A framework for the investigation of rare genetic disorders in neuropsychiatry

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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.


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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.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Tables 3 and 5.

Supplementary Table

Supplementary Tables 1, 2, and 4

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