Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Feb 11;5(1):11.
doi: 10.1186/2040-2392-5-11.

Behavioral signatures related to genetic disorders in autism

Hilgo Bruining  1   2 , Marinus Jc Eijkemans  2   3 , Martien Jh Kas  2 , Sarah R Curran  4 , Jacob As Vorstman #  1 , Patrick F Bolton #  4
Affiliations

Behavioral signatures related to genetic disorders in autism

Hilgo Bruining et al. Mol Autism. .

Abstract

Background: Autism spectrum disorder (ASD) is well recognized to be genetically heterogeneous. It is assumed that the genetic risk factors give rise to a broad spectrum of indistinguishable behavioral presentations.

Methods: We tested this assumption by analyzing the Autism Diagnostic Interview-Revised (ADI-R) symptom profiles in samples comprising six genetic disorders that carry an increased risk for ASD (22q11.2 deletion, Down's syndrome, Prader-Willi, supernumerary marker chromosome 15, tuberous sclerosis complex and Klinefelter syndrome; total n = 322 cases, groups ranging in sample sizes from 21 to 90 cases). We mined the data to test the existence and specificity of ADI-R profiles using a multiclass extension of support vector machine (SVM) learning. We subsequently applied the SVM genetic disorder algorithm on idiopathic ASD profiles from the Autism Genetics Resource Exchange (AGRE).

Results: Genetic disorders were associated with behavioral specificity, indicated by the accuracy and certainty of SVM predictions; one-by-one genetic disorder stratifications were highly accurate leading to 63% accuracy of correct genotype prediction when all six genetic disorder groups were analyzed simultaneously. Application of the SVM algorithm to AGRE cases indicated that the algorithm could detect similarity of genetic behavioral signatures in idiopathic ASD subjects. Also, affected sib pairs in the AGRE were behaviorally more similar when they had been allocated to the same genetic disorder group.

Conclusions: Our findings provide evidence for genotype-phenotype correlations in relation to autistic symptomatology. SVM algorithms may be used to stratify idiopathic cases of ASD according to behavioral signature patterns associated with genetic disorders. Together, the results suggest a new approach for disentangling the heterogeneity of ASD.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Overview of the different steps undertaken in the study. Step 1: development of SVM classifier to assess the presence and strength of behavioral signatures among genetic syndromes. Step 2: application of the classifier derived in step 1 to AGRE samples to test if similarity in behavioral signatures can be detected among idiopathic ASD subjects. Step 3: application of classifier derived in step 1 to sibling pairs with idiopathic ASD (AGRE) to test relative familiality of behavioral signatures derived from genetic syndromes. AGRE, Autism Genetics Resource Exchange; ASD, autism spectrum disorder; SVM, support vector machine.
Figure 2
Figure 2
PCA plot of ADI-R profiles of subjects in the genetic disorder sample. Colors/numbers denoting genetic disorder subgroups. 1, 22q11.2 deletion syndrome; 2, Down’s syndrome; 3, Prader-Willi syndrome; 4, supernumerary marker chromosome 15; 5, tuberous sclerosis complex; 6, Klinefelter syndrome. ADI-R, Autism Diagnostic Interview-Revised; PCA, principal component analysis.
Figure 3
Figure 3
SVM predicted probabilities of the original genetic groups, AGRE0 singleton dataset and randomly generated scores for the AGRE0 singleton dataset. Mean SVM probabilities differed significantly between the genetic groups and AGRE0 (P = 0.0024), between the genetic groups and random data (P <0.001) and between AGRE0 and random data (P <0.001). SVM, support vector machine.
Figure 4
Figure 4
PCA plot of ADI-R profiles of subjects in the genetic disorder sample, with the AGRE0 subsample inserted. PC2 is the dimension with the most differentiating contrast among the genetic disorder groups. AGRE0, on average, has negative values on PC1 and is around 0 on PC2. The TSC group (5) is also on average 0 on PC2 similar to AGRE0 and has the most negative average on PC1. Groups 1, 4 and 6 also display some closeness to AGRE0. Colors/numbers/letters denote genetic disorder subgroups. 1, 22q11.2 deletion syndrome; 2, Down’s syndrome; 3, Prader-Willi syndrome; 4, supernumerary marker chromosome 15, 5, tuberous sclerosis complex, 6, Klinefelter syndrome; A, AGRE0. ADI-R, Autism Diagnostic Interview-Revised; PCA, principal component analysis; TSC, tuberous sclerosis complex.
Figure 5
Figure 5
Correlation of SVM predicted probabilities between AGRE siblings. AGRE, Autism Genetics Resource Exchange; SVM, support vector machine.
See this image and copyright information in PMC

References

    1. Betancur C. Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res. 2011;1380:42–77. - PubMed
    1. Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet. 2008;9(5):341–355. doi: 10.1038/nrg2346. - DOI - PMC - PubMed
    1. Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD, Bailey AJ, Baird G, Battaglia A, Berney T, Bolshakova N, Bölte S, Bolton PF, Bourgeron T, Brennan S, Brian J, Bryson SE, Carson AR, Casallo G, Casey J, Chung BH, Cochrane L, Corsello C. et al.Functional impact of global rare copy number variation in autism spectrum disorders. Nature. 2010;466(7304):368–372. doi: 10.1038/nature09146. - DOI - PMC - PubMed
    1. Sanders SJ, Ercan-Sencicek AG, Hus V, Luo R, Murtha MT, Moreno-De-Luca D, Chu SH, Moreau MP, Gupta AR, Thomson SA, Mason CE, Bilguvar K, Celestino-Soper PB, Choi M, Crawford EL, Davis L, Wright NR, Dhodapkar RM, DiCola M, DiLullo NM, Fernandez TV, Fielding-Singh V, Fishman DO, Frahm S, Garagaloyan R, Goh GS, Kammela S, Klei L, Lowe JK, Lund SC. et al.Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron. 2011;70(5):863–885. doi: 10.1016/j.neuron.2011.05.002. - DOI - PMC - PubMed
    1. Scherer SW, Dawson G. Risk factors for autism: translating genomic discoveries into diagnostics. Hum Genet. 2011;130(1):123–148. doi: 10.1007/s00439-011-1037-2. - DOI - PubMed