Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

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

Nature Neuroscience volume 24, pages 737–745 (2021)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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 (https://open.win.ox.ac.uk/ukbiobank/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 (ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90002426 to ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90006360)

• Details of the clusters of associations identified by Peaks, including summary statistics for replication, available on BIG40 and in Supplementary Tables 2–6

• 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: https://jmarchini.org/bgenie/53

• qctool v1.4 and v2.0.1, software for pre-processing genetic data: https://www.well.ox.ac.uk/~gav/qctool_v1/ and https://www.well.ox.ac.uk/~gav/qctool_v2/

• Peaks v1.0, novel software for extracting clusters from multi-phenotype GWASs: https://github.com/wnfldchen/peaks

• PheWeb v1.1.19, a web server for browsing phenome-wide associations: https://github.com/statgen/pheweb60

• BIG40 open web server for Brain Imaging Genetics: https://open.win.ox.ac.uk/ukbiobank/big40

• plink2 v2.0, alpha software for conducting GWASs and pre-processing of genetic data: https://www.cog-genomics.org/plink/2.0/56

• CAVIAR v2.0, fine mapping software for extracting causal variants from summary statistics: http://genetics.cs.ucla.edu/caviar/3

• Open Targets Platform, an online web server for GWASs: https://genetics-app.netlify.app/61

• LDSC v1.0.1, software for heritability analysis from summary statistics (linkage score regression): https://github.com/bulik/ldsc/7

• The GTEx online resource. https://gtexportal.org/home/62

References

  1. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. F. Alfaro-Almagro, et al. Confound modelling in UK Biobank brain imaging. Neuroimage (in the press).

  3. Hormozdiari, F., Kostem, E., Yong Kang, E., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198, 497–508 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Clayton, D. Testing for association on the X chromosome. Biostatistics 9, 593–600 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Özbek, U. et al. Statistics for X-chromosome associations. Genet. Epidemiol. 42, 539–550 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Saleem F. & Rizvi S. W. Transgender associations and possible etiology: a literature review. Cureus 9, e1984 (2017).

  9. Unger, S. et al. Mutations in the cyclin family member FAM58A cause an X-linked dominant disorder characterized by syndactyly, telecanthus and anogenital and renal malformations. Nat. Genet. 40, 287–289 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Bedeschi, M. F. et al. STAR syndrome plus: the first description of a female patient with the lethal form. Am. J. Med. Genet. 173, 3226–3230 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Guen, V. J. et al. A homozygous deleterious CDK10 mutation in a patient with agenesis of corpus callosum, retinopathy, and deafness. Am. J. Med. Genet. 176, 92–98 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Suzuki, K. et al. Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population. Nat. Genet. 51, 379–386 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).

    Article  CAS  PubMed  Google Scholar 

  14. Tian, Y. et al. Y chromosome gene expression in the blood of male patients with ischemic stroke compared with male controls. Gend. Med. 9, 68–75 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang, W. et al. Disrupted functional connectivity of the hippocampus in patients with hyperthyroidism: evidence from resting-state fMRI. Eur. J. Radiol. 83, 1907–1913 (2014).

    Article  PubMed  Google Scholar 

  16. Vanmarsenille, L. et al. Increased dosage of RAB39B affects neuronal development and could explain the cognitive impairment in male patients with distal Xq28 copy number gains. Hum. Mutat. 35, 377–383 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Giannandrea, M. et al. Mutations in the small GTPase gene RAB39B are responsible for X-linked mental retardation associated with autism, epilepsy, and macrocephaly. Am. J. Hum. Genet. 86, 185–195 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wilson, G. R. et al. Mutations in RAB39B cause X-linked intellectual disability and early-onset Parkinson disease with α-synuclein pathology. Am. J. Hum. Genet. 95, 729–735 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Celestino-Soper, P. B. S. et al. Use of array CGH to detect exonic copy number variants throughout the genome in autism families detects a novel deletion in TMLHE. Hum. Mol. Genet. 20, 4360–4370 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Takano, K. et al. An X-linked channelopathy with cardiomegaly due to a CLIC2 mutation enhancing ryanodine receptor channel activity. Hum. Mol. Genet. 21, 4497–4507 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Miskinyte, S. et al. Loss of BRCC3 deubiquitinating enzyme leads to abnormal angiogenesis and is associated with syndromic moyamoya. Am. J. Hum. Genet. 88, 718–728 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rosas, H. D. et al. Cerebral cortex and the clinical expression of Huntington’s disease: complexity and heterogeneity. Brain 131, 1057–1068 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ware, S. M. et al. Identification and functional analysis of ZIC3 mutations in heterotaxy and related congenital heart defects. Am. J. Hum. Genet. 74, 93–105 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Witt, S. T., van Ettinger-Veenstra, H., Salo, T., Riedel, M. C. & Laird, A.R. What executive function network is that? An image-based meta-analysis of network labels. Preprint at bioRxiv https://doi.org/10.1101/2020.07.14.201202 (2020).

  25. Dubois, J. et al. Structural asymmetries of perisylvian regions in the preterm newborn. Neuroimage 52, 32–42 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Wiberg, A. et al. Handedness, language areas and neuropsychiatric diseases: Insights from brain imaging and genetics. Brain 142, 2938–2947 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Purandare, S. M. et al. A complex syndrome of left-right axis, central nervous system and axial skeleton defects in Zic3 mutant mice. Development 129, 2293–2302 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Dyer, A. H., Vahdatpour, C., Sanfeliu, A. & Tropea, D. The role of insulin-like growth factor 1 (IGF-1) in brain development, maturation and neuroplasticity. Neuroscience 325, 89–99 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Westwood, A. J. et al. Insulin-like growth factor-1 and risk of Alzheimer dementia and brain atrophy. Neurology 82, 1613–1619 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bates, K. A. et al. Clearance mechanisms of Alzheimer’s amyloid-β peptide: implications for therapeutic design and diagnostic tests. Mol. Psychol. 14, 469–486 (2009).

    Article  CAS  Google Scholar 

  31. Carrel, L. & Willard, H. F. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 434, 400–404 (2005).

    Article  CAS  PubMed  Google Scholar 

  32. Balaton, B. P., Cotton, A. M. & Brown, C. J. Derivation of consensus inactivation status for X-linked genes from genome-wide studies. Biol. Sex. Differ. 6, 1–11 (2015).

    Article  CAS  Google Scholar 

  33. Zhao, B. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhao, B. 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 https://doi.org/10.1038/s41380-019-0569-z (2019).

  35. Salat, D. H. et al. Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage 48, 21–28 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Thiffault, I. et al. A new autosomal recessive spastic ataxia associated with frequent white matter changes maps to 2q33–34. Brain 129, 2332–2340 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Cai, D. et al. Phospholipase D1 corrects impaired βAPP trafficking and neurite outgrowth in familial Alzheimer’s disease-linked presenilin-1 mutant neurons. Proc. Natl Acad. Sci. USA 103, 1936–1940 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Krishnan, B., Kayed, R. & Taglialatela, G. Elevated phospholipase D isoform 1 in Alzheimer’s disease patients’ hippocampus: relevance to synaptic dysfunction and memory deficits. Alzheimers Dement. (N Y) 4, 89–102 (2018).

    Article  Google Scholar 

  39. Jin, J. K. et al. Phospholipase D1 is up-regulated in the mitochondrial fraction from the brains of Alzheimer’s disease patients. Neurosci. Lett. 407, 263–267 (2006).

    Article  CAS  PubMed  Google Scholar 

  40. Solis, C. et al. Acute intermittent porphyria: studies of the severe homozygous dominant disease provides insights into the neurologic attacks in acute porphyrias. Arch. Neurol. 61, 1764–1770 (2004).

    Article  PubMed  Google Scholar 

  41. Lindberg, R. L. et al. Porphobilinogen deaminase deficiency in mice causes a neuropathy resembling that of human hepatic porphyria. Nat. Genet. 12, 195–199 (1996).

    Article  CAS  PubMed  Google Scholar 

  42. Homedan, C. et al. Mitochondrial energetic defects in muscle and brain of a Hmbs−/− mouse model of acute intermittent porphyria. Hum. Mol. Genet. 24, 5015–5023 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Zhang, J. et al. A founder mutation in VPS11 causes an autosomal recessive leukoencephalopathy linked to autophagic defects. PLoS Genet. 12, e1005848 (2016).

  44. Fecher, C. et al. Cell-type-specific profiling of brain mitochondria reveals functional and molecular diversity. Nat. Neurosci. 22, 1731–1742 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Garcia-Esparcia, P. et al. Mitochondrial activity in the frontal cortex area 8 and angular gyrus in Parkinson’s disease and Parkinson’s disease with dementia. Brain Pathol. 28, 43–57 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Lau, D. H. et al. Disruption of endoplasmic reticulum-mitochondria tethering proteins in post-mortem Alzheimer’s disease brain. Neurobiol. Dis. 143, 105020 (2020).

  47. Huang, Y. L. et al. Human CLEC18 gene cluster contains C-type lectins with differential glycan-binding specificity. J. Biol. Chem. 290, 21252–21263 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Jickling, G. C. & Sharp, F. R. Biomarker panels in ischemic stroke. Stroke 46, 915–920 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Finsterer, J. Central nervous system imaging in mitochondrial disorders. Can. J. Neurol. Sci. 36, 143–153 (2009).

    Article  PubMed  Google Scholar 

  50. Klosinski, L. P. et al. White matter lipids as a ketogenic fuel supply in aging female brain: implications for Alzheimer’s disease. EBioMedicine 2, 1888–1904 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bycrof, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  Google Scholar 

  54. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).

  55. König, I. R., Loley, C., Erdmann, J. & Ziegler, A. How to include chromosome X in your genome-wide association study. Genet. Epidemiol. 38, 97–103 (2014).

    Article  PubMed  Google Scholar 

  56. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  57. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  58. Fisher, R. A. Questions and answers #14. Am. Statistician 2, 30–33 (1948).

    Google Scholar 

  59. Suchenek, M. A. Elementary yet precise worst-case analysis of Floyd’s heap-construction program. Fundam. Inform. 120, 75–92 (2012).

    Article  Google Scholar 

  60. Gagliano, S. A. et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat. Genet. 52, 550–552 (2020).

    Article  CAS  Google Scholar 

  61. Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, 1056–1065 (2019).

    Article  CAS  Google Scholar 

  62. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom

    Stephen M. Smith, Gwenaëlle Douaud, Taylor Hanayik & Fidel Alfaro-Almagro

  2. Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada

    Winfield Chen & Lloyd T. Elliott

  3. Genomics PLC, Oxford, United Kingdom

    Kevin Sharp

Authors
  1. Stephen M. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gwenaëlle Douaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Winfield Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taylor Hanayik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fidel Alfaro-Almagro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kevin Sharp
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lloyd T. Elliott
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–6.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1038/s41593-021-00826-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00826-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing