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Understanding the genetic determinants of the brain with MOSTest
Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360598/ https://www.ncbi.nlm.nih.gov/pubmed/32665545 http://dx.doi.org/10.1038/s41467-020-17368-1 |
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author | van der Meer, Dennis Frei, Oleksandr Kaufmann, Tobias Shadrin, Alexey A. Devor, Anna Smeland, Olav B. Thompson, Wesley K. Fan, Chun Chieh Holland, Dominic Westlye, Lars T. Andreassen, Ole A. Dale, Anders M. |
author_facet | van der Meer, Dennis Frei, Oleksandr Kaufmann, Tobias Shadrin, Alexey A. Devor, Anna Smeland, Olav B. Thompson, Wesley K. Fan, Chun Chieh Holland, Dominic Westlye, Lars T. Andreassen, Ole A. Dale, Anders M. |
author_sort | van der Meer, Dennis |
collection | PubMed |
description | Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10(−8), MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. |
format | Online Article Text |
id | pubmed-7360598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73605982020-07-20 Understanding the genetic determinants of the brain with MOSTest van der Meer, Dennis Frei, Oleksandr Kaufmann, Tobias Shadrin, Alexey A. Devor, Anna Smeland, Olav B. Thompson, Wesley K. Fan, Chun Chieh Holland, Dominic Westlye, Lars T. Andreassen, Ole A. Dale, Anders M. Nat Commun Article Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10(−8), MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. Nature Publishing Group UK 2020-07-14 /pmc/articles/PMC7360598/ /pubmed/32665545 http://dx.doi.org/10.1038/s41467-020-17368-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article van der Meer, Dennis Frei, Oleksandr Kaufmann, Tobias Shadrin, Alexey A. Devor, Anna Smeland, Olav B. Thompson, Wesley K. Fan, Chun Chieh Holland, Dominic Westlye, Lars T. Andreassen, Ole A. Dale, Anders M. Understanding the genetic determinants of the brain with MOSTest |
title | Understanding the genetic determinants of the brain with MOSTest |
title_full | Understanding the genetic determinants of the brain with MOSTest |
title_fullStr | Understanding the genetic determinants of the brain with MOSTest |
title_full_unstemmed | Understanding the genetic determinants of the brain with MOSTest |
title_short | Understanding the genetic determinants of the brain with MOSTest |
title_sort | understanding the genetic determinants of the brain with mostest |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360598/ https://www.ncbi.nlm.nih.gov/pubmed/32665545 http://dx.doi.org/10.1038/s41467-020-17368-1 |
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