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An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank


UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer’s disease and mitochondrial disorders.

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Fig. 1: Manhattan plots for the four phenotypes achieving Bonferroni-corrected significance on the X chromosome.
Fig. 2: Heritability estimates (h2) for phenotypes grouped according to IDP categories.
Fig. 3: Paired-difference histograms for the sex-specific scans on the X chromosome.

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Data availability

Our resource includes openly released summary statistics and results for a variety of GWAS paradigms on the most recent release of 3,935 UKB brain imaging phenotypes. These results are released on BIG40 (, the European Bioinformatics Institute and the Supplementary Material of this paper. An enumeration of the aspects of our resource is as follows:

• Summary statistics for our disovery cohort, available on BIG40 (Manhattan plots, full downloads and a browsable interface) and European Bioinformatics Institute under study accession numbers GCST90002426–GCST90006360 ( to

• Details of the clusters of associations identified by Peaks, including summary statistics for replication, available on BIG40 and in Supplementary Tables 26

• Causal variants identified by CAVIAR for the four X chromosome clusters significant at the log10(P) ≥ 11.1 level (Supplementary Table 5)

• A full GWAS on all phenotypes and chromosomes, with the discovery and replication cohorts combined (available on the BIG40 website as a download and as a browsable interface)

• Sex-specific GWAS on the discovery cohort with genetic females and genetic males considered separately and combined through a Fisher meta-analysis (available as a download on BIG40)

• The heritability of each phenotype, assessed through LDSC on the full GWAS with discovery and replication cohorts combined (Supplementary Table 1)

Code availability

The following software packages and servers were used throughout this work:

• bgenie v1.3, software for efficient GWASs on high-dimensional phenotype data:

• qctool v1.4 and v2.0.1, software for pre-processing genetic data: and

• Peaks v1.0, novel software for extracting clusters from multi-phenotype GWASs:

• PheWeb v1.1.19, a web server for browsing phenome-wide associations:

• BIG40 open web server for Brain Imaging Genetics:

• plink2 v2.0, alpha software for conducting GWASs and pre-processing of genetic data:

• CAVIAR v2.0, fine mapping software for extracting causal variants from summary statistics:

• Open Targets Platform, an online web server for GWASs:

• LDSC v1.0.1, software for heritability analysis from summary statistics (linkage score regression):

• The GTEx online resource.


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This research was conducted, in part, using the UK Biobank Resource under application number 8107. We are grateful to UK Biobank for making the data available and to all UK Biobank study participants, who generously donated their time to make this resource possible. S.M.S. and G.D. were supported by Wellcome Trust Strategic Award 098369/Z/12/Z and Wellcome Trust Collaborative Award 215573/Z/19/Z. L.T.E. and W.C. were funded by NSERC grants RGPIN/05484-2019 and DGECR/00118-2019 and an NSERC Undergraduate Student Research Award. G.D. was supported by an MRC Career Development Fellowship (MR/K006673/1). The BIG40 Open Data Server is provided by the Wellcome Centre for Integrative Neuroimaging, which is supported by center funding from the Wellcome Trust (203139/Z/16/Z). Compute resources were provided by the Oxford Biomedical Research Computing (BMRC) facility (a joint development between Oxford’s Wellcome Centre for Human Genetics and the Big Data Institute, supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre). Some compute resources were also provided by ComputeCanada under the Resources for Research Groups program. We would like to thank the Resource Computing Managers at BMRC for their diligence in operation, with special thanks to J. Diprose, R. Esnouf and A. Huffman. We would also like to thank D. Mortimer, the Senior Informatics Officer at WIN FMRIB, and M. Siegert, the Research Computing Director and Site Lead at WestGrid/ComputeCanada. We are grateful to S. Shi, A. Winkler, T. Nichols, P. McCarthy, D. Greve and B. Fischl for helpful input.

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Authors and Affiliations



S.M.S. and L.T.E. co-directed this work. S.M.S., G.D., K.S. and L.T.E. interpreted the results and wrote the paper. All authors contributed to the analysis and the editing. W.C. wrote the Peaks software. T.H. created the BIG40 Pheweb resource. F.A. created novel brain imaging phenotypes for the UK Biobank.

Corresponding author

Correspondence to Lloyd T. Elliott.

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The authors declare no competing financial interests.

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Peer review information Nature Neuroscience thanks Alex Fornito, Jason Stein, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Comparisons of effect sizes and signs for genetic females and males.

Top row: Effect sizes for all associations with either genetic females or genetic males (or both) having -Log10(P) >= 7.5. Bottom row: effect sizes for all associations with either genetic females or genetic males (or both) having -Log10(P) >= 11.1. Left column: Scatter plots of effect sizes, indicating a small fraction (0.58%) of sign differences for -Log10(P) >= 7.5 and no sign differences (quadrants II and IV empty) for -Log10(P) >= 11.1 condition. Right column: Histograms of difference between effect sizes. Log y-scale indicates generally close matching of effect sizes.

Extended Data Fig. 2 Regional association plots of the significant variants in X.

First row: Region around rs2272737 (P = 3.5 × 10−21). This variant is an eQTL of FAM58A. Second row: Region around rs62595479 (P = 8.2 × 10−17). This variant is located in a pseudoautosomal region (PAR1) of X, in an intron of DHRSX. Third row: Region around rs644138 (P = 4.8 × 10−15). This variant is in an intron of SPRY3 (and is an eQTL in brain tissue of various genes). Bottom row: Region around rs12843772 (P = 5.1 × 10−12) located ≤150 bp from ZIC3. The genomic positions of the loci and genes are based on Human Genome build hg19. Regions considered include all loci within 10 kbp of the hit.

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Supplementary Tables 1–6.

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Smith, S.M., Douaud, G., Chen, W. et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci 24, 737–745 (2021).

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