Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
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
_version_ 1783559241283403776
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
work_keys_str_mv AT vandermeerdennis understandingthegeneticdeterminantsofthebrainwithmostest
AT freioleksandr understandingthegeneticdeterminantsofthebrainwithmostest
AT kaufmanntobias understandingthegeneticdeterminantsofthebrainwithmostest
AT shadrinalexeya understandingthegeneticdeterminantsofthebrainwithmostest
AT devoranna understandingthegeneticdeterminantsofthebrainwithmostest
AT smelandolavb understandingthegeneticdeterminantsofthebrainwithmostest
AT thompsonwesleyk understandingthegeneticdeterminantsofthebrainwithmostest
AT fanchunchieh understandingthegeneticdeterminantsofthebrainwithmostest
AT hollanddominic understandingthegeneticdeterminantsofthebrainwithmostest
AT westlyelarst understandingthegeneticdeterminantsofthebrainwithmostest
AT andreassenolea understandingthegeneticdeterminantsofthebrainwithmostest
AT daleandersm understandingthegeneticdeterminantsofthebrainwithmostest