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Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

Nature Genetics volume 51, pages 1637–1644 (2019)Cite this article

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Abstract

Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10−10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10−24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.

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Fig. 1: The number of independent significant variant-level associations discovered in UKB GWAS (n = 19,629 subjects) at different significance levels.
Fig. 2: Genes identified in gene-based association analysis of ROI volumes (n = 19,629 subjects) that have been linked to cognitive traits and mental health disorders in previous GWAS.
Fig. 3: Selected pairwise genetic correlations between ROI volumes (n = 21,821 subjects) and other traits.
Fig. 4: Prediction accuracy (incremental R-squared) of PRSs constructed by UKB GWAS (n = 19,629 subjects) summary statistics on the four independent datasets.

Data availability

The data used in this work were obtained from five publicly available datasets: the UKB study, the HCP study, the PING study, the PNC study and the ADNI study. We used 50 sets of publicly available GWAS summary statistics from several GWAS databases. The data resources are summarized in Supplementary Table 24. All UKB and meta-analysis GWAS summary statistics of 101 ROI volumes can be found at https://github.com/BIG-S2/GWAS.

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 US National Institutes of Health (NIH) grants MH086633 (H.Z.) and MH116527 (T.Li), and a grant from the Cancer Prevention Research Institute of Texas (H.Z.). We thank the individuals represented in the UKB, ADNI, HCP, PING and 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 UKB resource (application number 22783), subject to a data transfer agreement. We gratefully acknowledge all of the studies and databases that made GWAS summary data available. Part of data collection and sharing for this project was funded by the ADNI (NIH grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen Idec; Bristol-Myers Squibb; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research and Development; Johnson and Johnson Pharmaceutical Research and Development; Medpace.; Merck and Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Synarc; and Takeda Pharmaceutical. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Part of the data collection and sharing for this project was funded by the PING study (US NIH grant RC2DA029475). PING is funded by the National Institute on Drug Abuse and the Eunice Kennedy Shriver National Institute of Child Health and Human Development. PING data are disseminated by the PING Coordinating Center at the Center for Human Development, University of California, San Diego. Support for the collection of the PNC datasets was provided by grant RC2MH089983 awarded to R. Gur and RC2MH089924 awarded to H. Hakonarson. All PNC subjects were recruited through the Center for Applied Genomics at The Children’s Hospital in Philadelphia. HCP data were provided by the HCP, WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

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Authors and Affiliations

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

    Bingxin Zhao, Tianyou Luo, Yun Li, Yue Shan, Xifeng Wang, Fan Zhou, Ziliang Zhu & Hongtu Zhu

  2. Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Tengfei Li

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

    Tengfei Li & Hongtu Zhu

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

    Yun Li

  5. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Yun Li

  6. Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Jingwen Zhang

  7. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Liuqing Yang

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  1. Bingxin Zhao
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Consortia

Alzheimer’s Disease Neuroimaging Initiative

Pediatric Imaging, Neurocognition and Genetics

Contributions

B.Z., H.Z. and Y.L. designed the study. B.Z. and T.Luo performed the experiments and analyzed the data. T.Li, J.Z., T.Luo, Y.S., X.W., L.Y., F.Z. and Z.Z. downloaded the datasets, preprocessed MRI and DNA data, and undertook the quantity controls. B.Z., H.Z. and Y.L. wrote the manuscript with feedback from all authors.

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Correspondence to Hongtu Zhu.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–17 and Note

Reporting Summary

Supplementary Tables

Supplementary Tables 1–30

Supplementary Dataset 1

GWAS Manhattan plots for all 101 ROI volumes (n = 19,629 subjects).

Supplementary Dataset 2

GWAS quantile–quantile plots for all 101 ROI volumes (n = 19,629 subjects).

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Zhao, B., Luo, T., Li, T. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat Genet 51, 1637–1644 (2019). https://doi.org/10.1038/s41588-019-0516-6

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  • DOI: https://doi.org/10.1038/s41588-019-0516-6

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