Behavioral signatures related to genetic disorders in autism

Autism spectrum disorder (ASD) is a behaviorally defined syndrome characterized by variable abnormalities in social interactions and communication, in association with restricted interest patterns and unusual stereotyped behaviors. There has been a concerted effort over the last 20 years to identify causal genetic risk factors and as a result, an increasing number of rare, highly penetrant genetic variants are being implicated [1]. When present, these rare variants are thought to account for a large proportion of an individual’s genetic liability to the condition. Currently, specific genetic etiologies, including rare single nucleotide and copy number variants (CNVs) as well as larger chromosomal variations, can be identified in around 15 to 20% of patients [2‐5]. These findings highlight the complexity of the genetic architecture and heterogeneity of ASD and indicate that by using standard case–control designs, extremely large sample sizes will be required to unravel the heterogeneity and map the dysregulated signaling pathways involved in the pathophysiology of ASD [4, 6‐9].

The variability in phenotypic expression of autism observed in monozygotic twin pairs, coupled with the evidence from molecular genetic studies supporting a polygenic multi-factorial liability model has led to the recognition that the many genetic risk factors for autism give rise to a broad spectrum of behavioral presentations and hence the concept of autism as a spectrum disorder. The adoption of this model has led to an implicit assumption that specific genotype-phenotype correlations are unlikely to exist. However, there is evidence that ASD symptoms may be dissociable at the genetic level. Different genetic linkage regions have been obtained for social interaction and repetitive behavioral domains in ASD patients [10], and distinct developmental trajectories of social and repetitive behavior exist in the ASD population [11]. Moreover, in recent years, a growing interest has developed in the possibility that particular genetic disorders may give rise to characteristic patterns of autistic symptomatology. This interest is based on the assumption that perturbations in associated pathophysiological pathways would lead to relatively constrained and more specific phenotypic outcomes [12]. Indeed, a number of recent studies, involving a variety of genetic conditions including 16p11.2 and 7q11.23 CNVs, Williams syndrome, fragile X syndrome and neurofibromatosis, have indicated the existence of genetic disorder-specific behavioral profiles that encourage further efforts in this direction [4, 13‐16]. Building on these findings, we postulated that well-defined genetic conditions could give rise to relatively distinct patterns of autistic symptomatology. The designation of these patterns may be relevant to dissect ASD heterogeneity as other risk factors that perturb converging pathophysiological pathways, for example related to the genetic conditions, might lead to similar patterns of autistic symptomatology.

In the present study, we have undertaken a proof of concept study to determine if these genotype-phenotype correlations exist and whether they could be useful to disentangle the heterogeneity of ASD and complement future genetic studies. Support vector machine (SVM) learning was used to analyze ‘signatures’ of autistic symptomatology in six genetic developmental disorders associated with an increased risk for ASD [17‐20]. Based on the premise that other risk factors which dysregulate the same pathways may give rise to similar ‘signature’ patterns of behavior, we aimed to apply the SVM algorithms derived from genetic disorders to cases of idiopathic ASD. Finally, we investigated whether the SVM algorithm would detect enhanced behavioral similarity in affected sib pairs from the Autism Genetics Resource Exchange (AGRE) multiplex families. Figure 1 provides an overview of the different steps involved in the study.

This study demonstrates that patterns of autistic symptomatology can be associated with specific genetic disorders. There has been much speculation that such genotype-phenotype correlations exist but so far only limited evidence to support the conjecture. Our results are consistent with findings from animal research and suggest that different pathophysiological pathways underlie certain behavioral deficits [4, 45].

The current study is the first to test the specificity of genetic behavioral phenotypes using a machine learning paradigm. The ADI-R algorithm items comprised a comparatively small number of symptom features, yet we used this small set of items to classify our cases. The total number of correct allocations (63%) was substantial given the fact that five groups were compared. Indeed, this result was derived from one-by-one genetic disorder comparisons, in which strong contrast were evident. It was notable, however, that the SVM algorithm derived from the current sample differentiated between some classes better than others. This variability might be explained by the variation in sample sizes; thus, in future larger samples will need to be investigated. It was also notable that the ratings of the pattern of social dysfunction were among the best contributors to class prediction, raising the possibility that particular styles of social impairment may be related to particular genetic risk factors. Although differences in the typology of social impairments have been noted in ASD [46], differences in the types of social impairment have not been studied in detail and are only partially captured by the ADI-R items. For instance, social avoidance is commonly reported in fragile X syndrome, as another example of social behavioral specificity within a genetic disorder associated with ASD [47, 48]. It seems likely that with the incorporation of more symptoms and other phenotypic features, such as the presence of comorbid behavioral problems like those associated with ADHD [49], the ability to assign cases to specific classes of genetic disorder may be improved. The inclusion of other conditions such as fragile X syndrome may also help further map the patterns of genotype-phenotype correlations. Together, these extensions may reveal further contrasts or overlaps between genetic disorders that are biologically meaningful. For instance, it was already interesting that the prediction probabilities for SMC15 were similar to those for PWS. Both disorders are associated with abnormalities in the dosage of genes located in the 15q11-13 region and likely lead to perturbations in similar pathophysiological pathways.

The subjects of this study were included because they were ascertained for the presence of a genetic disorder and were assessed regardless of the presence or absence of behavioral concerns. Although this approach is likely to have minimized ascertainment biases, some bias cannot be ruled out. However, any enrichment of behavioral abnormalities in these cohorts is unlikely to give rise to the specific patterns of associations identified here. It was reassuring in this respect that the algorithm derived from all cases in the genetic disorder samples gave comparable results to the analyses that included only the subjects who scored above the ADI-R threshold for ASD. Analysis confirmed that IQ did not seem to act as a confounding factor in the SVM predictions. Also, the influence of age and medication as cofounds could be ruled out, as the ADI-R algorithm codes behaviors between 4 and 5 years old [35].

The application of the genetic disorder algorithm to AGRE samples indicated that the behavioral patterns observed in cases of idiopathic autism were not random. Therefore, these results could be used to estimate relative similarity to behavioral profiles designated from the genetic disorders. In addition, the sibling analysis showed correlation of SVM predictions between affected sib pairs. These findings indicate the feasibility to partition familiality into components according to patterns of autistic symptomatology, for example concordance in relative similarity to behavioral profiles related to the genetic disorders. This notion should be followed up by studies that incorporate genetic or pathway information to ascertain the behavior-based stratification in idiopathic samples. For instance, our allocation in idiopathic ASD to TSC-derived patterns may be supported by molecular data showing mammalian target of rapamycin (mTOR) pathway deregulation. Such a result would support the view that perturbation of the mTOR signaling cascade is a common pathophysiological feature of human neurological disorders, including mental retardation syndromes and ASDs [49]. If confirmed, such results could complement future gene searches, since stratification on the basis of behavioral profile may significantly increase the power to detect which (combination of) genetic disorder related pathways are most prominently involved. Indeed, the notion that pathophysiological processes are shared in syndromic and idiopathic cases of ASD is supported by a recent study that showed converging synaptic pathophysiology between syndromic (for example as a cause of a defined genetic disorder) and non-syndromic rodent models of autism [50]. Moreover, genotype stratification may also have important treatment implications, as other animal studies suggest that the best treatment approaches for some genetic disorders (for example fragile X syndrome) may be unsuitable for others (for example tuberous sclerosis) [49].

Our proof of concept study indicates the existence of ‘signature’ autistic behavioral profiles that index underlying genetic risk processes. These signatures may be helpful in disentangling the etiological and phenotypic heterogeneity evident in ASD, but warrant replication in larger and independent samples. The approach presented in this study could hold promise as a means of stratifying patients who may benefit from treatments targeted at specific pathways and as a way of identifying those patients in whom interventions may have unwanted effects.