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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...

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Autores principales: Zhao, Bingxin, Shan, Yue, Yang, Yue, Yu, Zhaolong, Li, Tengfei, Wang, Xifeng, Luo, Tianyou, Zhu, Ziliang, Sullivan, Patrick, Zhao, Hongyu, Li, Yun, Zhu, Hongtu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
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.
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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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