Skip to main content
SpringerLink
Log in

Modeling the Autism Spectrum Disorder Phenotype

  • Data Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Autism Spectrum Disorder (ASD) is highly heritable, and although there has been active research in an attempt to discover the genetic factors underlying ASD, diagnosis still depends heavily on behavioral assessments. Recently, several large-scale initiatives, including those of the Autism Consortium, have contributed to the collection of extensive information from families affected by ASD. Our goal was to develop an ontology that can be used 1) to provide improved access to the data collected by those who study ASD and other neurodevelopmental disorders, and 2) to assess and compare the characteristics of the instruments that are used in the assessment of ASD. We analyzed two dozen instruments used to assess ASD, studying the nature of the questions asked and items assessed, the method of delivery, and the overall scope of the content. These data together with the extensive literature on ASD contributed to our iterative development of an ASD phenotype ontology. The final ontology comprises 283 concepts distributed across three high-level classes, ‘Personal Traits’, ‘Social Competence’, and ‘Medical History’. The ontology is fully integrated with the Autism Consortium database, allowing researchers to pose ontology-based questions. The ontology also allows researchers to assess the degree of overlap among a set of candidate instruments according to several objective criteria. The ASD phenotype ontology has promise for use in research settings where extensive phenotypic data have been collected, allowing a concept-based approach to identifying behavioral features of importance and for correlating these with genotypic data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Note that the sum of the normalized questions in the three major branches is slightly higher than the number reported for the overall ontology. That is because in a few instances questions are mapped to concepts in more than one branch of the ontology. A question from CCC-2 illustrates: “moves the conversation to a favorite topic, even if others do not seem interested”. This question has to do with ‘Conversational Skills’ (found in the ‘Social Competence’ branch), and also relates to ‘Restricted and Unusual Interests’ (found in the ‘Personal Traits’ branch).

  2. Because a major branch of our ontology involves medical history, and because most ASD assessment instruments have few or no medical history concepts, we applied the objective measures only to combinations of instruments that included the Medical History (MH) assessment. This is on the assumption that in most research and clinical settings an instrument similar in coverage to the Autism Consortium Medical History assessment would be used.

  3. The concept is represented in the three instruments as “Self-Injury” (ADI-R), “Self-Injurious Behavior” (ADOS, RBS-R), and “Displays behaviors that cause injury to self” (VABS-II).

  4. ADI-R limits assessment to self-injurious behavior that “results in tissue damage”; RBS-R’s “Self-Injurious Behavior Subscale” includes measurements for specific acts “Hits self with body part”, “Hits self against surface or object”, “Hits self with object”, “Bites self”, “Pulls”, “Rubs or scratches self”, “Inserts finger or object”, and “Skin picking”.

  5. It may be that there are some instruments that are of interest to an investigator that are not included in the set used by the Autism Consortium. It should be relatively straightforward to map these additional instruments to the ontology, since we have provided definitions for each of the concepts in the ontology.

  6. It is important to note that unlike Wall et al. 2012 who propose using a small number of questions for diagnosing ASD, we are proposing a method for determining the best set of instruments that will cover the conceptual landscape of ASD, with a focus primarily on the research landscape where a goal of expanded data collection may need to be balanced with minimizing the investment of time and resources. It is possible that our methods might have implications for the future development of a comprehensive ASD instrument that can be administered in a shorter period of time, but that awaits further experimentation and validation.

  7. There may well be additional criteria for instrument selection that are independent of the criteria we have elucidated, including, among other things, whether the individual who is being tested has already had a full diagnostic workup, whether the research team already has experience with an instrument or set of instruments, and whether the expertise needed to administer an instrument is readily available or not within that setting.

References

  • Abrahams, B. S., & Geschwind, D. H. (2008). Advances in autism genetics: on the threshold of a new neurobiology. Nature Reviews Genetics, 9(5), 341–355.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Achenbach, T. M., & Dumenci, L. (2001). Advances in empirically based assessment: Revised cross-informant syndromes and new DSM-oriented scales for the CBCL, YSR, and TRF: comment on Lengua, Sadowksi, Friedrich, and Fischer. Journal of Consulting and Clinical Psychology, 69(4), 699–702.

    Article  CAS  PubMed  Google Scholar 

  • Achenbach, T. M., & Ruffle, T. M. (2000). The child behavior checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatrics in Review, 21(1), 265–271.

    Article  CAS  PubMed  Google Scholar 

  • AGRE—Autism Genetic Resource Exchange. https://research.agre.org/

  • Autism Consortium. http://autismconsortium.org/

  • Axelrod, B. N. (2002). Validity of the Wechsler abbreviated scale of intelligence and other very short forms of estimating intellectual functioning. Assessment, 9(1), 17–23.

    Article  PubMed  Google Scholar 

  • Baron-Cohen, S., Lombardo, M. V., Auyeung, B., Ashwin, E., Chakrabarti, B., & Knickmeyer, R. (2011). Why are autism spectrum conditions more prevalent in males? PLoS Biology, 9(6), e1001081.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Berg, J. M., & Geschwind, D. H. (2012). Autism genetics: searching for specificity and convergence. Genome Biology, 13(7), 247.

    Article  PubMed Central  PubMed  Google Scholar 

  • Bilder, R. M., Sabb, F. W., Parker, D. S., Kalar, D., Chu, W. W., Fox, J., et al. (2009). Cognitive ontologies for neuropsychiatric phenomics research. Cognitive Neuropsychiatry, 14(4–5), 419–450.

    Article  PubMed Central  PubMed  Google Scholar 

  • Bishop, D. V. M. (1998). Children’s communication checklist-2 (CCC-2). Development of the children’s communication checklist (CCC): a method for assessing qualitative aspects of communicative impairments in children. Journal of Child Psychology and Psychiatry, 39, 879–891.

    Article  CAS  PubMed  Google Scholar 

  • Bishop, D. V. M., & Baird, G. (2001). Parent and teacher report of pragmatic aspects of communication: use of the children’s communication checklist in a clinical setting. Developmental Medicine and Child Neurology, 43(12), 809–818.

    Article  CAS  PubMed  Google Scholar 

  • Bishop, S. L., Guthrie, W., Coffing, M., & Lord, C. (2011). Convergent validity of the mullen scales of early learning and the differential ability scales in children with autism spectrum disorders. American Journal on Intellectual and Developmental Disabilities, 116(5), 331–343.

    Article  PubMed  Google Scholar 

  • Bodenreider O. (2008). Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform. 67–79.

  • Bodfish JW, Symons FJ, Lewis MH (1999). Repetitive behavior scale-revised (RBS-R). Western Carolina Center Research Reports

  • Bodfish, J. W., Symons, F. J., Parker, D. E., & Lewis, M. H. (2000). Varieties of repetitive behavior in autism: comparisons to mental retardation. J Aut Dev Disord, 30(3), 237–243.

    Article  CAS  Google Scholar 

  • Bug, W. J., Ascoli, G. A., Grethe, J. S., Gupta, A., Fennema-Notestine, C., Laird, A. R., et al. (2008). The NIFSTD and BIRNLex vocabularies: Building comprehensive ontologies for neuroscience. Neuroinformatics, 6(3), 175–194.

    Article  PubMed Central  PubMed  Google Scholar 

  • Committee on Children With Disabilities. (2001). Technical report: the pediatrician’s role in the diagnosis and management of autistic spectrum disorder in children. Pediatrics, 107(5), E85.

    Article  Google Scholar 

  • Constantino, J. N., Przybeck, T., Friesen, D., & Todd, R. D. (2000). Reciprocal social behavior in children with and without pervasive developmental disorders. J Dev and Behavioral Pediatrics., 21, 2–11.

    Article  CAS  Google Scholar 

  • Constantino, J. N., Davis, S. A., Todd, R. D., et al. (2003). Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. Journal of Autism and Developmental Disorders, 33(4), 427–433.

    Article  PubMed  Google Scholar 

  • Dawson, G., Estes, A., Munson, J., Schellenberg, G., Bernier, R., & Abbott, R. (2007). Quantitative assessment of autism symptom-related traits in probands and parents: broader phenotype autism symptom scale. Journal of Autism and Developmental Disorders, 37(3), 523–536.

    Article  PubMed  Google Scholar 

  • Dean, R. S. (1978). Reliability and predictive validity of the dean laterality preference schedule with preadolescents. Perceptual and Motor Skills, 47(3 Pt 2), 1345–1346.

    Article  CAS  PubMed  Google Scholar 

  • Derogatis, L. R., Rickels, K., & Rock, A. F. (1976). The SCL-90 and the MMPI: a step in the validation of a new self-report scale. Brit J Psychiat., 128, 280–289.

    Article  CAS  PubMed  Google Scholar 

  • Devlin, B., & Scherer, S. W. (2012). Genetic architecture in autism spectrum disorder. Current Opinion in Genetics and Development, 22(3), 229–237.

    Article  CAS  PubMed  Google Scholar 

  • Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR), Washington, DC: American Psychiatric Association, 2000

  • Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM V), Washington DC: American Psychiatric Association, 2013. http://www.dsm5.org/Documents/Autism%20Spectrum%20Disorder%20Fact%20Sheet.pdf

  • Dunn, L. M., & Dunn, L. M. (1981). Peabody picture vocabulary test—Revised. Circle Pines: American Guidance Systems.

    Google Scholar 

  • Dutra, L., Campbell, L., & Westen, D. (2004). Quantifying clinical judgment in the assessment of adolescent psychopathology: reliability, validity, and factor structure of the child behavior checklist for clinician report. Journal of Clinical Psychology, 60(1), 65–85.

    Article  PubMed  Google Scholar 

  • Eaves, L. C., Wingert, H. D., Ho, H. H., & Mickelson, E. C. (2006). Screening for autism spectrum disorders with the social communication questionnaire. Journal of Developmental and Behavioral Pediatrics, 27(2 Suppl), S95–S103.

    Article  PubMed  Google Scholar 

  • El-Fishawy, P., & State, M. W. (2010). The genetics of autism: key issues, recent findings, and clinical implications. The Psychiatric Clinics of North America, 33(1), 83–105.

    Article  PubMed Central  PubMed  Google Scholar 

  • Falkmer, T., Anderson, K., Falkmer, M., & Horlin, C. (2013). Diagnostic procedures in autism spectrum disorders: a systematic literature review. European Child and Adolescent Psychiatry, 22(6), 329–340.

    Article  PubMed  Google Scholar 

  • Fischbach, G. D., & Lord, C. (2010). The Simons simplex collection: a resource for identification of autism genetic risk factors. Neuron, 68(2), 192–195.

    Article  CAS  PubMed  Google Scholar 

  • Folstein, S. E., & Rosen-Sheidley, B. (2001). Genetics of autism: complex aetiology for a heterogeneous disorder. Nature Reviews Genetics, 2(12), 943–955.

    Article  CAS  PubMed  Google Scholar 

  • Fombonne, E. (2009). Epidemiology of pervasive developmental disorders. Pediatric Research, 65(6), 591–598.

    Article  PubMed  Google Scholar 

  • Gallagher, C., & Burke, T. (2007). Age, gender and IQ effects on the Rey-Osterrieth complex figure test. British Journal of Clinical Psychology, 46(Pt 1), 35–45.

    Article  PubMed  Google Scholar 

  • Gardner, D., Akil, H., Ascoli, G. A., Bowden, D. M., Bug, W., Donohue, D. E., et al. (2008). The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics, 6(3), 149–160.

    Article  PubMed Central  PubMed  Google Scholar 

  • Geschwind, D. H. (2009). Advances in autism. Annual Review of Medicine, 60, 367–380.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworthy, L. (2000). Behavior rating inventory of executive function. Child Neuropsychology, 6(3), 235–238.

    Article  CAS  PubMed  Google Scholar 

  • Gioia, G. A., Espy, K. A., & Isquith, P. K. (2003). Behavior rating inventory of executive function-preschool version (BRIEF-P). Lutz: Psychological Assessment Resources.

    Google Scholar 

  • Hall, D., Huerta, M. F., McAuliffe, M. J., & Farber, G. K. (2012). Sharing heterogeneous data: the national database for autism research. Neuroinformatics, 10(4), 331–339.

    Article  PubMed  Google Scholar 

  • Hassanpour, S., O’Connor, M. J., & Das, A. K. (2011). Evaluation of semantic-based information retrieval methods in the autism phenotype domain. AMIA Annu Symp Proc., 2011, 569–577.

    PubMed Central  PubMed  Google Scholar 

  • Hoehndorf, R., Dumontier, M., Gkoutos, G. V. (2012). Evaluation of research in biomedical ontologies. Brief Bioinform. [Epub ahead of print]

  • Homack, S., Lee, D., & Riccio, C. A. (2005). Test review: Delis-Kaplan executive function system. Journal of Clinical and Experimental Neuropsychology, 27(5), 599–609.

    Article  PubMed  Google Scholar 

  • Huerta, M., & Lord, C. (2012). Diagnostic evaluation of autism spectrum disorders. Pediatric Clinics of North America, 59(1), 103–111. xi.

    Article  PubMed Central  PubMed  Google Scholar 

  • Imam, F. T., Larson, S. D., Bandrowski, A., Grethe, J. S., Gupta, A., & Martone, M. E. (2012). Development and use of Ontologies inside the neuroscience information framework: a practical approach. Frontiers in Genetics, 3, 111.

    Article  PubMed Central  PubMed  Google Scholar 

  • International Classification of Functioning, Disability, and Health. World Health Organization. http://www.who.int/classifications/icf/

  • Isquith, P. K., Gioia, G. A., & Espy, K. A. (2004). Executive function in preschool children: examination through everyday behavior. Developmental Neuropsychology, 26(1), 403–422.

    Article  PubMed  Google Scholar 

  • Johnson, C. P., & Myers, S. M. (2007). Council on children with disabilities. Identification and evaluation of children with autism spectrum disorders. Pediatrics, 120, 1183–1215.

    Article  PubMed  Google Scholar 

  • Kanner, L. (1943). Autistic disturbances of affective contact. The Nervous Child, 250(2), 217–250.

    Google Scholar 

  • Keith, T. Z. (1990). Confirmatory and hierarchical confirmatory analysis of the differential ability scales. Journal of Psychoeducational Assessment, 8(3), 391–405.

    Article  Google Scholar 

  • Keith, T. Z., Low, J. A., Reynolds, M. R., Patel, P. G., & Ridley, K. P. (2010). Higher-order factor structure of the differential ability scales–II: consistency across ages 4 to 17. Psychology in the Schools, 47(7), 676–697.

    Google Scholar 

  • Kim, S. H., & Lord, C. (2012). Combining information from multiple sources for the diagnosis of autism spectrum disorders for toddlers and young preschoolers from 12 to 47 months of age. Journal of Child Psychology and Psychiatry, 53(2), 143–151.

    Article  PubMed Central  PubMed  Google Scholar 

  • Kirkwood, M. W., Weiler, M. D., Bernstein, J. H., Forbes, P. W., & Waber, D. P. (2001). Sources of poor performance on the Rey-Osterrieth complex figure test among children with learning difficulties: a dynamic assessment approach. Clinical Neuropsychology, 15(3), 345–356.

    Article  CAS  Google Scholar 

  • Lajonchere, C. M., & AGRE Consortium. (2010). Changing the landscape of autism research: the autism genetic resource exchange. Neuron, 68(2), 187–191.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Lam, K. S., & Aman, M. G. (2007). The repetitive behavior scale-revised: independent validation in individuals with autism spectrum disorders. Journal of Autism and Developmental Disorders, 37(5), 855–866.

    Article  PubMed  Google Scholar 

  • Larson, S. D., & Martone, M. E. (2009). Ontologies for neuroscience: what are they and what are they good for? Frontiers in Neuroscience, 3(1), 60–67.

    Article  PubMed Central  PubMed  Google Scholar 

  • LeCouteur, A., Rutter, M., Lord, C., & Rios, P. (1989). Autism diagnostic interview: a standardized investigator-based instrument. Journal of Autism and Developmental Disorders, 19(3), 363–387.

    Article  CAS  Google Scholar 

  • LeCouteur, A., Lord, C., & Rutter, M. (2003). Autism diagnostic interview-revised (ADI-R). Los Angeles: Western Psychological Services.

    Google Scholar 

  • Lengua, L. J., Sadowski, C. A., Friedrich, W. N., & Fisher, J. (2001). Rationally and empirically derived dimensions of children’s symptomatology: expert ratings and confirmatory factor analyses of the CBCL. Journal of Consulting and Clinical Psychology, 69(4), 683–698.

    Article  CAS  PubMed  Google Scholar 

  • Levy, S. E., Mandell, D. S., & Schultz, R. T. (2009). Autism. Lancet., 374(9701), 1627–1638.

    Article  Google Scholar 

  • Lord, C., & Jones, R. M. (2012). Annual research review: re-thinking the classification of autism spectrum disorders. Journal of Child Psychology and Psychiatry, 53(5), 490–509.

    Article  PubMed Central  PubMed  Google Scholar 

  • Lord, C., Rutter, M., Goode, S., Heemsbergen, J., Jordan, H., Mawhood, L., et al. (1989). Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. Journal of Autism and Developmental Disorders, 19(2), 185–212.

    Article  CAS  PubMed  Google Scholar 

  • Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685.

    Article  CAS  PubMed  Google Scholar 

  • Lord, C., Risi, S., Lambrecht, L., et al. (2000). The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205–223.

    Article  CAS  PubMed  Google Scholar 

  • Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (2001). Autism diagnostic observation schedule (ADOS). Los Angeles: Western Psychological Services.

    Google Scholar 

  • Macedoni-Lukšič, M., Petrič, I., Cestnik, B., & Urbančič, T. (2011). Developing a deeper understanding of autism: connecting knowledge through literature mining. Autism Res Treat, 2011, 307152.

    PubMed Central  PubMed  Google Scholar 

  • Mahjouri, S., & Lord, C. E. (2012). What the DSM-5 portends for research, diagnosis, and treatment of autism spectrum disorders. Current Psychiatry Reports, 14(6), 739–747.

    Article  PubMed  Google Scholar 

  • Massa, J., Gomes, H., Tartter, V., Wolfson, V., & Halperin, J. M. (2008). Concordance rates between parent and teacher clinical evaluation of language fundamentals observational rating scale. International Journal of Language & Communication Disorders, 43(1), 99–110.

    Article  Google Scholar 

  • Matson, J. L. (2007). Current status of differential diagnosis for children with autism spectrum disorders. Research in Developmental Disabilities, 28(2), 109–118.

    Article  PubMed  Google Scholar 

  • Mattila, M. L., Kielinen, M., Linna, S. L., Jussila, K., Ebeling, H., Bloigu, R., et al. (2011). Autism spectrum disorders according to DSM-IV-TR and comparison with DSM-5 draft criteria: an epidemiological study. Journal of the American Academy of Child and Adolescent Psychiatry, 50(6), 583–592.

    Article  PubMed  Google Scholar 

  • McPartland, J., & Volkmar, F. R. (2012). Autism and related disorders. Handbook of Clinical Neurology, 106, 407–418.

    Article  PubMed  Google Scholar 

  • Medical Subject Headings. National Library of Medicine. http://www.nlm.nih.gov/mesh/

  • Miles, J. H. (2011). Autism spectrum disorders–a genetics review. Genetics in Medicine, 13(4), 278–294.

    Article  PubMed  Google Scholar 

  • Moldin, S. O., & Rubenstein, J. L. R. (2006). Understanding autism: from basic neuroscience to treatment. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Mullen, E. M. (1995). Mullen scales of early learning, AGS edition (Mullen). Circle Pines: American Guidance Service.

    Google Scholar 

  • NCBO—National Center for Biomedical Ontologies. http://www.bioontology.org/wiki/index.php/Ontology_Metrics.

  • NDAR - National Database for Autism Research. http://ndar.nih.gov/

  • Noy, N. F., Shah, N. H., Whetzel, P. L., Dai, B., Dorf, M., Griffith, N., Jonquet, C., Rubin, D. L., Storey, M. A., Chute, C. G., Musen, M.A. (2009). BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Research 37

  • Noy, N., Tudorache, T., Nyulas, C., & Musen, M. (2010). The ontology life cycle: integrated tools for editing, publishing, peer review, and evolution of ontologies. AMIA Annu Symp Proc, 2010, 552–556.

    PubMed Central  PubMed  Google Scholar 

  • Pandolfi, V., Magyar, C. I., & Dill, C. A. (2009). Confirmatory factor analysis of the child behavior checklist 1.5-5 in a sample of children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 39(7), 986–995.

    Article  PubMed Central  PubMed  Google Scholar 

  • Petric, I., Urbanicic, T., & Cestnik, B. (2007). Discovering hidden knowledge from biomedical literature. Informatica, 31, 15–20.

    Google Scholar 

  • Price, L. R., Raju, N., Lurie, A., Wilkins, C., & Zhu, J. (2006). Conditional standard errors of measurement for composite scores on the Wechsler preschool and primary scale of intelligence-third edition. Psychological Reports, 98(1), 237–252.

    Article  PubMed  Google Scholar 

  • Protégé ontology editor and knowledge-base framework. http://protege.stanford.edu/

  • Risi, S., Lord, C., Gotham, K., Corsello, C., Chrysler, C., Szatmari, P., et al. (2006). Combining information from multiple sources in the diagnosis of autism spectrum disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 45(9), 1094–1103.

    Article  PubMed  Google Scholar 

  • Rubin, D. L., Shah, N. H., & Noy, N. F. (2008). Biomedical ontologies: a functional perspective. Briefings in Bioinformatics, 9(1), 75–90.

    Article  PubMed  Google Scholar 

  • Rutter, M. L. (2011). Progress in understanding autism: 2007–2010. Journal of Autism and Developmental Disorders, 41(4), 395–404.

    Article  PubMed  Google Scholar 

  • Scattone, D., Raggio, D. J., & May, W. (2011). Comparison of the Vineland adaptive behavior scales, second edition, and the Bayley scales of infant and toddler development, third edition. Psychological Reports, 109(2), 626–634.

    Article  PubMed  Google Scholar 

  • Scherer, S. W., & Dawson, G. (2011). Risk factors for autism: translating genomic discoveries into diagnostics. Human Genetics, 130(1), 123–148.

    Article  PubMed  Google Scholar 

  • Simons Foundation Autism Research Initiative (SFARI). http://sfari.org/resources/

  • Sparrow, S. S., & Cicchetti, D. V. (1985). Diagnostic uses of the Vineland adaptive behavior scales. Journal of Pediatric Psychology, 10(2), 215–225.

    Article  CAS  PubMed  Google Scholar 

  • Sparrow, S. S., Cicchetti, D. V., & Balla, D. A. (2005). Vineland adaptive behavior scales, second edition (Vineland-II). Circle Pines: AGS Publishing.

    Google Scholar 

  • Tager-Flusberg, H., & Joseph, R. M. (2003). Identifying neurocognitive phenotypes in autism. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 358(1430), 303–314.

    Article  PubMed Central  PubMed  Google Scholar 

  • Tomanik, S. S., Pearson, D. A., Loveland, K. A., Lane, D. M., & Bryant, S. J. (2007). Improving the reliability of autism diagnoses: examining the utility of adaptive behavior. Journal of Autism and Developmental Disorders, 37(5), 921–928.

    Article  PubMed  Google Scholar 

  • Tsatsanis, K. D., Dartnall, N., Cicchetti, D., Sparrow, S. S., Klin, A., & Volkmar, F. R. (2003). Concurrent validity and classification accuracy of the Leiter and Leiter-R in low-functioning children with autism. Journal of Autism and Developmental Disorders, 33(1), 23–30.

    Article  PubMed  Google Scholar 

  • Tu, S. W., Tennakoon, L., O’Connor, M., Shankar, R., & Das, A. (2008). Using an integrated ontology and information model for querying and reasoning about phenotypes: the case of autism. AMIA Annu Symp Proc, 6, 727–731.

    Google Scholar 

  • Unified Medical Language System. National Library of Medicine. http://www.nlm.nih.gov/research/umls/

  • Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQL 4.0: reliability and validity of the pediatric quality of life inventory version 4.0 generic core scales in healthy and patient populations. Medical Care, 39(8), 800–812.

    Article  CAS  PubMed  Google Scholar 

  • Volkmar, F. R., & Reichow, B. (2013). Autism in DSM-5: progress and challenges. Mol Autism, 4(1), 13.

    Article  PubMed Central  PubMed  Google Scholar 

  • Volkmar, F. R., Sparrow, S. S., Goudreau, D., Cicchetti, D. V., Paul, R., & Cohen, D. J. (1987). Social deficits in autism: an operational approach using the Vineland adaptive behavior scales. Journal of the American Academy of Child and Adolescent Psychiatry, 26(2), 156–161.

    Article  CAS  PubMed  Google Scholar 

  • Volkmar, F. R., State, M., & Klin, A. (2009). Autism and autism spectrum disorders: diagnostic issues for the coming decade. Journal of Child Psychology and Psychiatry, 50(1–2), 108–115.

    Article  PubMed  Google Scholar 

  • Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1999). Comprehensive test of phonological processing. Austin: PRO-ED, Inc.

    Google Scholar 

  • Walker, D. R., Thompson, A., Zwaigenbaum, L., Goldberg, J., Bryson, S. E., Mahoney, W. J., et al. (2004). Specifying PDD-NOS: a comparison of PDD-NOS, Asperger syndrome, and autism. Journal of the American Academy of Child and Adolescent Psychiatry, 43(2), 172–180.

    Article  PubMed  Google Scholar 

  • Wall, D. P., Kosmicki, J., Deluca, T. F., Harstad, E., & Fusaro, V. A. (2012). Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry., 2, e100.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Ward-King, J., Cohen, I. L., Penning, H., & Holden, J. J. A. (2010). Brief report: telephone administration of the autism diagnostic interview-revised: Reliability and suitability for use in research. Journal of Autism and Developmental Disorders, 40, 1285–1290.

    Article  PubMed  Google Scholar 

  • Wechsler, D. (2003). Wechsler preschool and primary scale of intelligence – Third Edition. San Antonio: The Psychological Corp.

    Google Scholar 

  • Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: better or worse than the DSM-IV? Research in Developmental Disabilities, 32(2), 768–773.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors were supported in part by grants from an Anonymous Foundation, the Autism Consortium, and the Harvard Clinical and Translational Science Center (NIH/NCRR UL1 RR025758-01). The authors thank Juliane Schneider and Cecilia Vernes for their contributions to the ontology and the team at MGH who developed the Autism Consortium database, including David Pauls and Julia O’Rourke.

Author information

Authors and Affiliations

  1. Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA

    Alexa T. McCray, Philip Trevvett & H. Robert Frost

  2. Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH, 03755, USA

    H. Robert Frost

Authors
  1. Alexa T. McCray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philip Trevvett
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Robert Frost
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexa T. McCray.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 170 kb)

ESM 2

(OWL 168 kb)

ESM 3

(PDF 233 kb)

ESM 4

(PDF 1655 kb)

ESM 5

(PDF 176 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

McCray, A.T., Trevvett, P. & Frost, H.R. Modeling the Autism Spectrum Disorder Phenotype. Neuroinform 12, 291–305 (2014). https://doi.org/10.1007/s12021-013-9211-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-013-9211-4

Keywords

Search

Navigation