Le numéro de janvier 2021 de l’American Journal of Psychiatry est consacré à la génétique et aux mécanismes sous-jacents dans le trouble obsessionnel compulsif et le TSA.

1. Kalin NH. Genes, Cells, and Neural Circuits Relevant to OCD and Autism Spectrum Disorder. Am J Psychiatry ;2021 (Jan 1) ;178(1):1-4.

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2. Binder EB. Genotype-Phenotype Predictions in Autism : Are We There Yet ?. Am J Psychiatry ;2021 (Jan 1) ;178(1):11-12.

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3. Manoli DS, State MW. Autism Spectrum Disorder Genetics and the Search for Pathological Mechanisms. Am J Psychiatry ;2021 (Jan 1) ;178(1):30-38.

Recent progress in the identification of genes and genomic regions contributing to autism spectrum disorder (ASD) has had a broad impact on our understanding of the nature of genetic risk for a range of psychiatric disorders, on our understanding of ASD biology, and on defining the key challenges now facing the field in efforts to translate gene discovery into an actionable understanding of pathology. While these advances have not yet had a transformative impact on clinical practice, there is nonetheless cause for real optimism : reliable lists of risk genes are large and growing rapidly ; the identified encoded proteins have already begun to point to a relatively small number of areas of biology, where parallel advances in neuroscience and functional genomics are yielding profound insights ; there is strong evidence pointing to mid-fetal prefrontal cortical development as one nexus of vulnerability for some of the largest-effect ASD risk genes ; and there are multiple plausible paths forward toward rational therapeutics development that, while admittedly challenging, constitute fundamental departures from what was possible prior to the era of successful gene discovery.

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4. Zhan Y, Wei J, Liang J, Xu X, He R, Robbins TW, Wang Z. Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model. Am J Psychiatry ;2021 (Jan 1) ;178(1):65-76.

OBJECTIVE : Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. METHODS : Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD : Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112 ; ABIDE-II, N=1,114 ; ADHD-200 sample : N=776 ; OCD local institutional database : N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. RESULTS : Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. CONCLUSIONS : The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

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5. Chawner S, Doherty JL, Anney RJL, Antshel KM, Bearden CE, Bernier R, Chung WK, Clements CC, Curran SR, Cuturilo G, Fiksinski AM, Gallagher L, Goin-Kochel RP, Gur RE, Hanson E, Jacquemont S, Kates WR, Kushan L, Maillard AM, McDonald-McGinn DM, Mihaljevic M, Miller JS, Moss H, Pejovic-Milovancevic M, Schultz RT, Green-Snyder L, Vorstman JA, Wenger TL, Hall J, Owen MJ, van den Bree MBM. A Genetics-First Approach to Dissecting the Heterogeneity of Autism : Phenotypic Comparison of Autism Risk Copy Number Variants. Am J Psychiatry ;2021 (Jan 1) ;178(1):77-86.

OBJECTIVE : Certain copy number variants (CNVs) greatly increase the risk of autism. The authors conducted a genetics-first study to investigate whether heterogeneity in the clinical presentation of autism is underpinned by specific genotype-phenotype relationships. METHODS : This international study included 547 individuals (mean age, 12.3 years [SD=4.2], 54% male) who were ascertained on the basis of having a genetic diagnosis of a rare CNV associated with high risk of autism (82 16p11.2 deletion carriers, 50 16p11.2 duplication carriers, 370 22q11.2 deletion carriers, and 45 22q11.2 duplication carriers), as well as 2,027 individuals (mean age, 9.1 years [SD=4.9], 86% male) with autism of heterogeneous etiology. Assessments included the Autism Diagnostic Interview-Revised and IQ testing. RESULTS : The four genetic variant groups differed in autism symptom severity, autism subdomain profile, and IQ profile. However, substantial variability was observed in phenotypic outcome in individual genetic variant groups (74%-97% of the variance, depending on the trait), whereas variability between groups was low (1%-21%, depending on the trait). CNV carriers who met autism criteria were compared with individuals with heterogeneous autism, and a range of profile differences were identified. When clinical cutoff scores were applied, 54% of individuals with one of the four CNVs who did not meet full autism diagnostic criteria had elevated levels of autistic traits. CONCLUSIONS : Many CNV carriers do not meet full diagnostic criteria for autism but nevertheless meet clinical cutoffs for autistic traits. Although profile differences between variants were observed, there is considerable variability in clinical symptoms in the same variant.

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6. Douard E, Zeribi A, Schramm C, Tamer P, Loum MA, Nowak S, Saci Z, Lord MP, Rodríguez-Herreros B, Jean-Louis M, Moreau C, Loth E, Schumann G, Pausova Z, Elsabbagh M, Almasy L, Glahn DC, Bourgeron T, Labbe A, Paus T, Mottron L, Greenwood CMT, Huguet G, Jacquemont S. Effect Sizes of Deletions and Duplications on Autism Risk Across the Genome. Am J Psychiatry ;2021 (Jan 1) ;178(1):87-98.

OBJECTIVE : Deleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, levels of autism risk conferred by most rare CNVs remain unknown. The authors recently developed statistical models to estimate the effect size on IQ of all CNVs, including undocumented ones. In this study, the authors extended this model to autism susceptibility. METHODS : The authors identified CNVs in two autism populations (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models were used to test nine quantitative variables associated with genes encompassed in CNVs to explain their effects on IQ, autism susceptibility, and behavioral domains. RESULTS : The « probability of being loss-of-function intolerant » (pLI) best explains the effect of CNVs on IQ and autism risk. Deleting 1 point of pLI decreases IQ by 2.6 points in autism and unselected populations. The effect of duplications on IQ is threefold smaller. Autism susceptibility increases when deleting or duplicating any point of pLI. This is true for individuals with high or low IQ and after removing de novo and known recurrent neuropsychiatric CNVs. When CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behavior, and phonological memory, whereas both equally affect motor skills. CONCLUSIONS : Autism risk conferred by duplications is less influenced by IQ compared with deletions. The model applied in this study, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk and IQ loss. These models will help to interpret CNVs identified in the clinic.

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