Deletion Impact on General Intelligence Is Similar in Autistic Than in Other Neurodevelopmental Disorders and General Population.

Poster Presentation
Friday, May 3, 2019: 11:30 AM-1:30 PM
Room: 710 (Palais des congres de Montreal)
G. Huguet1, C. Schramm2, E. A. Douard1, A. Main3, M. Jean Louis1, M. Auger1, G. Schumann4, T. Bourgeron5, Z. Pausova6, S. Karama7, A. Labbe8, T. Paus9, C. Greenwood10, I. Deary11 and S. Jacquemont12, (1)Research Center of UHC Sainte-Justine, Montreal, QC, Canada, (2)University of Montreal, Montreal, QC, Canada, (3)HEC Montreal, Montreal, QC, Canada, (4)King's College de Londres, London, United Kingdom, (5)University Denis Diderot Paris 7, Paris, France, (6)Hospital for Sick Children, Toronto, ON, Canada, (7)Douglas Mental Health University Institute, Montreal, QC, Canada, (8)Département de Sciences de la Décision, HEC Montreal, Montreal, QC, Canada, (9)Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada, (10)Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada, (11)University of Edinburgh, Edinburgh, United Kingdom, (12)CHU Sainte Justine, University of Montreal, Montreal, QC, Canada
Background: Copy number variants (CNVs) classified as pathogenic are identified in 10 to 15 % of patients referred for neurodevelopmental disorders (examples: intellectual disability, autism, …). However, their effect-sizes on cognitive traits measured as a continuum remain mostly unknown because the vast majority of them are too rare to be studied individually using association studies. In a previous study, we developed a model to predict the effect-size of any deletion on intelligence quotient (IQ). This model was based on data from two general population cohorts. We validated this model by assessing the concordance between our predictions with literature for 15 recurrent CNVs and we estimated that our model has 75% of accuracy.

Objectives: We aim to extend this model to neurodevelopmental disorders, especially autistic populations to better handle the rare and non-recurrent deletions that may explain the low IQ measured in some patients.

Methods: We called CNVs ≥50Kb from genotyping data from 5 general population cohorts (n=17,449) as well as two disease cohorts, the Simons Simplex Collection of autism (n=2,574) and a neurodevelopmental disorder family cohort from CHU Sainte Justine (n=239). General intelligence was measured using different IQ scales or general factor (g-factor) which was computed using principal components analysis of different cognitive scores. Linear and non-linear models investigated functional annotations of genes included in CNVs to identify features explaining their effect-size on IQ. Then, we meta-analysed the results obtained for each cohort. Validation was performed using intra-class correlation comparing IQ predicted by the model to empirical data.

Results: We investigated a total of > 80,000 CNVs covering > 5,000 genes. Effect-size of deletions in disease cohort, including an autistic cohort, is similar to the one we observed in general population. Among 10 functional annotations, constraint scores (in particular the probability of being intolerant to haploinsufficiency-pLI) best explain the effect of deletions on non-verbal IQ with a decrease of 0.17 points Z-score per unit of pLI (CI 95%: [-0.22;-0.13]). The same effect-size was observed for the normalized g-factor with -0.20 Z-score (CI 95%: [-0.26;-0.14]). Effect-size of CNVs was similar across all methods used to measure general intelligence and across general population disease cohorts and complex comorbid clinical cases (neurodevelopmental disorders and/or autism). The concordance between this new prediction and the decrease of general intelligence computed in previous studies on recurrent CNVs is >75%. We did not identify any interactions or non-linear genetic effects on general intelligence.

Conclusions: The effect-size of deletions on general intelligence can be reliably estimated across the genome and is independent of participants’ status. Results suggest omnigenic effects of haploinsufficiency and overexpression on general intelligence. This represents a framework to study variants too rare to perform individual association studies and we provide a new online tool for clinicians to estimate the contribution of undocumented CNVs to patient’s cognitive deficits in the neurodevelopmental clinic. We hope that the characterization of rare variants will help to better understand the heterogeneity of autism disorder.

See more of: Statistical Genetics
See more of: Statistical Genetics