Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models.
copy-number variation
hierarchical Bayesian model
informative prior distributions
intelligence
rare variant analysis
Journal
Genetic epidemiology
ISSN: 1098-2272
Titre abrégé: Genet Epidemiol
Pays: United States
ID NLM: 8411723
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
05
12
2019
revised:
24
06
2020
accepted:
21
07
2020
pubmed:
13
8
2020
medline:
26
5
2021
entrez:
13
8
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
825-840Subventions
Organisme : CIHR
ID : PJT‐148620
Pays : Canada
Organisme : CIHR
ID : 159734
Pays : Canada
Organisme : Chief Scientist Office
ID : CZD/16/6
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 104036/Z/14/Z
Pays : United Kingdom
Informations de copyright
© 2020 Wiley Periodicals LLC.
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