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.

  • Article
  • Published:

An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics

European Journal of Human Genetics (2023)Cite this article

Subjects

Abstract

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.

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: Power comparison and ROC curves of UKCOR1 and M1.
Fig. 2: Power comparison and ROC curves of UKCOR1 and M2.
Fig. 3: Analysis results of the 58 Volumetric IDPs.

Similar content being viewed by others

Data availability

The author states that all data necessary for confirming the conclusions presented in the article are represented fully within the article. The UK Biobank summary statistics are available in the https://open.win.ox.ac.uk/ukbiobank/big40/BIGv2/.

Code availability

The software for our proposed method MTAFS is available at https://github.com/Qiaolan/MTAFS. All results for the real data analyses are at https://drive.google.com/drive/folders/1VpKHkT4mHnNXVesygkbt8KSx_pio0En9?usp=share_link.

References

  1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013;14:483–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. O’Reilly PF, Hoggart CJ, Pomyen Y, Calboli FCF, Elliott P, Jarvelin MR, et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PloS One. 2012;7:e34861.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Pan W. Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genetic Epidemiology: The Official Publication of the International Genetic Epidemiology. Society 2009;33:497–507.

    Google Scholar 

  5. He Q, Avery CL, Lin DY. A general framework for association tests with multivariate traits in large-scale genomics studies. Genet Epidemiol. 2013;37:759–67.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhu X, Feng T, Tayo BO, Liang J, Young JH, Franceschini N, et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am J Hum Genet. 2015;96:21–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Xu X, Tian L, Wei LJ. Combining dependent tests for linkage or association across multiple phenotypic traits. Biostatistics 2003;4:223–9.

    Article  PubMed  Google Scholar 

  8. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50:229–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Liu Y, Xie J. Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures. J Am Stat Assoc. 2020;115:393–402.

    Article  CAS  PubMed  Google Scholar 

  10. van der Meer D, Frei O, Kaufmann T, Shadrin AA, Devor A, Smeland OB, et al. Understanding the genetic determinants of the brain with MOSTest. Nat Commun. 2020;11:1–9.

    Google Scholar 

  11. Van der Sluis S, Posthuma D, Dolan CV. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 2013;9:e1003235.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Liu Z, Lin X. A geometric perspective on the power of principal component association tests in multiple phenotype studies. J Am Stat Assoc. 2019;114:975–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kim J, Bai Y, Pan W. An adaptive association test for multiple phenotypes with GWAS summary statistics. Genet Epidemiol. 2015;39:651–63.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ray D, Boehnke M. Methods for meta-analysis of multiple traits using GWAS summary statistics. Genet Epidemiol. 2018;42:134–45.

    Article  PubMed  Google Scholar 

  15. Liu Z, Lin X. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 2018;74:165–75.

    Article  PubMed  Google Scholar 

  16. Guo B, Wu B. Integrate multiple traits to detect novel trait–gene association using GWAS summary data with an adaptive test approach. Bioinformatics 2019;35:2251–7.

    Article  CAS  PubMed  Google Scholar 

  17. Wu C. Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank. Genetics 2020;215:947–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 2018;562:210–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Song C, Min X, Zhang H. The screening and ranking algorithm for change-points detection in multiple samples. Ann Appl Stat. 2016;10:2102.

    Article  PubMed  Google Scholar 

  21. David HA, Nagaraja HN. Order statistics: John Wiley & Sons; 2004.

  22. Wu B, Guan W, Pankow JS. On efficient and accurate calculation of significance p-values for sequence kernel association testing of variant set. Ann Hum Genet. 2016;80:123–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Aschard H, Vilhjálmsson BJ, Greliche N, Morange PE, Trégouët DA, Kraft P. Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Am J Hum Genet. 2014;94:662–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sporns O, Tononi G, Kötter R. The human connectome: a structural description of the human brain. PLoS Comput Biol. 2005;1:e42.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bi Xa HuX, Xie Y, Wu H. A novel CERNNE approach for predicting Parkinson’s Disease-associated genes and brain regions based on multimodal imaging genetics data. Med Image Anal. 2021;67:101830.

    Article  PubMed  Google Scholar 

  26. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Schulz H, Ruppert AK, Herms S, Wolf C, Mirza-Schreiber N, Stegle O, et al. Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus. Nat Commun. 2017;8:1511.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sayers EW, Beck J, Bolton EE, Bourexis D, Brister JR, Canese K, et al. Database resources of the national center for biotechnology information. Nucleic acids Res. 2021;49:D10.

    Article  CAS  PubMed  Google Scholar 

  29. Watanabe K, Taskesen E, Van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1–11.

    Article  CAS  Google Scholar 

  30. Li MX, Yeung JMY, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet. 2012;131:747–56.

    Article  CAS  PubMed  Google Scholar 

  31. Chambers T, Escott-Price V, Legge S, Baker E, Singh KD, Walters JTR, et al. Genetic common variants associated with cerebellar volume and their overlap with mental disorders: a study on 33,265 individuals from the UK-Biobank. Mol Psychiatry. 2022;27:2282–90.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K, et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci. 2021;24:737–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zhao B, Luo T, Li T, Li Y, Zhang J, Shan Y, 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. 2019;51:1637–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhao B, Zhang J, Ibrahim JG, Luo T, Santelli RC, Li Y, 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. 2021;26:3943–55.

    Article  PubMed  Google Scholar 

  35. Van Der Meer D, Kaufmann T, Shadrin AA, Makowski C, Frei O, Roelfs D, et al. The genetic architecture of human cortical folding. Sci Adv 2021;7:eabj9446.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  CAS  Google Scholar 

  37. Hofer E, Roshchupkin GV, Adams HHH, Knol MJ, Lin H, Li S, et al. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nat Commun. 2020;11:1–16.

    Article  Google Scholar 

  38. van Der Lee SJ, Knol MJ, Chauhan G, Satizabal CL, Smith AV, Hofer E, et al. A genome-wide association study identifies genetic loci associated with specific lobar brain volumes. Commun Biol. 2019;2:285.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank the Section Editor and two anonymous reviewers for their valuable comments and suggestions. We would also like to acknowledge the Ohio Supercomputer Center for providing resources that have contributed to the research results reported within this paper.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA

    Qiaolan Deng & Chi Song

  2. Department of Statistics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA

    Qiaolan Deng & Shili Lin

Authors
  1. Qiaolan Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chi Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shili Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QD conceived the study design, performed analyses, developed methodology and drafted the manuscript. CS and SL provided extensive feedback regarding the study design, methodology and analyses, and revised the manuscript.

Corresponding author

Correspondence to Shili Lin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The UK Biobank brain imaging data (https://open.win.ox.ac.uk/ukbiobank/big40/BIGv2/) were publicly available without identifying information.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, Q., Song, C. & Lin, S. An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics. Eur J Hum Genet (2023). https://doi.org/10.1038/s41431-023-01389-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41431-023-01389-7

Search

Advanced search

Quick links