A framework for the investigation of rare genetic disorders in neuropsychiatry


De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

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Fig. 1: Overview of rare genetic disorders (RGDs).

Debbie Maizels/Springer Nature.

Fig. 2: Cross-domain impact of RGDs and limitations of current evidence.

Debbie Maizels/Springer Nature.

Fig. 3: Thresholds for genome-wide significant association.

Debbie Maizels/Springer Nature.

Fig. 4: Functional assays across disorders and models.

Debbie Maizels/Springer Nature.


  1. 1.

    US Food and Drug Administration. Orphan Drug Act. (1983).

  2. 2.

    Loane, M. et al. Twenty-year trends in the prevalence of Down syndrome and other trisomies in Europe: impact of maternal age and prenatal screening. Eur. J. Hum. Genet. 21, 27–33 (2013).

    PubMed  Google Scholar 

  3. 3.

    McKusick-Nathans Institute of Genetic Medicine. Online Mendelian Inheritance in Man, OMIM® (Johns Hopkins University, Baltimore, MD, USA) https://omim.org/ (accessed 28 April 2018).

  4. 4.

    McRae, J. F. et al. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438 (2017).

    CAS  Google Scholar 

  5. 5.

    Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Catterall, W. A., Kalume, F. & Oakley, J. C. NaV1.1 channels and epilepsy. J. Physiol. 588, 1849–1859 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Escayg, A. et al. Mutations of SCN1A, encoding a neuronal sodium channel, in two families with GEFS+2. Nat. Genet. 24, 343–345 (2000).

    CAS  PubMed  Google Scholar 

  8. 8.

    Marshall, C. R. et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49, 27–35 (2017).

    CAS  PubMed  Google Scholar 

  9. 9.

    Lukowski, A. F., Milojevich, H. M. & Eales, L. Cognitive functioning in children with down syndrome: current knowledge and future directions. Adv. Child Dev. Behav. 56, 257–289 (2019).

    PubMed  Google Scholar 

  10. 10.

    Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Ben-Shalom, R. et al. Opposing effects on NaV1.2 function underlie differences between SCN2A variants observed in individuals with autism spectrum disorder or infantile seizures. Biol. Psychiatry 82, 224–232 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Power, R. A. et al. Fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse vs their unaffected siblings. JAMA Psychiatry 70, 22–30 (2013).

    PubMed  Google Scholar 

  13. 13.

    Stefansson, H. et al. CNVs conferring risk of autism or schizophrenia affect cognition in controls. Nature 505, 361–366 (2014).

    CAS  PubMed  Google Scholar 

  14. 14.

    Sanders, S. J. et al. Whole genome sequencing in psychiatric disorders: the WGSPD consortium. Nat. Neurosci. 20, 1661–1668 (2017).

    CAS  PubMed  Google Scholar 

  15. 15.

    Reuter, M. S. et al. Diagnostic yield and novel candidate genes by exome sequencing in 152 consanguineous families with neurodevelopmental disorders. JAMA Psychiatry 74, 293–299 (2017).

    PubMed  Google Scholar 

  16. 16.

    Schaefer, G. B. et al. Array comparative genomic hybridization findings in a cohort referred for an autism evaluation. J. Child Neurol. 25, 1498–1503 (2010).

    PubMed  Google Scholar 

  17. 17.

    Retterer, K. et al. Clinical application of whole-exome sequencing across clinical indications. Genet. Med. 18, 696–704 (2016).

    CAS  PubMed  Google Scholar 

  18. 18.

    Sawyer, S. L. et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin. Genet. 89, 275–284 (2016).

    CAS  PubMed  Google Scholar 

  19. 19.

    Tammimies, K. et al. Molecular diagnostic yield of chromosomal microarray analysis and whole-exome sequencing in children with autism spectrum disorder. JAMA 314, 895–903 (2015).

    CAS  PubMed  Google Scholar 

  20. 20.

    Kosmicki, J. A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504–510 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Weiner, D. J. et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49, 978–985 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    EuroEPINOMICS-RES Consortium. De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am. J. Hum. Genet. 95, 360–370 (2014).

    Google Scholar 

  23. 23.

    Heyne, H. O. et al. De novo variants in neurodevelopmental disorders with epilepsy. Nat. Genet. 50, 1048–1053 (2018).

    CAS  PubMed  Google Scholar 

  24. 24.

    Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Singh, T. et al. The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability. Nat. Genet. 49, 1167–1173 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Willsey, A. J. et al. De Novo coding variants are strongly associated with Tourette disorder. Neuron 94, 486–499.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Finkel, R. S. et al. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N. Engl. J. Med. 377, 1723–1732 (2017).

    CAS  PubMed  Google Scholar 

  30. 30.

    Mendell, J. R. et al. Single-dose gene-replacement therapy for spinal muscular atrophy. N. Engl. J. Med. 377, 1713–1722 (2017).

    CAS  PubMed  Google Scholar 

  31. 31.

    Schneider, M. et al. Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome. Am. J. Psychiatry 171, 627–639 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Rees, E. et al. Evidence that duplications of 22q11.2 protect against schizophrenia. Mol. Psychiatry 19, 37–40 (2014).

    CAS  PubMed  Google Scholar 

  33. 33.

    Kendall, K. M. et al. Archival report cognitive performance among carriers of pathogenic copy number variants: analysis of 152,000 UK Biobank subjects. Biol. Psychiatry 83, 103–110 (2016).

    Google Scholar 

  34. 34.

    D’Angelo, D. et al. Defining the effect of the 16p11.2 duplication on cognition, behavior, and medical comorbidities. JAMA Psychiatry 73, 20–30 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Niemi, M. E. K. et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562, 268–271 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Moreno-De-Luca, A. et al. The role of parental cognitive, behavioral, and motor profiles in clinical variability in individuals with chromosome 16p11.2 deletions. JAMA Psychiatry 72, 119–126 (2015).

    PubMed  Google Scholar 

  37. 37.

    Insel, T. R. The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. Am. J. Psychiatry 171, 395–397 (2014).

    PubMed  Google Scholar 

  38. 38.

    Cuthbert, B. N. & Insel, T. R. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 11, 126 (2013).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Cuthbert, B. N. Research Domain Criteria: toward future psychiatric nosologies. Dialog-. Clin. Neurosci. 17, 89–97 (2015).

    Google Scholar 

  40. 40.

    Constantino, J. N. et al. Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J. Autism Dev. Disord. 33, 427–433 (2003).

    PubMed  Google Scholar 

  41. 41.

    van Os, J. & Reininghaus, U. Psychosis as a transdiagnostic and extended phenotype in the general population. World Psychiatry 15, 118–124 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Olsen, L. et al. Prevalence of rearrangements in the 22q11.2 region and population-based risk of neuropsychiatric and developmental disorders in a Danish population: a case-cohort study. Lancet Psychiatry 5, 573–580 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Männik, K. et al. Copy number variations and cognitive phenotypes in unselected populations. JAMA. 313, 2044–2054 (2015).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Simons Vip, C., Spiro, J. E. & Chung, W. K. Simons Variation in Individuals Project (Simons VIP): a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Neuron 73, 1063–1067 (2012).

    Google Scholar 

  45. 45.

    Stessman, H. A., Bernier, R. & Eichler, E. E. A genotype-first approach to defining the subtypes of a complex disease. Cell 156, 872–877 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    He, X. et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 9, e1003671 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Singh, T. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci. 19, 571–577 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Ahn, K. et al. High rate of disease-related copy number variations in childhood onset schizophrenia. Mol. Psychiatry 19, 568–572 (2014).

    CAS  PubMed  Google Scholar 

  52. 52.

    Satterstrom, F.K. et al. Novel genes for autism implicate both excitatory and inhibitory cell lineages in risk. Preprint at https://www.biorxiv.org/content/10.1101/484113v3 (2018).

  53. 53.

    Mighell, T. L., Evans-Dutson, S. & O’Roak, B. J. A saturation mutagenesis approach to understanding PTEN lipid phosphatase activity and genotype-phenotypes relationships. Am. J. Hum. Genet. 102, 943–955 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Huguet, G. et al. Measuring and estimating the effect sizes of copy number variants on general intelligence in community-based samples. JAMA Psychiatry 75, 447–457 (2018).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Geschwind, D. H. Autism: many genes, common pathways? Cell 135, 391–395 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Cheng, H. et al. Phenotypic and biochemical analysis of an international cohort of individuals with variants in NAA10 and NAA15. Hum. Mol. Genet. 28, 2900–2919 (2019).

    PubMed  Google Scholar 

  57. 57.

    Chakravarti, A., Clark, A. G. & Mootha, V. K. Distilling pathophysiology from complex disease genetics. Cell 155, 21–26 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N. & Geschwind, D. H. The road to precision psychiatry: translating genetics into disease mechanisms. Nat. Neurosci. 19, 1397–1407 (2016).

    CAS  PubMed  Google Scholar 

  59. 59.

    Parikshak, N. N., Gandal, M. J. & Geschwind, D. H. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16, 441–458 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Amin, N. D. & Paşca, S. P. Building models of brain disorders with three-dimensional organoids. Neuron 100, 389–405 (2018).

    CAS  PubMed  Google Scholar 

  61. 61.

    Sun, Y. et al. A deleterious Nav1.1 mutation selectively impairs telencephalic inhibitory neurons derived from Dravet Syndrome patients. eLife 5, e13073 (2016).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Willsey, A. J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

    CAS  PubMed  Google Scholar 

  65. 65.

    Cargnin, F. et al. FOXG1 orchestrates neocortical organization and cortico-cortical connections. Neuron 100, 1083–1096.e5 (2018).

    CAS  PubMed  Google Scholar 

  66. 66.

    Guloksuz, S., Pries, L. K. & van Os, J. Application of network methods for understanding mental disorders: pitfalls and promise. Psychol. Med. 47, 2743–2752 (2017).

    CAS  PubMed  Google Scholar 

  67. 67.

    Sheffield, J. M. et al. Transdiagnostic associations between functional brain network integrity and cognition. JAMA Psychiatry 74, 605–613 (2017).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Cao, H. et al. Toward leveraging human connectomic data in large consortia. Generalizability of fMRI-based brain graphs across sites, sessions, and paradigms. Cereb. Cortex (2018).

  69. 69.

    Anticevic, A. et al. Association of Thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry 72, 882–891 (2015).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Bruno, J. L. et al. Longitudinal identification of clinically distinct neurophenotypes in young children with fragile X syndrome. Proc. Natl. Acad. Sci. USA 114, 10767–10772 (2017).

    CAS  PubMed  Google Scholar 

  71. 71.

    Hazlett, H. C. et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 542, 348–351 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Bearden, C. E. & Thompson, P. M. Emerging global initiatives in neurogenetics: the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium. Neuron 94, 232–236 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Thompson, P. M. et al. ENIGMA and the individual: predicting factors that affect the brain in 35 countries worldwide. Neuroimage 145 Pt B, 389–408 (2017).

    Google Scholar 

  74. 74.

    Thompson, P. M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    National Advisory Mental Health Council Workgroup on Genomics. Opportunities and Challenges of Psychiatric Genetics (NAHMC, 2018).

  76. 76.

    Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Deisseroth, K., Etkin, A. & Malenka, R. C. Optogenetics and the circuit dynamics of psychiatric disease. J. Am. Med. Assoc. 313, 2019–2020 (2015).

    Google Scholar 

  78. 78.

    Stoodley, C. J. et al. Author Correction: Altered cerebellar connectivity in autism and cerebellar-mediated rescue of autism-related behaviors in mice. Nat. Neurosci. 21, 1016 (2018).

    CAS  PubMed  Google Scholar 

  79. 79.

    Anthony, T. E. et al. Control of stress-induced persistent anxiety by an extra-amygdala septohypothalamic circuit. Cell 156, 522–536 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Stewart, A. M. & Kalueff, A. V. Developing better and more valid animal models of brain disorders. Behav. Brain Res. 276, 28–31 (2015).

    PubMed  Google Scholar 

  81. 81.

    LeBlanc, J. J. et al. Visual evoked potentials detect cortical processing deficits in Rett syndrome. Ann. Neurol. 78, 775–786 (2015).

    CAS  PubMed  Google Scholar 

  82. 82.

    Lovelace, J. W., Ethell, I. M., Binder, D. K. & Razak, K. A. Translation-relevant EEG phenotypes in a mouse model of Fragile X Syndrome. Neurobiol. Dis. 115, 39–48 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Chadman, K. K., Yang, M. & Crawley, J. N. Criteria for validating mouse models of psychiatric diseases. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 150B, 1–11 (2009).

    PubMed  PubMed Central  Google Scholar 

  84. 84.

    Galvão-Coelho, N. L., Galvão, A. C. M., da Silva, F. S. & de Sousa, M. B. C. Common marmosets: a potential translational animal model of juvenile depression. Front. Psychiatry 8, 175 (2017).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Oikonomidis, L. et al. A dimensional approach to modeling symptoms of neuropsychiatric disorders in the marmoset monkey. Dev. Neurobiol. 77, 328–353 (2017).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Mao, P., Cui, D., Zhao, X.-D. & Ma, Y.-Y. Prefrontal dysfunction and a monkey model of schizophrenia. Neurosci. Bull. 31, 235–241 (2015).

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Kotani, M. et al. The atypical antipsychotic blonanserin reverses (+)-PD-128907- and ketamine-induced deficit in executive function in common marmosets. Behav. Brain Res. 305, 212–217 (2016).

    CAS  PubMed  Google Scholar 

  88. 88.

    Clarke, H. F. et al. Orbitofrontal dopamine depletion upregulates caudate dopamine and alters behavior via changes in reinforcement sensitivity. J. Neurosci. 34, 7663–7676 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Zhou, Y. et al. Atypical behaviour and connectivity in SHANK3-mutant macaques. Nature 570, 326–331 (2019).

    CAS  PubMed  Google Scholar 

  90. 90.

    Pașca, S. P. The rise of three-dimensional human brain cultures. Nature 553, 437–445 (2018).

    PubMed  Google Scholar 

  91. 91.

    Bredenoord, A. L., Clevers, H. & Knoblich, J. A. Human tissues in a dish: The research and ethical implications of organoid technology. Science 355, eaaf9414 (2017).

    PubMed  Google Scholar 

  92. 92.

    Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Wang, J. et al. A resting EEG study of neocortical hyperexcitability and altered functional connectivity in fragile X syndrome. J. Neurodev. Disord. 9, 11 (2017).

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Sahin, M. et al. Discovering translational biomarkers in neurodevelopmental disorders. Nat. Rev. Drug Discov. 18, 235–236 (2018).

    Google Scholar 

  95. 95.

    Donaldson, Z. R. & Hen, R. From psychiatric disorders to animal models: a bidirectional and dimensional approach. Biol. Psychiatry 77, 15–21 (2015).

    PubMed  Google Scholar 

  96. 96.

    Spencer, C. M. et al. Modifying behavioral phenotypes in Fmr1KO mice: genetic background differences reveal autistic-like responses. Autism Res. 4, 40–56 (2011).

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Aylor, D. L. et al. Genetic analysis of complex traits in the emerging Collaborative Cross. Genome Res. 21, 1213–1222 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Vockley, J. et al. Phenylalanine hydroxylase deficiency: diagnosis and management guideline. Genet. Med. 16, 188–200 (2014).

    CAS  PubMed  Google Scholar 

  99. 99.

    Berry-Kravis, E. M. et al. Drug development for neurodevelopmental disorders: lessons learned from fragile X syndrome. Nat. Rev. Drug Discov. 17, 280–299 (2018).

    CAS  PubMed  Google Scholar 

  100. 100.

    Krueger, D. A. et al. Everolimus for treatment of tuberous sclerosis complex-associated neuropsychiatric disorders. Ann. Clin. Transl. Neurol. 4, 877–887 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    O’Leary, H. M. et al. Placebo-controlled crossover assessment of mecasermin for the treatment of Rett syndrome. Ann. Clin. Transl. Neurol. 5, 323–332 (2018).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Guy, J., Gan, J., Selfridge, J., Cobb, S. & Bird, A. Reversal of neurological defects in a mouse model of Rett syndrome. Science 315, 1143–1147 (2007).

    CAS  PubMed  Google Scholar 

  103. 103.

    Henderson, C. et al. Reversal of disease-related pathologies in the fragile X mouse model by selective activation of GABAB receptors with arbaclofen. Sci. Transl. Med. 4, 152ra128 (2012).

    PubMed  Google Scholar 

  104. 104.

    Dolan, B. M. et al. Rescue of fragile X syndrome phenotypes in Fmr1 KO mice by the small-molecule PAK inhibitor FRAX486. Proc. Natl. Acad. Sci. USA 110, 5671–5676 (2013).

    CAS  PubMed  Google Scholar 

  105. 105.

    Jacquemont, S. et al. Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 478, 97–102 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Wiesel, T. N. & Hubel, D. H. Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26, 1003–1017 (1963).

    CAS  Google Scholar 

  107. 107.

    Berry-Kravis, E. AFQ056 for language learning in children with FXS. https://clinicaltrials.gov/ct2/show/NCT02920892.

  108. 108.

    Bebin, M. Preventing epilepsy using vigabatrin in infants with tuberous sclerosis complex. https://clinicaltrials.gov/ct2/show/NCT02849457.

  109. 109.

    Jozwiak, S. Long-term, prospective study evaluating clinical and molecular biomarkers of epileptogenesis in a genetic model of epilepsy—Tuberous Sclerosis Complex (EPISTOP). https://clinicaltrials.gov/ct2/show/NCT02098759.

  110. 110.

    Kothari, C. et al. Phelan-McDermid syndrome data network: integrating patient reported outcomes with clinical notes and curated genetic reports. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 177, 613–624 (2018).

    PubMed  Google Scholar 

  111. 111.

    Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417–428 (2011).

    CAS  PubMed  Google Scholar 

  112. 112.

    Berry-Kravis, E. et al. Mavoglurant in fragile X syndrome: results of two randomized, double-blind, placebo-controlled trials. Sci. Transl. Med. 8, 321ra5 (2016).

    PubMed  Google Scholar 

  113. 113.

    van der Vaart, T., Overwater, I. E., Oostenbrink, R., Moll, H. A. & Elgersma, Y. Treatment of cognitive deficits in genetic disorders: a systematic review of clinical trials of diet and drug treatments. JAMA Neurol. 72, 1052–1060 (2015).

    PubMed  Google Scholar 

  114. 114.

    Wolff, M. et al. Genetic and phenotypic heterogeneity suggest therapeutic implications in SCN2A-related disorders. Brain 140, 1316–1336 (2017).

    PubMed  Google Scholar 

  115. 115.

    Guerrini, R. & Falchi, M. Dravet syndrome and SCN1A gene mutation related-epilepsies: cognitive impairment and its determinants. Dev. Med. Child Neurol. 53 Suppl 2, 11–15 (2011).

    PubMed  Google Scholar 

  116. 116.

    Moreno-De-Luca, D., Moreno-De-Luca, A., Cubells, J. F. & Sanders, S. J. Cross-disorder comparison of four neuropsychiatric CNV loci. Curr. Genet. Med. Rep. 2, 151–161 (2014).

    Google Scholar 

  117. 117.

    Demkow, U. & Wolańczyk, T. Genetic tests in major psychiatric disorders-integrating molecular medicine with clinical psychiatry—why is it so difficult? Transl. Psychiatry 7, e1151 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    US Department of Health and Human Services. Food and Drug Administration, Center for Drug Evaluation and Research (CDER) & Center for Biologics Evaluation and Research (CBER). Rare diseases: common issues in drug development guidance for industry. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM458485.pdf (2019).

  119. 119.

    Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    PubMed  Google Scholar 

  120. 120.

    Senthil, G., Dutka, T., Bingaman, L. & Lehner, T. Genomic resources for the study of neuropsychiatric disorders. Mol. Psychiatry 22, 1659–1663 (2017).

    CAS  PubMed  Google Scholar 

  121. 121.

    Bastarache, L. et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359, 1233–1239 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Pedersen, C. B. et al. The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).

    CAS  PubMed  Google Scholar 

  123. 123.

    Rusk, N. The UK Biobank. Nat. Methods 15, 1001 (2018).

    CAS  PubMed  Google Scholar 

  124. 124.

    An, J.-Y. & Sanders, S. J. Appreciating the population-wide impact of copy number variants on cognition. Biol. Psychiatry 82, 78–80 (2017).

    PubMed  Google Scholar 

  125. 125.

    Köhler, S. et al. The Human Phenotype Ontology in 2017. Nucleic Acids Res. 45 D1, D865–D876 (2017).

    Google Scholar 

  126. 126.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127.

    Finucane, B.M. et al. 15q duplication syndrome and related disorders. in Gene Reviews (eds. Pagon, R.A. et al.) (University of Washington, Seattle) https://www.ncbi.nlm.nih.gov/books/NBK367946/ (2016).

  128. 128.

    Miller, I.O. & Sotero de Menezes, M.A. SCN1A seizure disorders. in Gene Reviews (eds. Pagon, R.A. et al.) (University of Washington, Seattle) https://www.ncbi.nlm.nih.gov/books/NBK1318/ (2007).

  129. 129.

    Kirov, G. et al. The penetrance of copy number variations for schizophrenia and developmental delay. Biol. Psychiatry 75, 378–385 (2014).

    CAS  PubMed  Google Scholar 

  130. 130.

    Moreno-De-Luca, D. et al. Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts. Mol. Psychiatry 18, 1090–1095 (2013).

    CAS  PubMed  Google Scholar 

  131. 131.

    Malhotra, D. & Sebat, J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell 148, 1223–1241 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Sanders, S. J. et al. Progress in understanding and treating SCN2A-mediated disorders. Trends Neurosci. 41, 442–456 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Joseph, L. et al. Characterization of autism spectrum disorder and neurodevelopmental profiles in youth with XYY syndrome. J. Neurodev. Disord. 10, 30 (2018).

    PubMed  PubMed Central  Google Scholar 

  134. 134.

    Lynch, M. Rate, molecular spectrum, and consequences of human mutation. Proc. Natl. Acad. Sci. USA 107, 961–968 (2010).

    CAS  PubMed  Google Scholar 

  135. 135.

    Scheldeman, C. et al. mTOR-related neuropathology in mutant tsc2 zebrafish: Phenotypic, transcriptomic and pharmacological analysis. Neurobiol. Dis. 108, 225–237 (2017).

    CAS  PubMed  Google Scholar 

  136. 136.

    Kelly, E. et al. mGluR5 modulation of behavioral and epileptic phenotypes in a mouse model of tuberous sclerosis complex. Neuropsychopharmacology 43, 1457–1465 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Shukla, G. et al. Magnetoencephalographic identification of epileptic focus in children with generalized electroencephalographic (EEG) Features but focal imaging abnormalities. J. Child Neurol. 32, 981–995 (2017).

    PubMed  Google Scholar 

  138. 138.

    Pietri, T. et al. The first mecp2-null zebrafish model shows altered motor behaviors. Front. Neural Circuits 7, 118 (2013).

    PubMed  PubMed Central  Google Scholar 

  139. 139.

    Wu, Y. et al. Characterization of Rett Syndrome-like phenotypes in Mecp2-knockout rats. J. Neurodev. Disord. 8, 23 (2016).

    PubMed  PubMed Central  Google Scholar 

  140. 140.

    Chen, Y. et al. Modeling Rett syndrome using TALEN-edited MECP2 mutant cynomolgus monkeys. Cell 169, 945–955.e10 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Pidcock, F. S. et al. Functional outcomes in Rett syndrome. Brain Dev. 38, 76–81 (2016).

    PubMed  Google Scholar 

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This paper offers a synthesis of the ideas generated at the NIMH-sponsored workshop “Rare Genetic Disease Workshop: Window into Genomic Risk and Resilience of Mental Disorders,” held in September 2017, with the goal of discussing research and clinical opportunities presented by recent discoveries of RGDs with high risk for developmental neuropsychiatric disorders. Analyses utilize data generated by the Saguenay Youth Study, OMIM (https://www.omim.org), ExAC (http://exac.broadinstitute.org/) and the DECIPHER Consortium, including the Developmental Disorders Genotype-Phenotype Database (DDG2P, https://decipher.sanger.ac.uk/info/ddg2p). A full list of centers that contributed to the generation of the DECIPHER data is available at http://decipher.sanger.ac.uk and via email from decipher@sanger.ac.uk. We also thank G. Senthil for helpful feedback on the manuscript.

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Correspondence to Raquel E. Gur or Carrie E. Bearden.

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Peer review information Hannah Stower was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Impact of RGDs on neuropsychiatric domains.

a, Many RGDs impact cognition, measured by IQ. For CNVs, the decrease in IQ (x axis) can be predicted by considering the pLI score of the genes within the CNV. CNVs that are predicted to markedly reduce IQ are more likely to be de novo (y axis), based on logistic regression (blue line) of 2,743 CNVs detected in patients with neurodevelopmental disorders and the general population (gray distributions at top and bottom). Updated analysis from ref. 54. b, In Fig. 2, we show the odds ratio for ID/NDD, ASD and SCZ across different CNV loci. Here, we show an equivalent plot for single-gene RGDs. Insufficient control data exist to estimate odds ratio, and therefore we show the percentage of cases with ID/NDD, ASD, and IE based on curated publication review applied equally across genes (https://dbd.geisingeradmi.org) with the number of cases are shown in parentheses (see Supplementary Table 2 for numbers). Abbreviations: ID, intellectual disability; NDD, neurodevelopmental delay; ASD, autism spectrum disorder; SCZ, schizophrenia; IE, infantile epilepsy; pLI, probability loss-of-function intolerant.

Extended Data Fig. 2 Thresholds for genome-wide significant association with de novo PTVs.

a, Gene mutability is a function of gene length (cDNA) and sequence context (particularly GC content). b, RGD gene discovery from exome sequencing has been driven by de novo mutations, leading to a bias towards larger genes with higher mutability. c, Thresholds of statistical association (colored lines) are estimated for a given number of de novo PTV mutations (3, 5, 10, and 20) as cohort size (x axis) and gene mutability/size (y axis) varies. P values are estimated based on the rate of de novo PTV mutations in controls4 and a Poisson distribution (see Methods for details). Abbreviations: pLI, probability of loss-of-function intolerance; ASD, autism spectrum disorder; DDD: Deciphering Developmental Disorders; GC content, guanine-cytosine content.

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Supplementary Information

Supplementary Methods, Supplementary Tables 3 and 5.

Supplementary Table

Supplementary Tables 1, 2, and 4

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Sanders, S.J., Sahin, M., Hostyk, J. et al. A framework for the investigation of rare genetic disorders in neuropsychiatry. Nat Med 25, 1477–1487 (2019). https://doi.org/10.1038/s41591-019-0581-5

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