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Disentangling the Heterogeneity of Autism Spectrum Disorders: The Identification of ‘Signature' Behavioural Phenotypes Derived From Specific Genetic Syndromes

Friday, 3 May 2013: 09:00-13:00
Banquet Hall (Kursaal Centre)
H. Bruining1, R. J. Eijkemans2, M. J. Kas3, S. R. Curran4, J. A. Vorstman5 and P. F. Bolton6, (1)Psychiatry, Brain Centre Rudolf Magnus, Amsterdam, Netherlands, (2)Brain Centre Rudolf Magnus, Utrecht, Netherlands, (3)Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands, (4)Kings College London, London, United Kingdom, (5)Psychiatry, Brain Centre Rudolf Magnus, Utrecht, Netherlands, (6)Institute of Psychiatry, King's College London, London, United Kingdom
Background: Aetiological and phenotypic heterogeneity in autism spectrum disorder poses major challenges to mapping causal pathophysiological pathways.   

Objectives: In order to disentangle this heterogeneity, we conducted a ‘proof of principle’ study to test the hypothesis that there exist ‘signature’ autism behavioural phenotypes that index underlying genetic risk and that these behavioural profiles can be used to help disentangle heterogeneity.

Methods: We tested the specificity autism behavioural phenotypes  using Support Vector Learning Machine Analysis (SVM) on samples comprising  six different genetic syndromes that carry an increased risk for autism spectrum disorder: 22q11.2 deletion syndrome, Down’s syndrome, Prader Willi syndrome, Supernumerary Marker chromosome 15, Tuberous sclerosis complex and Klinefelter syndrome (n = 322, groups ranging 21-90).

Results: The SVM analysis of items from the autism diagnostic interview  identified syndrome specific behavioural phenotypes with   63 % accuracy (compared to randome accuracy of 23%). We next tested whether these ‘signature’ behavioural phenotypes could be identified in idiopathic cases of autism spectrum disorder and whether they exhibited a liability to familiality, by analyzing autism diagnostic interview items from families collected as part of the Autism Genetics Resource Exchange (AGRE). These analyses indicated that the signatures behavioural profiles occured significantly more often than random expectation, with 63% of probands exhibiting the signature behavioural profile associated with  Tuberous Sclerosis Complex.  Furthermore, examination of the profiles in the probands siblings indicated that the ‘signature’ behavioural phenotypes exhibited significant familiality.

Conclusions: These results indicate that genetic disorders associated with autism spectrum disorders exhibit distinctive behavioural phenotypes and that similar ‘signature’  phenotypes  exist in cases with idiopathic autism spectrum disorder and that these ‘breed true’ within families.  Together the findings indicate that heterogeneity in the behavioural manifestations of autism spectrum disorder index the nature of the underlying genetic risk in pathophysiology. These approaches provide a conceptual approach to disentangling heterogeneity and subtyping cases for more personalized treatments.

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