early development. Mistakes in this process are hypothesized to occur in disorders like autism and schizophrenia. Later in life, signaling pathways are essential in maintaining proper communication between neuronal and non-neuronal cells, and disrupting this balance may result in disorders like Alzheimer's disease. The Wnt/beta-catenin pathway has a well-established role in cancer. Here, we review recent evidence showing the involvement of Wnt/beta-catenin signaling in neurodevelopment as well as in neurodegenerative diseases. We suggest that the onset/development of such pathological conditions may involve the additive effect of genetic variation within Wnt signaling components and of molecules that modulate the activity of this signaling cascade.

Phenotypes of Drosophila brain neurons in primary culture reveal a role for fascin in neurite shape and trajectory

Subtle cellular phenotypes in the CNS may evade detection by routine histopathology. Here, we demonstrate the value of primary culture for revealing genetically determined neuronal phenotypes at high resolution. Gamma neurons of Drosophila melanogaster mushroom bodies (MBs) are remodeled during metamorphosis under the control of the steroid hormone 20-hydroxyecdysone (20E). In vitro, wild-type gamma neurons retain characteristic morphogenetic features, notably a single axon-like dominant primary process and an arbor of short dendrite-like processes, as determined with microtubule-polarity markers. We found three distinct genetically determined phenotypes of cultured neurons from grossly normal brains, suggesting that subtle in vivo attributes are unmasked and amplified in vitro. First, the neurite outgrowth response to 20E is sexually dimorphic, being much greater in female than in male gamma neurons. Second, the gamma neuron-specific "naked runt" phenotype results from transgenic insertion of an MB-specific promoter. Third, the recessive, pan-neuronal "filagree" phenotype maps to singed, which encodes the actin-bundling protein fascin. Fascin deficiency does not impair the 20E response, but neurites fail to maintain their normal, nearly straight trajectory, instead forming curls and hooks. This is accompanied by abnormally distributed filamentous actin. This is the first demonstration of fascin function in neuronal morphogenesis. Our findings, along with the regulation of human Fascin1 (OMIM 602689) by CREB (cAMP response element-binding protein) binding protein, suggest FSCN1 as a candidate gene for developmental brain disorders. We developed an automated method of computing neurite curvature and classifying neurons based on curvature phenotype. This will facilitate detection of genetic and pharmacological modifiers of neuronal defects resulting from fascin deficiency.

Imbalanced genomic imprinting in brain development: an evolutionary basis for the aetiology of autism

We describe a new hypothesis for the development of autism, that it is driven by imbalances in brain development involving enhanced effects of paternally expressed imprinted genes, deficits of effects from maternally expressed genes, or both. This hypothesis is supported by: (1) the strong genomic-imprinting component to the genetic and developmental mechanisms of autism, Angelman syndrome, Rett syndrome and Turner syndrome; (2) the core behavioural features of autism, such as self-focused behaviour, altered social interactions and language, and enhanced spatial and mechanistic cognition and abilities, and (3) the degree to which relevant brain functions and structures are altered in autism and related disorders. The imprinted brain theory of autism has important implications for understanding the genetic, epigenetic, neurological and cognitive bases of autism, as ultimately due to imbalances in the outcomes of intragenomic conflict between effects of maternally vs. paternally expressed genes.

Genetics of autism spectrum disorder

Autism is a highly heritable complex neurodevelopmental disorder characterized by distinct impairments of cognitive function in the field of social interaction and speech development. Different approaches have been undertaken worldwide to identify susceptibility loci or genes for autism spectrum disorders. No clear conclusions can be made today about genetic loci involved in these disorders. The review will focus on relevant results from the last decade of research with emphasis on whole genome screens and association studies.

Autism spectrum disorders: Molecular genetic advances

Despite the strong genetic basis of autism spectrum disorders (ASD), research efforts in the last decade have not been successful in the identification of confirmed susceptibility genes. We review the present status of genetic linkage, candidate gene, and association studies, pointing out the limitations of these approaches and the challenge of dealing with the clinical and genetic complexity of autism. Finally, we outline how recent technological and bioinformatic advances, together with an increasing understanding of the structure of the human genome, have set the stage to perform more comprehensive and well powered studies, possibly leading to a turning point in the understanding of the genetic basis of this devastating disorder.