Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits

Bingxin Zhao, Yue Shan, Yue Yang, Zhaolong Yu, Tengfei Li, Xifeng Wang, Tianyou Luo, Ziliang Zhu, Patrick Sullivan, Hongyu Zhao, Yun Li () and Hongtu Zhu ()
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Bingxin Zhao: University of North Carolina at Chapel Hill
Yue Shan: University of North Carolina at Chapel Hill
Yue Yang: University of North Carolina at Chapel Hill
Zhaolong Yu: Yale University
Tengfei Li: University of North Carolina at Chapel Hill
Xifeng Wang: University of North Carolina at Chapel Hill
Tianyou Luo: University of North Carolina at Chapel Hill
Ziliang Zhu: University of North Carolina at Chapel Hill
Patrick Sullivan: University of North Carolina at Chapel Hill
Hongyu Zhao: Yale University
Yun Li: University of North Carolina at Chapel Hill
Hongtu Zhu: University of North Carolina at Chapel Hill

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-23130-y

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