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Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits
Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-t...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128893/ https://www.ncbi.nlm.nih.gov/pubmed/34001886 http://dx.doi.org/10.1038/s41467-021-23130-y |
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author | Zhao, Bingxin Shan, Yue Yang, Yue Yu, Zhaolong Li, Tengfei Wang, Xifeng Luo, Tianyou Zhu, Ziliang Sullivan, Patrick Zhao, Hongyu Li, Yun Zhu, Hongtu |
author_facet | Zhao, Bingxin Shan, Yue Yang, Yue Yu, Zhaolong Li, Tengfei Wang, Xifeng Luo, Tianyou Zhu, Ziliang Sullivan, Patrick Zhao, Hongyu Li, Yun Zhu, Hongtu |
author_sort | Zhao, Bingxin |
collection | PubMed |
description | Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10(−8). The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10(−31)) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction. |
format | Online Article Text |
id | pubmed-8128893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81288932021-06-01 Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits Zhao, Bingxin Shan, Yue Yang, Yue Yu, Zhaolong Li, Tengfei Wang, Xifeng Luo, Tianyou Zhu, Ziliang Sullivan, Patrick Zhao, Hongyu Li, Yun Zhu, Hongtu Nat Commun Article Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10(−8). The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10(−31)) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction. Nature Publishing Group UK 2021-05-17 /pmc/articles/PMC8128893/ /pubmed/34001886 http://dx.doi.org/10.1038/s41467-021-23130-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhao, Bingxin Shan, Yue Yang, Yue Yu, Zhaolong Li, Tengfei Wang, Xifeng Luo, Tianyou Zhu, Ziliang Sullivan, Patrick Zhao, Hongyu Li, Yun Zhu, Hongtu Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
title | Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
title_full | Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
title_fullStr | Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
title_full_unstemmed | Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
title_short | Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
title_sort | transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128893/ https://www.ncbi.nlm.nih.gov/pubmed/34001886 http://dx.doi.org/10.1038/s41467-021-23130-y |
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