Advertisement

An Autism Case History to Review the Systematic Analysis of Large-Scale Data to Refine the Diagnosis and Treatment of Neuropsychiatric Disorders

  • Isaac S. Kohane
    Correspondence
    Address correspondence to Isaac S. Kohane, M.D., Center for Biomedical Informatics, 10 Shattuck Street, Boston, MA 02115
    Affiliations
    Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
    Search for articles by this author

      Abstract

      Analysis of large-scale systems of biomedical data provides a perspective on neuropsychiatric disease that may be otherwise elusive. Described here is an analysis of three large-scale systems of data from autism spectrum disorder (ASD) and of ASD research as an exemplar of what might be achieved from study of such data. First is the biomedical literature that highlights the fact that there are two very successful but quite separate research communities and findings pertaining to genetics and the molecular biology of ASD. There are those studies positing ASD causes that are related to immunological dysregulation and those related to disorders of synaptic function and neuronal connectivity. Second is the emerging use of electronic health record systems and other large clinical databases that allow the data acquired during the course of care to be used to identify distinct subpopulations, clinical trajectories, and pathophysiological substructures of ASD. These systems reveal subsets of patients with distinct clinical trajectories, some of which are immunologically related and others which follow pathologies conventionally thought of as neurological. The third is genome-wide genomic and transcriptomic analyses which show molecular pathways that overlap neurological and immunological mechanisms. The convergence of these three large-scale data perspectives illustrates the scientific leverage that large-scale data analyses can provide in guiding researchers in an approach to the diagnosis of neuropsychiatric disease that is inclusive and comprehensive.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Biological Psychiatry
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Lederberg J.
        Infectious history.
        Science. 2000; 288: 287-293
        • Bear M.F.
        • Huber K.M.
        • Warren S.T.
        The mGluR theory of fragile X mental retardation.
        Trends Neurosci. 2004; 27: 370-377
        • Auerbach B.D.
        • Osterweil E.K.
        • Bear M.F.
        Mutations causing syndromic autism define an axis of synaptic pathophysiology.
        Nature. 2011; 480: 63-68
        • Ashwood P.
        • Wills S.
        • Van De Water J.
        The immune response in autism: a new frontier for autism research.
        J Leukoc Biol. 2006; 80: 1-15
        • Patterson P.H.
        Immune involvement in schizophrenia and autism: etiology, pathology and animal models.
        Behav Brain Res. 2009; 204: 313-321
        • Malkova N.V.
        • Yu C.Z.
        • Hsiao E.Y.
        • Moore M.J.
        • Patterson P.H.
        Maternal immune activation yields offspring displaying mouse versions of the three core symptoms of autism.
        Brain Behav Immun. 2012; 26: 607-616
        • Campbell M.G.
        • Kohane I.S.
        • Kong S.W.
        Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome.
        BMC Med Genomics. 2013; 6: 34
        • Yu T.W.
        • Chahrour M.H.
        • Coulter M.E.
        • Jiralerspong S.
        • Okamura-Ikeda K.
        • Ataman B.
        • et al.
        Using whole-exome sequencing to identify inherited causes of autism.
        Neuron. 2013; 77: 259-273
        • Bartlett C.W.
        • Goedken R.
        • Vieland V.J.
        Effects of updating linkage evidence across subsets of data: reanalysis of the autism genetic resource exchange data set.
        Am J Hum Genet. 2005; 76: 688-695
        • Ma D.
        • Cuccaro M.
        • Jaworski J.
        • Haynes C.
        • Stephan D.
        • Parod J.
        • et al.
        Dissecting the locus heterogeneity of autism: significant linkage to chromosome 12q14.
        Mol Psychiatry. 2007; 12: 376-384
        • Lai M.C.
        • Lombardo M.V.
        • Baron-Cohen S.
        Autism.
        Lancet. 2013; 383: 896-910
        • Rosti R.O.
        • Sadek A.A.
        • Vaux K.K.
        • Gleeson J.G.
        The genetic landscape of autism spectrum disorders.
        Dev Med Child Neurol. 2014; 56: 12-18
        • Kohane I.S.
        • Eran A.
        Can we measure autism?.
        Sci Transl Med. 2013; 5: 209-218
        • Abrahams B.S.
        • Arking D.E.
        • Campbell D.B.
        • Mefford H.C.
        • Morrow E.M.
        • Weiss L.A.
        • et al.
        SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs).
        Mol Autism. 2013; 4: 36
        • Caelleigh A.S.
        PubMed Central and the new publishing landscape: shifts and tradeoffs.
        Acad Med. 2000; 75 ([editorial]): 4-10
        • Liu R.
        • Wang X.
        • Chen G.Y.
        • Dalerba P.
        • Gurney A.
        • Hoey T.
        • et al.
        The prognostic role of a gene signature from tumorigenic breast-cancer cells.
        N Engl J Med. 2007; 356: 217-226
        • Raponi M.
        • Dossey L.
        • Jatkoe T.
        • Wu X.
        • Chen G.
        • Fan H.
        • et al.
        MicroRNA classifiers for predicting prognosis of squamous cell lung cancer.
        Cancer Res. 2009; 69: 5776-5783
        • Kang H.
        • Chen I.M.
        • Wilson C.S.
        • Bedrick E.J.
        • Harvey R.C.
        • Atlas S.R.
        • et al.
        Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia.
        Blood. 2010; 115: 1394-1405
        • Silvester A.
        Jean Martin Charcot (1825–93) and John Hughlings Jackson (1835–1911): Neurology in France and England in the 19th century.
        J Med Biogr. 2009; 17: 210-213
        • Buntin M.B.
        • Jain S.H.
        • Blumenthal D.
        Health information technology: laying the infrastructure for national health reform.
        Health Aff (Millwood). 2010; 29: 1214-1219
        • Murphy S.
        • Churchill S.
        • Bry L.
        • Chueh H.
        • Weiss S.
        • Lazarus R.
        • et al.
        Instrumenting the health care enterprise for discovery research in the genomic era.
        Genome Res. 2009; 19: 1675-1681
        • Kohane I.S.
        Using electronic health records to drive discovery in disease genomics.
        Nat Rev Genet. 2011; 12: 417-428
        • Perlis R.H.
        • Iosifescu D.V.
        • Castro V.M.
        • Murphy S.N.
        • Gainer V.S.
        • Minnier J.
        • et al.
        Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.
        Psychol Med. 2012; 42: 41-50
        • Hoogenboom W.S.
        • Perlis R.H.
        • Smoller J.W.
        • Zeng-Treitler Q.
        • Gainer V.S.
        • Murphy S.N.
        • et al.
        Limbic system white matter microstructure and long-term treatment outcome in major depressive disorder: a diffusion tensor imaging study using legacy data.
        World J Biol Psychiatry. 2012; 15: 122-134
        • Kullo I.J.
        • Fan J.
        • Pathak J.
        • Savova G.K.
        • Ali Z.
        • Chute C.G.
        Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease.
        JAMIA. 2010; 17: 568-574
        • Chapman W.W.
        • Nadkarni P.M.
        • Hirschman L.
        • D’Avolio L.W.
        • Savova G.K.
        • Uzuner O.
        Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions.
        JAMIA. 2011; 18: 540-543
        • Savova G.K.
        • Masanz J.J.
        • Ogren P.V.
        • Zheng J.
        • Sohn S.
        • Kipper-Schuler K.C.
        • et al.
        Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.
        JAMIA. 2010; 17: 507-513
        • Kho A.N.
        • Pacheco J.A.
        • Peissig P.L.
        • Rasmussen L.
        • Newton K.M.
        • Weston N.
        • et al.
        Electronic medical records for genetic research: results of the eMERGE consortium.
        Sci Transl Med. 2011; 3: 79re71
        • Carroll R.J.
        • Thompson W.K.
        • Eyler A.E.
        • Mandelin A.M.
        • Cai T.
        • Zink R.M.
        • et al.
        Portability of an algorithm to identify rheumatoid arthritis in electronic health records.
        JAMIA. 2012; 19: e162-e169
        • Jha A.K.
        • DesRoches C.M.
        • Campbell E.G.
        • Donelan K.
        • Rao S.R.
        • Ferris T.G.
        • et al.
        Use of electronic health records in U.S. hospitals.
        N Engl J Med. 2009; 360: 1628-1638
        • Devoe J.E.
        • Gold R.
        • McIntire P.
        • Puro J.
        • Chauvie S.
        • Gallia C.A.
        Electronic health records vs. Medicaid claims: completeness of diabetes preventive care data in community health centers.
        Ann Fam Med. 2011; 9: 351-358
        • Perlis R.H.
        • Iosifescu D.V.
        • Castro V.M.
        • Murphy S.N.
        • Gainer V.S.
        • Minnier J.
        • et al.
        Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.
        Psychol Med. 2011; 42: 41-50
        • Gallagher P.J.
        • Castro V.
        • Fava M.
        • Weilburg J.B.
        • Murphy S.N.
        • Gainer V.S.
        • et al.
        Antidepressant response in patients with major depression exposed to NSAIDs: a pharmacovigilance study.
        Am J Psychiatry. 2012; 169: 1065-1072
        • Pato M.T.
        • Sobell J.L.
        • Medeiros H.
        • Abbott C.
        • Sklar B.M.
        • Buckley P.F.
        • et al.
        The genomic psychiatry cohort: Partners in discovery.
        Neuropsychiatr Genet. 2013; 162: 306-312
        • McMurry A.J.
        • Murphy S.N.
        • MacFadden D.
        • Weber G.
        • Simons W.W.
        • Orechia J.
        • et al.
        SHRINE: enabling nationally scalable multi-site disease studies.
        PloS One. 2013; 8: e55811
        • Weber G.M.
        • Murphy S.N.
        • McMurry A.J.
        • Macfadden D.
        • Nigrin D.J.
        • Churchill S.
        • et al.
        The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories.
        J Am Med Inform Assoc. 2009; 16: 624-630
      1. U.C. San Diego Health Sciences (2014): UC ReX frequently asked questions. Available at: http://ctri.ucsd.edu/Informatics/UC-ReX/Pages/ucrex-FAQ.aspx. Accessed October 1, 2013.

        • Kohane I.S.
        • McMurry A.
        • Weber G.
        • Macfadden D.
        • Rappaport L.
        • Kunkel L.
        • et al.
        The co-morbidity burden of children and young adults with autism spectrum disorders.
        PlOS One. 2012; 7: e33224
        • Ananthakrishnan A.N.
        • Cai T.
        • Savova G.
        • Cheng S.C.
        • Chen P.
        • Perez R.G.
        • et al.
        Improving case definition of Crohn’s disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach.
        Inflamm Bowel Dis. 2013; 19: 1411-1420
        • Doshi-Velez F.
        • Ge Y.
        • Kohane I.
        Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis.
        Pediatrics. 2014; 133: e54-e63
        • Iyer V.R.
        • Eisen M.B.
        • Ross D.T.
        • Schuler G.
        • Moore T.
        • Lee J.C.F.
        • et al.
        The transcriptional program in the response of human fibroblasts to serum.
        Science. 1999; 283 ([see comments]): 83-87
        • Kohane I.
        • McMurry A.
        • Weber G.
        • MacFadden D.
        • Rappaport L.
        • Kunkel L.
        • et al.
        The co-morbidity burden of children and young adults with autism spectrum disorders.
        PLoS ONE. 2012; 7: e33224
        • Atladóttir H.O.
        • Pedersen M.G.
        • Thorsen P.
        • Mortensen P.B.
        • Deleuran B.
        • Eaton W.W.
        • et al.
        Association of family history of autoimmune diseases and autism spectrum disorders.
        Pediatrics. 2009; 124: 687-694
        • Brown A.S.
        • Sourander A.
        • Hinkka-Yli-Salomäki S.
        • McKeague I.W.
        • Sundvall J.
        • Surcel H.-M.
        Elevated maternal C-reactive protein and autism in a national birth cohort.
        Mol Psychiatry. 2013; 19: 259-264
        • Sullivan S.
        • Rai D.
        • Golding J.
        • Zammit S.
        • Steer C.
        The association between autism spectrum disorder and psychotic experiences in the Avon longitudinal study of parents and children (ALSPAC) birth cohort.
        J Am Acad Child Adolesc Psychiatry. 2013; 52 (e802): 806-814
        • Kaelber D.C.
        • Foster W.
        • Gilder J.
        • Love T.E.
        • Jain A.K.
        Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data.
        J Am Med Inform Assoc. 2012; 19: 965-972
        • Voineagu I.
        • Wang X.
        • Johnston P.
        • Lowe J.K.
        • Tian Y.
        • Horvath S.
        • et al.
        Transcriptomic analysis of autistic brain reveals convergent molecular pathology.
        Nature. 2011; 474: 380-384
        • Cayre M.
        • Canoll P.
        • Goldman J.E.
        Cell migration in the normal and pathological postnatal mammalian brain.
        Prog Neurobiol. 2009; 88: 41-63
        • Streuli C.H.
        • Akhtar N.
        Signal co-operation between integrins and other receptor systems.
        Biochem J. 2009; 418: 491-506
        • Levi-Montalcini R.
        • Skaper S.D.
        • Dal Toso R.
        • Petrelli L.
        • Leon A.
        Nerve growth factor: from neurotrophin to neurokine.
        Trends Neurosci. 1996; 19: 514-520
        • Lee K.M.
        • Hwang S.K.
        • Lee J.A.
        Neuronal autophagy and neurodevelopmental disorders.
        Exp Neurobiol. 2013; 22: 133-142
        • Corbett B.A.
        • Kantor A.B.
        • Schulman H.
        • Walker W.L.
        • Lit L.
        • Ashwood P.
        • et al.
        A proteomic study of serum from children with autism showing differential expression of apolipoproteins and complement proteins.
        Mol Psychiatry. 2006; 12: 292-306
        • Hu V.
        • Sarachana T.
        • Kim K.
        • Nguyen A.
        • Kulkarni S.
        • Steinberg M.H.
        • et al.
        Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: evidence for circadian rhythm dysfunction in severe autism.
        Autism Res. 2009; 2: 78-97
        • Kong S.W.
        • Collins C.D.
        • Shimizu-Motohashi Y.
        • Holm I.A.
        • Campbell M.G.
        • Lee I.-H.
        • et al.
        Characteristics and predictive value of blood transcriptome signature in males with autism spectrum disorders.
        PLoS One. 2012; 7: e49475
        • Pinto D.
        • Pagnamenta A.T.
        • Klei L.
        • Anney R.
        • Merico D.
        • Regan R.
        • et al.
        Functional impact of global rare copy number variation in autism spectrum disorders.
        Nature. 2010; 466: 368-372
        • Greenberg S.A.
        How citation distortions create unfounded authority: analysis of a citation network.
        BMJ (Clinical research ed). 2009; 339: b2680
        • Comi A.M.
        • Zimmerman A.W.
        • Frye V.H.
        • Law P.A.
        • Peeden J.N.
        Familial clustering of autoimmune disorders and evaluation of medical risk factors in autism.
        J Child Neurol. 1999; 14: 388-394
        • Warren R.P.
        • Odell J.D.
        • Warren W.L.
        • Burger R.A.
        • Maciulis A.
        • Daniels W.W.
        • et al.
        Strong association of the third hypervariable region of HLA-DR beta 1 with autism.
        J Neuroimmunol. 1996; 67: 97-102
        • Johnson W.G.
        • Buyske S.
        • Mars A.E.
        • Sreenath M.
        • Stenroos E.S.
        • Williams T.A.
        • et al.
        HLA-DR4 as a risk allele for autism acting in mothers of probands possibly during pregnancy.
        Arch Pediatr Adolesc Med. 2009; 163: 542-546
        • Hall D.
        • Huerta M.F.
        • McAuliffe M.J.
        • Farber G.K.
        Sharing heterogeneous data: the national database for autism research.
        Neuroinformatics. 2012; 10: 331-339
        • Sullivan P.F.
        The psychiatric GWAS consortium: big science comes to psychiatry.
        Neuron. 2010; 68: 182-186
        • Smoller J.W.
        • Craddock N.
        • Kendler K.
        • Lee P.H.
        • Neale B.M.
        • et al.
        • Cross-Disorder Group of the Psychiatric Genomics C
        Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.
        Lancet. 2013; 381: 1371-1379
        • Craddock N.
        • Kendler K.
        • Neale M.
        • Nurnberger J.
        • Purcell S.
        • et al.
        • Cross-Disorder Phenotype Group of the Psychiatric GC
        Dissecting the phenotype in genome-wide association studies of psychiatric illness.
        Br J Psychiatry. 2009; 195: 97-99
        • Lee S.H.
        • Ripke S.
        • Neale B.M.
        • Faraone S.V.
        • Purcell S.M.
        • et al.
        • Cross-Disorder Group of the Psychiatric Genomics C
        Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.
        Nat Genet. 2013; 45: 984-994
        • Smoller J.W.
        Disorders and borders: psychiatric genetics and nosology.
        Am J Med Genet B Neuropsychiatr Genet. 2013; 162B: 559-578
        • Dazert E.
        • Hall M.N.
        mTOR signaling in disease.
        Curr Opin Cell Biol. 2011; 23: 744-755
        • Pardo C.A.
        • Eberhart C.G.
        The neurobiology of autism.
        Brain Pathol. 2007; 17: 434-447
        • Voineagu I.
        • Eapen V.
        Converging pathways in autism spectrum disorders: interplay between synaptic dysfunction and immune responses.
        Front Hum Neurosci. 2013; 7: 738
        • Herbert M.R.
        • Russo J.P.
        • Yang S.
        • Roohi J.
        • Blaxill M.
        • Kahler S.G.
        • et al.
        Autism and environmental genomics.
        Neurotoxicology. 2006; 27: 671-684
        • Patel C.J.
        • Chen R.
        • Kodama K.
        • Ioannidis J.P.
        • Butte A.J.
        Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus.
        Hum Genet. 2013; 132: 495-508
        • Patel C.J.
        • Bhattacharya J.
        • Butte A.J.
        An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus.
        PLoS One. 2010; 5: e10746
        • Wolf G.
        The Data Driven Life.
        The New York Times. 2010; (Sunday Magazine: MM38.)