Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits.
Bingxin ZhaoYue ShanYue YangZhaolong YuTengfei LiXifeng WangTianyou LuoZiliang ZhuPatrick SullivanHongyu ZhaoYun LiHongtu ZhuPublished in: Nature communications (2021)
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.
Keyphrases
- genome wide
- dna methylation
- copy number
- high resolution
- genome wide identification
- genome wide association
- white matter
- small molecule
- gene expression
- resting state
- machine learning
- cerebral ischemia
- single cell
- big data
- brain injury
- mass spectrometry
- subarachnoid hemorrhage
- data analysis
- blood brain barrier
- functional connectivity
- fluorescence imaging