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Complex Autism Spectrum Disorders and Cutting-Edge Molecular Diagnostic Tests
Abstract
Autism spectrum disorders (ASDs) are incompletely understood neurodevelopmental disorders diagnosed solely on the basis of behavioral assessments of social, communicative, and repetitive symptoms.1 Although ASD is behaviorally distinctive and reliably identified by experienced clinicians, the disorder is clinically and genetically extremely heterogeneous. Psychologists, who began to define autism subgroups in the 1990s, found neither behavioral measures of core ASD symptoms nor cognitive measures reliably identified subgroups with similar outcomes or risk of recurrence in siblings.2 At the same time, geneticists were attempting with negligible success to find “autism genes” using molecular linkage and association analysis that had successfully identified the genes for cystic fibrosis and Huntington disease.3 Above all, their failures were attributed to an inability to assemble homogeneous ASD cohorts for analysis. This spurred the search for biomarkers, sometimes called endophenotypes, that might sort out the etiologic heterogeneity associated with ASD.4 The goal was to identify features that occur consistently in a portion of patients with ASD and are relatively discrete, quantifiable, and most importantly etiologically relevant. Physical dysmorphology appeared to fit the criteria.
... Autism is a term coined about a century ago, derived from the Greek root referring to 'self ' , and describes a wide range of human interpersonal behaviors 1 . Autistic tendencies may be recognized in many individuals as part of human variation 2 , but these features can be severe and therefore disabling [3][4][5] . The most recent Diagnostic and Statistical Manual of Mental Disorders, the fifth edition (DSM-5), uses the single omnibus classification 'autism spectrum disorder' (ASD) to encompass what once were considered several distinct diagnostic entities (such as autistic disorder, Asperger's disorder and pervasive developmental disorder not otherwise specified). ...
... CNVs involving genomic disorder loci (n = 69) or CNVs affecting previously reported ASD-risk genes (n = 58), all determined by standard diagnostic reporting criteria 16,17,37 and many associated with known syndromes of which ASD can be a component feature 5,9,10 . There were also 22 CNVs that overlapped with the ASD-risk genes found in this study (Fig. 3a). ...
... Samples for WGS and data access policy. We collected 5,205 unique samples (5,193 individuals) from 2,066 unique families with children diagnosed with ASD. The cohort consists of 2,618 children with ASD (1,740 probands and 878 affected siblings). ...
We are performing whole-genome sequencing of families with autism spectrum disorder (ASD) to build a resource (MSSNG) for subcategorizing the phenotypes and underlying genetic factors involved. Here we report sequencing of 5,205 samples from families with ASD, accompanied by clinical information, creating a database accessible on a cloud platform and through a controlled-access internet portal. We found an average of 73.8 de novo single nucleotide variants and 12.6 de novo insertions and deletions or copy number variations per ASD subject. We identified 18 new candidate ASD-risk genes and found that participants bearing mutations in susceptibility genes had significantly lower adaptive ability (P = 6 × 10⁻⁴). In 294 of 2,620 (11.2%) of ASD cases, a molecular basis could be determined and 7.2% of these carried copy number variations and/or chromosomal abnormalities, emphasizing the importance of detecting all forms of genetic variation as diagnostic and therapeutic targets in ASD.
... The 51 phenotypes examined in this work span ASD specific core-symptom measures, cognitive and adaptive functioning, behavioral problems, neurological indicators, and dysmorphic biomarker. The dysmorphic biomarker phenotype, quantified using the Autism Dysmorphology Measure (Miles et al., 2008), distinguishes complex autism (dysmorphic and/or microcephalic) (Miles, 2015) from essential autism (nondysmorphic and not microcephalic). Given that the distinction between complex and essential autism is important in dissecting the ASD etiologic heterogeneity (Miles, 2015;Spencer et al., 2018), we restricted the sample analyzed in this work to only the 560 probands who underwent dysmorphology examinations (Zhao et al., 2019;Matta et al., 2021). ...
... The dysmorphic biomarker phenotype, quantified using the Autism Dysmorphology Measure (Miles et al., 2008), distinguishes complex autism (dysmorphic and/or microcephalic) (Miles, 2015) from essential autism (nondysmorphic and not microcephalic). Given that the distinction between complex and essential autism is important in dissecting the ASD etiologic heterogeneity (Miles, 2015;Spencer et al., 2018), we restricted the sample analyzed in this work to only the 560 probands who underwent dysmorphology examinations (Zhao et al., 2019;Matta et al., 2021). All the 51 phenotype markers utilized in this work are summarized in Table 1. ...
Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate.
... 9 Miles et al hypothesized that, because those with equivocal and complex ASD showed evidence of an insult during early morphogenesis, they would be genetically distinct from those with essential/nondysmorphic ASD. Our group used her classification system to stratify a population-based cohort of 258 ASD children from Newfoundland and Labrador, Canada: 65.1% were classified as essential, 14.3% as equivocal, and 20.5% as complex. 8 The combined diagnostic yield from chromosomal microarray (CMA) (for ASD-associated copy number variants [CNVs]) and WES (for pathogenic variants in ASD-risk genes) was 15.8%. ...
... Karyotypes identify an ASD-associated chromosomal syndrome in 2% of ASD cases. 9,14 Most common are a supernumerary isodicentric chromosome 15 [idic (15)] involving the imprinted Prader-Willi/Angelman syndrome region (such individuals have four copies of the proximal 15q region instead of the usual two copies), Down syndrome, and the sex chromosome aneuploidies. 15 Over 85% of maternally derived idic(15) cases develop ASD. ...
Autism spectrum disorder (ASD) encompasses a group of neurodevelopmental conditions diagnosed solely on the basis of behavioral assessments that reveal social deficits. Progress has been made in understanding its genetic underpinnings, but most ASD-associated genetic variants, which include copy number variants (CNVs) and mutations in ASD-risk genes, account for no more than 1 % of ASD cases. This high level of genetic heterogeneity leads to challenges obtaining and interpreting genetic testing in clinical settings. The traditional definition of syndromic ASD is a disorder with a clinically defined pattern of somatic abnormalities and a neurobehavioral phenotype that may include ASD. Most have a known genetic cause. Examples include fragile X syndrome and tuberous sclerosis complex. We propose dividing syndromic autism into the following two groups: (i) ASD that occurs in the context of a clinically defined syndrome-recognizing these disorders depends on the familiarity of the clinician with the features of the syndrome, and the diagnosis is typically confirmed by targeted genetic testing (eg, mutation screening of FMR1); (ii) ASD that occurs as a feature of a molecularly defined syndrome-for this group of patients, ASD-associated variants are identified by genome-wide testing that is not hypothesis driven (eg, microarray, whole exome sequencing). These ASD groups cannot be easily clinically defined because patients with a given variant have variable somatic abnormalities (dysmorphism and birth defects). In this article, we review common diagnoses from the above categories and suggest a testing strategy for patients, guided by determining whether the individual has essential or complex ASD; patients in the latter group have multiple morphologic anomalies on physical examination. Finally, we recommend that the syndromic versus nonsyndromic designation ultimately be replaced by classification of ASD according to its genetic etiology, which will inform about the associated spectrum and penetrance of neurobehavioral and somatic manifestations.
... 77 Adding information regarding comorbidity such as ID, distinctive morphological features, and/or (congenital) physical conditions to the ASD diagnosis may have direct clinical utility as it indicates the a priori likelihood of identifying a pathogenic variant. 25,78,79 With the increased uptake of genetic testing in clinical practice, such integrated diagnostic practice can also be expected to contribute to the identification of rare genetic disorders, as well as to a more comprehensive understanding of the range of associated phenotypes. Recognizing rare, highly penetrant variants and their impact is an important step toward precision medicine strategies, [80][81][82] which can include individualized screening for comorbid conditions, genetic counseling, 2 and possibly guidance for development or implementation of targeted therapeutic interventions. ...
Genetic factors contribute to the etiology of autism spectrum disorder (ASD), a group of neurodevelopmental conditions with an estimated population prevalence of 2.3%. Further elucidation of the genetic architecture underlying ASD continues. Against this backdrop, we review history and current use of the concept "syndromic autism", which refers to both genetic etiology and phenotypic co-comorbidity. We question whether this term is still helpful, both in clinical and in research contexts. We will outline the arguments in support of potentially abandoning usage of this construct and propose alternative strategies to facilitate the identification of clinically relevant subsets of individuals diagnosed with ASD. The emergence of the concept of syndromic autism, while understandable from a historical perspective, erroneously conflates two different attributions: genetic etiology and phenotypic co-morbidity. Current evidence indicates that these two components are independent, not only when the concept is used to describe individual patients, but also when used as a descriptor of (groups of) genes. Continued usage of distinction between syndromic versus non-syndromic autism may slow scientific progress and negatively affect clinical care. We propose that the use of scientifically valid and clinically useful distinctions will strengthen the evidence-base of clinical and research practice.
... This included component scores from Autism Diagnostic Interview -Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS), Repetitive Behavior Scale (RBS), Social Responsiveness Scale (SRS), Aberrant Behavior Checklist (ABC), Child Behavior Checklist (CBCL), IQ, Vineland adaptive measures, dysmorphology examination, and Broader Autism Phenotype Questionnaire (BAPQ) for the parents. Similar to the work done in [13], the selected phenotype subset includes the dsymorphology measure used to distinguish complex autism (dysmorphic and/or microcephalic) [15] from essential autism (non-dysmorphic and not microcephalic). Not all the SSC sites conducted dsymorphology exams, hence this study sample is limited to 560 probands, out of a total of 2759 SSC probands. ...
Children with Autism Spectrum Disorder (ASD) exhibit a wide diversity in type, number, and severity of social deficits as well as communicative and cognitive difficulties. It is a challenge to categorize the phenotypes of a particular ASD patient with their unique genetic variants. There is a need for a better understanding of the connections between genotype information and the phenotypes to sort out the heterogeneity of ASD. In this study, single nucleotide polymorphism (SNP) and phenotype data obtained from a simplex ASD sample are combined using a PheWAS-inspired approach to construct a phenotype-phenotype network. The network is clustered, yielding groups of etiologically related phenotypes. These clusters are analyzed to identify relevant genes associated with each set of phenotypes. The results identified multiple discriminant SNPs associated with varied phenotype clusters such as ASD aberrant behavior (self-injury, compulsiveness and hyperactivity), as well as IQ and language skills. Overall, these SNPs were linked to 22 significant genes. An extensive literature search revealed that eight of these are known to have strong evidence of association with ASD. The others have been linked to related disorders such as mental conditions, cognition, and social functioning.Clinical relevance- This study further informs on connections between certain groups of ASD phenotypes and their unique genetic variants. Such insight regarding the heterogeneity of ASD would support clinicians to advance more tailored interventions and improve outcomes for ASD patients.
... The implication of these results, which must be verified in a prospective study, is that this metabolomics-based test battery is potentially able to detect more than 50% of individuals at risk for ASD. While biomarkers of any kind cannot provide a definitive diagnosis, combining a metabolomics-based screen with a behavioral screening tool such as the M-CHAT/F increases the likelihood that those at risk for ASD can be detected as early as possible [Kohane & Eran, 2013;Miles, 2015]. ...
Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty‐four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4‐hydroxyproline, or α‐ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%–57%) and 91% specificity (95% CI, 86%–94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments.
Lay Summary
Analysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood‐based molecules to determine if still a higher percentage of children with autism can be detected using this strategy.
... By genome sequencing of 5205 samples of individuals with ASD (N = 2620) and healthy controls of simplex and multiplex families, Yuen et al. (2017) detected an average of 400 CNVs (size > 2 kb) for each analyzed genome. Seven percent of involved individuals showed at least one pathogenic chromosomal variation (N = 21) or different subtype of CNVs (N = 152; holding megabase CNVs, CNVs involving genomic disorder loci, or CNVs affecting previously reported ASD-risk genes) (Betancur, 2011;Miles, 2015;Tammimies et al., 2015). In particular, the authors also found 22 CNVs that overlapped with some of the genes that they indicated as being associated with ASD susceptibility (Yuen et al., 2017). ...
... Their results suggest that phenotypic heterogeneity does not closely map to genetic variation implying that analysis of sub-phenotypes is not a productive path forward for discovering genetic risk variants in ASD. A more productive path in the search for etiologically discrete ASD subgroups might be to identify biologically based phenotypes (biomarkers) which also subdivide ASD [5]. ...
... However, these changes alone cannot account for the dramatic spike in the rate increase observed, particularly over the past two decades (Fig. 3) [Weintraub, 2011;Nightingale, 2012], and a concerted worldwide effort is striving to tease out contributing pathogenic risks and environmental and/or epigenetic factors [Genuis, 2010;Mitka, 2010;Miley, 2011;Rzhetsky et al., 2014;Loke et al., 2015;Raz et al., 2015;Sampson and Mazmanian, 2015] and to identify reliable diagnostic biomarkers and treatment options [Walsh et al., 2011;Veenstra-VanderWeele and Blakely, 2012;Goldani et al., 2014;Sinha et al., 2014;Ali et al., 2015;Anderson, 2015]. The introduction of chromosomal microarray technology is opening opportunities for important diagnostic strides [Miles, 2015;Tammimies et al., 2015]. For example, Syn-apDx, a diagnostic company based in Boston, MA, recently completed two clinical trials: STORY (Syn-apDx Autism Gene Expression Analysis Study) and SAGA (SynapDx Autism Gene Expression Analysis Study) with an aim to define a gene expression signature indicative of ASD and to establish its clinical sensitivity and specificity (see: https://clinicaltrials. gov). ...
Preclinical Research
Neuropsychiatric disorders are a heterogeneous group of conditions that often share underlying mitochondrial dysfunction and biological pathways implicated in their pathogenesis, progression, and treatment. To date, these disorders have proven notoriously resistant to molecular‐targeted therapies, and clinical options are relegated to interventional types, which do not address the core symptoms of the disease. In this review, we discuss emerging epigenetic‐driven approaches using novel acylcarnitine esters (carnitinoids) that act on master regulators of antioxidant and cytoprotective genes and mitophagic pathways. These carnitinoids are actively transported, mitochondria‐localizing, biomimetic coenzyme A surrogates of short‐chain fatty acids, which inhibit histone deacetylase and may reinvigorate synaptic plasticity and protect against neuronal damage. We outline these neuroprotective effects in the context of treatment of neuropsychiatric disorders such as autism spectrum disorder and schizophrenia. Drug Dev Res 77 : 53–72, 2016. © 2016 Wiley Periodicals, Inc.
... Patients at HUG-CELL were 10.9 AE 7.8 years old at ascertainment, about 23% of them were syndromic, and 56 (13%) were probands of familial cases. The male-female ratio was 3.2-1, which may be accounted for by a higher proportion of patients with intellectual disability (ID) as compared to other studies (roughly 77% versus 55%) with reported male-female ratios closer to 4 to 1 (Centers for Disease Control and Prevention, 2014;Miles, 2015;Werlin & Geschwind, 2013). ...
In the health domain, the move of generating big data is opening new methodologies in detection as well as prediction of various diseases and disorders. The first phase of the present chapter has provided insights into the role of big data analytics in the detection of one such neuro-disorder, that is, autism spectrum disorder (ASD). The data lake concept has provided a direction to resolve the issue by providing a common platform for storing tremendous amount of data in all formats (structured, unstructured, or raw). However, if the entire data have potential value, the data lakes need to be strategically designed as otherwise it can lead to data swamps. Therefore, in the second phase, data lake based on Hadoop architecture and Apache Spark engine has been provided for the analysis of the health data. The proposed system has resolved the data storage issue, management, and analytics on a single platform. Hence, the novelty of the chapter is that it is pointing towards the faster exploration as well as management of data so that the timely generation of hypothesis can help in analyzing ASD.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder generally manifesting in the first few years of life and tending to persist into adolescence and adulthood. It is characterized by deficits in communication and social interaction and restricted, repetitive patterns of behavior, interests, and activities. It is a disorder with multifactorial etiology. In this chapter, we will focus on the most important and common epidemiological studies, pathogenesis, screening, and diagnostic tools along with an explication of genetic testing in ASD.
Individuals with Autism Spectrum Disorder (ASD) share characteristics (impairments in socialization and communication, and repetitive interests and behavior), but differ in their developmental course, pattern of symptoms, and cognitive and language abilities. The development of standardized phenotyping has revealed ASD to clinically be vastly heterogeneous, ranging from milder presentations to more severe forms associated with profound intellectual disability. Some 100 genes have now been implicated in the etiology of ASD, and advances in genome-wide testing continue to yield new data at an unprecedented rate. As the translation of this data is incorporated into clinical care, genetic professionals/counsellors, as well as primary care physicians, will benefit from guidelines and tools to effectively communicate such genomic information. Here, we present a model to facilitate communication regarding the complexities of ASD, where clinical and genetic heterogeneity, as well as overlapping neurological conditions are inherent. We outline an approach for counselling families about their genomic results grounded in our direct experience from counselling families participating in an ASD research study, and supported by rationale from the literature.
Although the diagnosis of autism spectrum disorder (ASD) is based on behavioral signs and symptoms, the evaluation of a child with ASD has become increasingly focused on the identification of the genetic etiology of the disorder. In this review, we begin with a clinical overview of ASD, highlighting the heterogeneity of the disorder. We then discuss the genetics of ASD and present updated guidelines on genetic testing. We then consider the insights gained from the identification of both single gene disorders and rare variants, with regard to clinical phenomenology and potential treatment targets.
Copyright © 2015 Elsevier Inc. All rights reserved.
Despite significant heritability of autism spectrum disorders (ASDs), their extreme genetic heterogeneity has proven challenging for gene discovery. Studies of primarily simplex families have implicated de novo copy number changes and point mutations, but are not optimally designed to identify inherited risk alleles. We apply whole-exome sequencing (WES) to ASD families enriched for inherited causes due to consanguinity and find familial ASD associated with biallelic mutations in disease genes (AMT, PEX7, SYNE1, VPS13B, PAH, and POMGNT1). At least some of these genes show biallelic mutations in nonconsanguineous families as well. These mutations are often only partially disabling or present atypically, with patients lacking diagnostic features of the Mendelian disorders with which these genes are classically associated. Our study shows the utility of WES for identifying specific genetic conditions not clinically suspected and the importance of partial loss of gene function in ASDs.
Chromosomal microarray (CMA) is increasingly utilized for genetic testing of individuals with unexplained developmental delay/intellectual disability (DD/ID), autism spectrum disorders (ASD), or multiple congenital anomalies (MCA). Performing CMA and G-banded karyotyping on every patient substantially increases the total cost of genetic testing. The International Standard Cytogenomic Array (ISCA) Consortium held two international workshops and conducted a literature review of 33 studies, including 21,698 patients tested by CMA. We provide an evidence-based summary of clinical cytogenetic testing comparing CMA to G-banded karyotyping with respect to technical advantages and limitations, diagnostic yield for various types of chromosomal aberrations, and issues that affect test interpretation. CMA offers a much higher diagnostic yield (15%-20%) for genetic testing of individuals with unexplained DD/ID, ASD, or MCA than a G-banded karyotype ( approximately 3%, excluding Down syndrome and other recognizable chromosomal syndromes), primarily because of its higher sensitivity for submicroscopic deletions and duplications. Truly balanced rearrangements and low-level mosaicism are generally not detectable by arrays, but these are relatively infrequent causes of abnormal phenotypes in this population (<1%). Available evidence strongly supports the use of CMA in place of G-banded karyotyping as the first-tier cytogenetic diagnostic test for patients with DD/ID, ASD, or MCA. G-banded karyotype analysis should be reserved for patients with obvious chromosomal syndromes (e.g., Down syndrome), a family history of chromosomal rearrangement, or a history of multiple miscarriages.
Multiple lines of evidence indicate a strong genetic contribution to autism spectrum disorders (ASDs). Current guidelines for clinical genetic testing recommend a G-banded karyotype to detect chromosomal abnormalities and fragile X DNA testing, but guidelines for chromosomal microarray analysis have not been established.
A cohort of 933 patients received clinical genetic testing for a diagnosis of ASD between January 2006 and December 2008. Clinical genetic testing included G-banded karyotype, fragile X testing, and chromosomal microarray (CMA) to test for submicroscopic genomic deletions and duplications. Diagnostic yield of clinically significant genetic changes was compared.
Karyotype yielded abnormal results in 19 of 852 patients (2.23% [95% confidence interval (CI): 1.73%-2.73%]), fragile X testing was abnormal in 4 of 861 (0.46% [95% CI: 0.36%-0.56%]), and CMA identified deletions or duplications in 154 of 848 patients (18.2% [95% CI: 14.76%-21.64%]). CMA results for 59 of 848 patients (7.0% [95% CI: 5.5%-8.5%]) were considered abnormal, which includes variants associated with known genomic disorders or variants of possible significance. CMA results were normal in 10 of 852 patients (1.2%) with abnormal karyotype due to balanced rearrangements or unidentified marker chromosome. CMA with whole-genome coverage and CMA with targeted genomic regions detected clinically relevant copy-number changes in 7.3% (51 of 697) and 5.3% (8 of 151) of patients, respectively, both higher than karyotype. With the exception of recurrent deletion and duplication of chromosome 16p11.2 and 15q13.2q13.3, most copy-number changes were unique or identified in only a small subset of patients.
CMA had the highest detection rate among clinically available genetic tests for patients with ASD. Interpretation of microarray data is complicated by the presence of both novel and recurrent copy-number variants of unknown significance. Despite these limitations, CMA should be considered as part of the initial diagnostic evaluation of patients with ASD.
The use of genome-wide tests to provide molecular diagnosis for individuals with autism spectrum disorder (ASD) requires more study.
To perform chromosomal microarray analysis (CMA) and whole-exome sequencing (WES) in a heterogeneous group of children with ASD to determine the molecular diagnostic yield of these tests in a sample typical of a developmental pediatric clinic.
The sample consisted of 258 consecutively ascertained unrelated children with ASD who underwent detailed assessments to define morphology scores based on the presence of major congenital abnormalities and minor physical anomalies. The children were recruited between 2008 and 2013 in Newfoundland and Labrador, Canada. The probands were stratified into 3 groups of increasing morphological severity: essential, equivocal, and complex (scores of 0-3, 4-5, and ≥6).
All probands underwent CMA, with WES performed for 95 proband-parent trios.
The overall molecular diagnostic yield for CMA and WES in a population-based ASD sample stratified in 3 phenotypic groups.
Of 258 probands, 24 (9.3%, 95%CI, 6.1%-13.5%) received a molecular diagnosis from CMA and 8 of 95 (8.4%, 95%CI, 3.7%-15.9%) from WES. The yields were statistically different between the morphological groups. Among the children who underwent both CMA and WES testing, the estimated proportion with an identifiable genetic etiology was 15.8% (95%CI, 9.1%-24.7%; 15/95 children). This included 2 children who received molecular diagnoses from both tests. The combined yield was significantly higher in the complex group when compared with the essential group (pairwise comparison, P = .002). [table: see text].
Among a heterogeneous sample of children with ASD, the molecular diagnostic yields of CMA and WES were comparable, and the combined molecular diagnostic yield was higher in children with more complex morphological phenotypes in comparison with the children in the essential category. If replicated in additional populations, these findings may inform appropriate selection of molecular diagnostic testing for children affected by ASD.
Importance
The prevalence of autism spectrum disorders (ASDs) has increased markedly in recent decades, which researchers have suggested could be caused in part by nonetiologic factors such as changes in diagnosis reporting practices. To our knowledge, no study has quantified the degree to which changes in reporting practices might explain this increase. Danish national health registries have undergone a change in diagnostic criteria in 1994 and the inclusion of outpatient contacts to health registries in 1995.Objective
To quantify the effect of changes in reporting practices in Denmark on reported ASD prevalence. Design, Setting, and Participants
We used a population-based birth cohort approach that includes information on all individuals with permanent residence in Denmark. We assessed all children born alive from January 1, 1980, through December 31, 1991, in Denmark (n = 677 915). The children were followed up from birth until ASD diagnosis, death, emigration, or the end of follow-up on December 31, 2011, whichever occurred first. The analysis uses a stratified Cox proportional hazards regression model with the changes in reporting practices modeled as time-dependent covariates.Exposures
The change in diagnostic criteria in 1994 and the inclusion of outpatient diagnoses in 1995.Main Outcomes and Measures
Autism spectrum disorders.Results
For Danish children born during the study period, 33% (95% CI, 0%-70%) of the increase in reported ASD prevalence could be explained by the change in diagnostic criteria alone; 42% (95% CI, 14%-69%), by the inclusion of outpatient contacts alone; and 60% (95% CI, 33%-87%), by the change in diagnostic criteria and the inclusion of outpatient contacts.Conclusions and Relevance
Changes in reporting practices can account for most (60%) of the increase in the observed prevalence of ASDs in children born from 1980 through 1991 in Denmark. Hence, the study supports the argument that the apparent increase in ASDs in recent years is in large part attributable to changes in reporting practices.
Background
Phenotypic heterogeneity in autism has long been conjectured to be a major hindrance to the discovery of genetic risk factors, leading to numerous attempts to stratify children based on phenotype to increase power of discovery studies. This approach, however, is based on the hypothesis that phenotypic heterogeneity closely maps to genetic variation, which has not been tested. Our study examines the impact of sub-phenotyping of a well-characterized ASD sample on genetic homogeneity and the ability to discover common genetic variants conferring liability to ASD.
Methods
Genome-wide genotypic data of 2576 families from the Simons Simplex Collection (SSC) were analyzed in the overall sample and phenotypic subgroups defined on the basis of diagnosis, IQ, and symptom profiles. We conducted a family-based association study as well as estimating heritability and evaluating allele scores for each phenotypic subgroup.
Results
Association analyses revealed no genome-wide significant association signal. Sub-phenotyping did not increase power substantially. Moreover, allele scores built from the most associated SNPs, based on the odds ratio in the full sample, predicted case status in subsets of the sample equally well and heritability estimates were very similar for all subgroups.
Conclusions
In genome-wide association analysis of the SSC sample, reducing phenotypic heterogeneity had at most a modest impact on genetic homogeneity. Our results are based on a relatively small sample, one with greater homogeneity than the entire population; if they apply more broadly, they imply that analysis of sub-phenotypes is not a productive path forward for discovering genetic risk variants in ASD.
The last several years have marked a turning point in the genetics of autism spectrum disorder (ASD) due to rapidly advancing genomic technologies. As the pool of bona fide risk genes and regions accumulates, several key themes have emerged: these include the important role of rare and de novo mutation, the biological overlap among so-called syndromic and 'idiopathic' ASD, the elusive nature of the common variant contribution to risk, and the observation that the tremendous locus heterogeneity underlying ASD appears to converge on a relatively small number of key biological processes. Perhaps most striking has been the revelation that ASD mutations show tremendous phenotypic variability ranging from social disability to schizophrenia, intellectual disability, language impairment, epilepsy and typical development.
Heterogeneity within the autism diagnosis obscures the genetic basis of the disorder and impedes our ability to develop effective treatments. We found that by using two readily available tests, autism can be divided into two subgroups, “essential autism” and “complex autism,” with different outcomes and recurrence risks. Complex autism consists of individuals in whom there is evidence of some abnormality of early morphogenesis, manifested by either significant dysmorphology or microcephaly. The remainder have “essential autism.” From 1995 to 2001, 260 individuals who met DSM-IV criteria for autistic disorder were examined. Five percent (13/260) were microcephalic and 16% (41/260) had significant physical anomalies. Individually, each trait predicted a poorer outcome. Together they define the “complex autism” subgroup, comprising 20% (46/233) of the total autism population. Individuals with complex autism have lower IQs (P = 0.006), more seizures (P = 0.0008), more abnormal EEGs (46% vs. 30%), more brain abnormalities by MRI (28% vs. 13%). Everyone with an identifiable syndrome was in the complex group. Essential autism defines the more heritable group with higher sib recurrence (4% vs. 0%), more relatives with autism (20% vs. 9%), and higher male to female ratio (6.5:1 vs. 3.2:1). Their outcome was better with higher IQs (P = 0.02) and fewer seizures (P = 0.0008). They were more apt to develop autism with a regressive onset (43% vs. 23%, P = 0.02). Analysis of the features predictive of poor outcome (IQ < 55, functionally non-verbal) showed that microcephaly was 100% specific but only 14% sensitive; the presence of physical anomalies was 86% specific and 34% sensitive. The two tests combined yielded 87% specificity, 47% sensitivity, and an odds ratio of 4.8:1 for poor outcome. Separating essential from complex autism should be the first diagnostic step for children with autism spectrum disorders as it allows better prognostication and counseling. Definition of more homogeneous populations should increase power of research analyses. © 2005 Wiley-Liss, Inc.
In this issue, a pair of studies (Levy et al. and Sanders et al.) identify several de novo copy-number variants that together account for 5%-8% of cases of simplex autism spectrum disorders. These studies suggest that several hundreds of loci are likely to contribute to the complex genetic heterogeneity of this group of disorders. An accompanying study in this issue (Gilman et al.), presents network analysis implicating these CNVs in neural processes related to synapse development, axon targeting, and neuron motility.
The prevalence, in children aged under 15, of severe impairments of social interaction, language abnormalities, and repetitive stereotyped behaviors was investigated in an area of London. A "socially impaired" group (more than half of whom were severely retarded) and a comparison group of "sociable severely mentally retarded" children were identified. Mutism or echolalia, and repetitive stereotyped behaviors were found in almost all the socially impaired children, but to a less marked extent in a minority of the sociable severely retarded. Certain organic conditions were found more often in the socially impaired group. A subgroup with a history of Kanner's early childhood autism could be identified reliably but shared many abnormalities with other socially impaired children. The relationships between mental retardation, typical autism, and other conditions involving social impairment were discussed, and a system of classification based on quality of social interaction was considered.