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Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706)

Molecular Psychiatry volume 26, pages 3943–3955 (2021)Cite this article

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Abstract

Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 × 10−05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 × 10−12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value < 4.5 × 10−10, adjusted for testing multiple phenotypes). Gene-based association study prioritized 112 significant genes, most of which are novel. More importantly, association lookups found that many of the novel SNPs and genes of DTI parameters have previously been implicated with cognitive and mental health traits. In conclusion, the present study identifies many new genetic variants at SNP, locus and gene levels for integrity of brain WM tracts and provides the overview of pleiotropy with cognitive and mental health traits.

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Code availability

We made use of publicly available software and tools. All codes used to generate results that are reported in this paper are available upon request.

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Acknowledgements

This research was partially supported by U.S. NIH grants MH086633 and MH116527, and a grant from the Cancer Prevention Research Institute of Texas. We thank the individuals represented in the UK Biobank and the Philadelphia Neurodevelopmental Cohort (PNC) datasets for their participation and the research teams for their work in collecting, processing and disseminating these datasets for analysis. This research has been conducted using the UK Biobank resource (application number 22783), subject to a data transfer agreement. Ethics approval for the UK Biobank study was obtained from the North West Centre for Research Ethics Committee (11/NW/0382). For the PNC study, the institutional review boards of both the University of Pennsylvania and the Children’s Hospital of Philadelphia approved all study procedures. Informed consent was obtained from all subjects. We gratefully acknowledge all the studies and databases that made their GWAS summary data available. The authors acknowledge the Texas Advanced Computing Center (TACC, http://www.tacc.utexas.edu) at The University of Texas at Austin for providing HPC and storage resources that have contributed to the research results reported within this paper.

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Authors and Affiliations

  1. Departments of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Bingxin Zhao, Jingwen Zhang, Joseph G. Ibrahim, Tianyou Luo, Yun Li, Yue Shan, Ziliang Zhu, Fan Zhou & Hongtu Zhu

  2. Departments of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Rebecca C. Santelli

  3. Departments of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Yun Li

  4. Departments of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Yun Li

  5. Departments of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Tengfei Li

  6. Biomedical Research Imaging Center, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Tengfei Li & Hongtu Zhu

  7. Department of Statistics, Texas A&M University, College Station, TX, 77843, USA

    Huiling Liao

  8. Wellcome Trust Centre for Integrative Neuroimaging, Big Data Institute, University of Oxford, Oxford, OX1 2JD, UK

    Thomas E. Nichols

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Zhao, B., Zhang, J., Ibrahim, J.G. et al. Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706). Mol Psychiatry 26, 3943–3955 (2021). https://doi.org/10.1038/s41380-019-0569-z

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