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Neural correlates of polygenic risk score for autism spectrum disorders in general population

Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder wit...

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Autores principales: Khundrakpam, Budhachandra, Vainik, Uku, Gong, Jinnan, Al-Sharif, Noor, Bhutani, Neha, Kiar, Gregory, Zeighami, Yashar, Kirschner, Matthias, Luo, Cheng, Dagher, Alain, Evans, Alan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475696/
https://www.ncbi.nlm.nih.gov/pubmed/32954337
http://dx.doi.org/10.1093/braincomms/fcaa092
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author Khundrakpam, Budhachandra
Vainik, Uku
Gong, Jinnan
Al-Sharif, Noor
Bhutani, Neha
Kiar, Gregory
Zeighami, Yashar
Kirschner, Matthias
Luo, Cheng
Dagher, Alain
Evans, Alan
author_facet Khundrakpam, Budhachandra
Vainik, Uku
Gong, Jinnan
Al-Sharif, Noor
Bhutani, Neha
Kiar, Gregory
Zeighami, Yashar
Kirschner, Matthias
Luo, Cheng
Dagher, Alain
Evans, Alan
author_sort Khundrakpam, Budhachandra
collection PubMed
description Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age(2) on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.
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spelling pubmed-74756962020-09-17 Neural correlates of polygenic risk score for autism spectrum disorders in general population Khundrakpam, Budhachandra Vainik, Uku Gong, Jinnan Al-Sharif, Noor Bhutani, Neha Kiar, Gregory Zeighami, Yashar Kirschner, Matthias Luo, Cheng Dagher, Alain Evans, Alan Brain Commun Original Article Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age(2) on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging. Oxford University Press 2020-07-08 /pmc/articles/PMC7475696/ /pubmed/32954337 http://dx.doi.org/10.1093/braincomms/fcaa092 Text en © The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Khundrakpam, Budhachandra
Vainik, Uku
Gong, Jinnan
Al-Sharif, Noor
Bhutani, Neha
Kiar, Gregory
Zeighami, Yashar
Kirschner, Matthias
Luo, Cheng
Dagher, Alain
Evans, Alan
Neural correlates of polygenic risk score for autism spectrum disorders in general population
title Neural correlates of polygenic risk score for autism spectrum disorders in general population
title_full Neural correlates of polygenic risk score for autism spectrum disorders in general population
title_fullStr Neural correlates of polygenic risk score for autism spectrum disorders in general population
title_full_unstemmed Neural correlates of polygenic risk score for autism spectrum disorders in general population
title_short Neural correlates of polygenic risk score for autism spectrum disorders in general population
title_sort neural correlates of polygenic risk score for autism spectrum disorders in general population
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475696/
https://www.ncbi.nlm.nih.gov/pubmed/32954337
http://dx.doi.org/10.1093/braincomms/fcaa092
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