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Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models.

Authors
  • Jiang, Lai1, 2, 3
  • Huguet, Guillaume3, 4
  • Schramm, Catherine1, 3, 4
  • Ciampi, Antonio2
  • Main, Antoine3, 4, 5
  • Passo, Claudine3, 4
  • Jean-Louis, Martineau3, 4
  • Auger, Maude3, 4
  • Schumann, Gunter6
  • Porteous, David7, 8, 9
  • Jacquemont, Sébastien3, 4
  • Greenwood, Celia M T1, 2, 10, 11
  • 1 Lady Davis Institute, Jewish General Hospital, Montreal, Canada. , (Canada)
  • 2 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. , (Canada)
  • 3 Centre Hospitalier Universitaire (CHU) Sainte-Justine, Montreal, Canada. , (Canada)
  • 4 Universite de Montreal, Montreal, Canada. , (Canada)
  • 5 Department of Decision Sciences, Hautes etudes commerciales de Montreal (HEC), Montreal, Canada. , (Canada)
  • 6 Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
  • 7 Department of Psychology, Lothian Birth Cohorts Group, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, UK.
  • 8 Medical Genetics Section, Centre for Genomic Experimental Medicine, MRC Institute of Genetics Molecular Medicine, Western General Hospital, The University of Edinburgh, Edinburgh, UK.
  • 9 Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK.
  • 10 Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada. , (Canada)
  • 11 Department of Human Genetics, McGill University, Montreal, Canada. , (Canada)
Type
Published Article
Journal
Genetic Epidemiology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Nov 01, 2020
Volume
44
Issue
8
Pages
825–840
Identifiers
DOI: 10.1002/gepi.22344
PMID: 32783248
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

It is challenging to estimate the phenotypic impact of the structural genome changes known as copy-number variations (CNVs), since there are many unique CNVs which are nonrecurrent, and most are too rare to be studied individually. In recent work, we found that CNV-aggregated genomic annotations, that is, specifically the intolerance to mutation as measured by the pLI score (probability of being loss-of-function intolerant), can be strong predictors of intellectual quotient (IQ) loss. However, this aggregation method only estimates the individual CNV effects indirectly. Here, we propose the use of hierarchical Bayesian models to directly estimate individual effects of rare CNVs on measures of intelligence. Annotation information on the impact of major mutations in genomic regions is extracted from genomic databases and used to define prior information for the approach we call HBIQ. We applied HBIQ to the analysis of CNV deletions and duplications from three datasets and identified several genomic regions containing CNVs demonstrating significant deleterious effects on IQ, some of which validate previously known associations. We also show that several CNVs were identified as deleterious by HBIQ even if they have a zero pLI score, and the converse is also true. Furthermore, we show that our new model yields higher out-of-sample concordance (78%) for predicting the consequences of carrying known recurrent CNVs compared with our previous approach. © 2020 Wiley Periodicals LLC.

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