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Genetic analyses identify widespread sex-differential participation bias

Nature Genetics volume 53, pages 663–671 (2021)Cite this article

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Abstract

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging since it requires the genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study contrasting one subgroup versus another. For example, we showed that sex exhibits artifactual autosomal heritability in the presence of sex-differential participation bias. By performing a genome-wide association study of sex in approximately 3.3 million males and females, we identified over 158 autosomal loci spuriously associated with sex and highlighted complex traits underpinning differences in study participation between the sexes. For example, the body mass index–increasing allele at FTO was observed at higher frequency in males compared to females (odds ratio = 1.02, P = 4.4 × 10−36). Finally, we demonstrated how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

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Fig. 1: Manhattan plot for a GWAS of sex in 2,462,132 participants from 23andMe.
Fig. 2: SNP heritability on the liability scale for sex across five studies.
Fig. 3: Illustration of the concept and consequences of sex-differential participation bias.
Fig. 4: Genetic correlation with being born female versus male and 38 traits in the UK Biobank and 23andMe.
Fig. 5: PS distribution highlights sex-differential participation by educational level in the UK Biobank.

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Data availability

The GWAS results are available through the GWAS catalog accession nos. GCST90013473 (23andMe) and GCST90013474. Full summary statistics for 23andMe are available upon request from https://research.23andme.com/dataset-access/.

Code availability

Scripts are available at https://github.com/dsgelab/genobias.

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Acknowledgements

We thank G. Davey Smith for insightful comments. This research was conducted by using the UK Biobank resource under application no. 31063. A.G. was supported by the Academy of Finland Fellowship (no. 323116). This work was supported by the Medical Research Council (Unit Programme number MC_UU_12015/2). M.G.N. is a fellow of the Jacobs Foundation and is supported by ZonMw grant nos. 849200011 and 531003014 from the Netherlands Organization for Health Research and Development and a VENI grant awarded by the Dutch Research Council (VI.Veni.191 G.030). A. Abdellaoui is supported by the Foundation Volksbond Rotterdam and ZonMw grant no. 849200011 from the Netherlands Organization for Health Research and Development. The FinnGen project is funded by 2 grants from Business Finland (nos. HUS 4685/31/2016 and UH 4386/31/2016) and 11 industry partners (AbbVie, AstraZeneca UK, Biogen MA, Celgene Corporation, Celgene International II Sàrl, Genentech, Merck Sharp & Dohme Corp, Pfizer, GlaxoSmithKline, Sanofi, Maze Therapeutics, Janssen Biotech). We thank the following biobanks for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki/); THL Biobank (https://thl.fi/en/web/thl-biobank); Helsinki Biobank (https://www.helsinginbiopankki.fi/fi/etusivu); Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx); Finnish Clinical Biobank Tampere (https://www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere); Biobank of Eastern Finland (https://ita-suomenbiopankki.fi/en/); Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki); Finnish Red Cross Blood Service Biobank (https://www.veripalvelu.fi/verenluovutus/biopankkitoiminta); and Terveystalo Biobank (https://www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). All Finnish Biobanks are members of the BBMRI.fi infrastructure (http://www.bbmri.fi/). We thank the research participants and employees of 23andMe who contributed to this study.

Author information

Author notes

  1. These authors contributed equally: Nicola Pirastu, Mattia Cordioli, Michel G. Nivard, John R. B. Perry, Andrea Ganna.

Authors and Affiliations

  1. Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK

    Nicola Pirastu & Peter Joshi

  2. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

    Mattia Cordioli, Gianmarco Mignogna, Pietro Della Briotta Parolo, Juha Karjalainen & Andrea Ganna

  3. 23andMe, Inc., Sunnyvale, CA, USA

    Priyanka Nandakumar, Michelle Agee, Stella Aslibekyan, Robert K. Bell, Katarzyna Bryc, Sarah K. Clark, Sarah L. Elson, Kipper Fletez-Brant, Pierre Fontanillas, Nicholas A. Furlotte, Pooja M. Gandhi, Karl Heilbron, Barry Hicks, Karen E. Huber, Ethan M. Jewett, Yunxuan Jiang, Aaron Kleinman, Keng-Han Lin, Nadia K. Litterman, Marie K. Luff, Matthew H. McIntyre, Kimberly F. McManus, Joanna L. Mountain, Sahar V. Mozaffari, Elizabeth S. Noblin, Carrie A. M. Northover, Jared O’Connell, Aaron A. Petrakovitz, Steven J. Pitts, G. David Poznik, J. Fah Sathirapongsasuti, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Robert J. Tunney, Vladimir Vacic, Xin Wang, Amir Zare, Adam Auton & David Hinds

  4. Department of Statistics and Quantitative Methods, University of Milano Bicocca, Milan, Italy

    Gianmarco Mignogna & Rino Bellocco

  5. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Gianmarco Mignogna, Masahiro Kanai, Nikolas Baya, Caitlin E. Carey, Juha Karjalainen, Benjamin M. Neale, Raymond K. Walters & Andrea Ganna

  6. Department of Psychiatry, Amsterdam Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands

    Abdel Abdellaoui

  7. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

    Benjamin Hollis, Felix R. Day, Ken K. Ong & John R. B. Perry

  8. The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Benjamin Hollis

  9. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Masahiro Kanai, Juha Karjalainen & Andrea Ganna

  10. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Masahiro Kanai

  11. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Masahiro Kanai

  12. Department of Biomedicine, Aarhus University, Aarhus, Denmark

    Veera M. Rajagopal, Thomas D. Als & Anders D. Børglum

  13. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark

    Veera M. Rajagopal, Thomas D. Als, Preben Bo Mortensen, Ole Mors, Thomas Werge, Merete Nordentoft, David M. Hougaard, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen & Anders D. Børglum

  14. Centre for Genomics and Personalized Medicine, Center for Genimics and Personalized Medice, Aarhus University, Aarhus, Denmark

    Veera M. Rajagopal, Thomas D. Als & Anders D. Børglum

  15. Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark

    Veera M. Rajagopal, Thomas D. Als & Anders D. Børglum

  16. Stanley Center for Psychiatric Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Nikolas Baya, Caitlin E. Carey, Benjamin M. Neale & Raymond K. Walters

  17. Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands

    Matthijs D. Van der Zee, Eco de Geus & Michel G. Nivard

  18. Department of Paediatrics, University of Cambridge, Cambridge, UK

    Ken K. Ong

  19. Division of Molecular Pathology, Institute of Medical Sciences, University of Tokyo, Tokyo, Japan

    Takayuki Morisaki

  20. BioBank Japan, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Takayuki Morisaki

  21. Department of Internal Medicine, Institute of Medical Science, University of Tokyo Hospital, Tokyo, Japan

    Takayuki Morisaki

  22. Amsterdam Public Health Research institute, Amsterdam, the Netherlands

    Eco de Geus

  23. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Rino Bellocco

  24. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Yukinori Okada

  25. Laboratory of Statistical Immunology, World Premier International Immunology Frontier Research Center, Osaka University, Suita, Japan

    Yukinori Okada

  26. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan

    Yukinori Okada

  27. Amsterdam Public Health, Methodology Program, Amsterdam, the Netherlands

    Michel G. Nivard

  28. Amsterdam Neuroscience-Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam, the Netherlands

    Michel G. Nivard

  29. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark

    Preben Bo Mortensen

  30. Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark

    Preben Bo Mortensen

  31. Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark

    Ole Mors

  32. Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark

    Thomas Werge

  33. Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark

    Merete Nordentoft

  34. Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark

    David M. Hougaard, Jonas Bybjerg-Grauholm & Marie Bækvad-Hansen

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  1. Nicola Pirastu
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Consortia

FinnGen Study

  • Mattia Cordioli
  • , Pietro Della Briotta Parolo
  • , Juha Karjalainen
  •  & Andrea Ganna

23andMe Research Team

  • Michelle Agee
  • , Stella Aslibekyan
  • , Robert K. Bell
  • , Katarzyna Bryc
  • , Sarah K. Clark
  • , Sarah L. Elson
  • , Kipper Fletez-Brant
  • , Pierre Fontanillas
  • , Nicholas A. Furlotte
  • , Pooja M. Gandhi
  • , Karl Heilbron
  • , Barry Hicks
  • , Karen E. Huber
  • , Ethan M. Jewett
  • , Yunxuan Jiang
  • , Aaron Kleinman
  • , Keng-Han Lin
  • , Nadia K. Litterman
  • , Marie K. Luff
  • , Matthew H. McIntyre
  • , Kimberly F. McManus
  • , Joanna L. Mountain
  • , Sahar V. Mozaffari
  • , Elizabeth S. Noblin
  • , Carrie A. M. Northover
  • , Jared O’Connell
  • , Aaron A. Petrakovitz
  • , Steven J. Pitts
  • , G. David Poznik
  • , J. Fah Sathirapongsasuti
  • , Janie F. Shelton
  • , Suyash Shringarpure
  • , Chao Tian
  • , Joyce Y. Tung
  • , Robert J. Tunney
  • , Vladimir Vacic
  • , Xin Wang
  •  & Amir Zare

iPSYCH Consortium

  • Preben Bo Mortensen
  • , Ole Mors
  • , Thomas Werge
  • , Merete Nordentoft
  • , David M. Hougaard
  • , Jonas Bybjerg-Grauholm
  •  & Marie Bækvad-Hansen

Contributions

N.P., M.C., P.N., C.E.C., M.D.V.d.Z., A. Abdellaoui, D.H., B.M.N., R.K.W., M.G.N., J.R.B.P. and A.G. designed the study. N.P., M.C., P.N., G.M., A. Abdellaoui, B.H., M.K., V.M.R., P.D.B.P., N.B., J.K., T.D.A., M.D.V.d.Z., R.B., A.D.B., A. Auton, D.H., M.G.N., J.R.B.P. and A.G. analyzed the data. N.P., M.C., A. Abdellaoui, C.E.C., F.R.D., K.K.O., R.B., P.J., B.M.N., R.K.W., M.G.N., J.R.B.P. and A.G. interpreted the results. P.N., A. Abdellaoui, V.M.R., T.D.A., T.M., E.d.G., Y.O., A.D.B., A. Auton, D.H., B.M.N., M.G.N., J.R.B.P. and A.G. provided the data. N.P., M.C., B.M.N., M.G.N., J.R.B.P. and A.G. wrote the manuscript. 23 and Me Research Team, FinnGen Study and iPSYCH Consortium provided data.

Corresponding authors

Correspondence to John R. B. Perry or Andrea Ganna.

Ethics declarations

Competing interests

P.N., A. Auton and D.H. are employed by 23andMe. P.J. is a paid consultant to Global Gene Corp and Humanity Inc.

Additional information

Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Different participation bias scenarios that may lead to a correlation between sex and genetic variants.

S, selection (that is participation in the study); X, trait; Gx, genotype causing X. The assumed causal paths are shown in blue, and the induced correlations are shown in red. Three scenarios exist in which sex can become heritable due to selection. a, Sex causes X which in turn causes selection. b, X and sex influence the selection independently. c, The effect of X on selection is different between the two sexes. This is the scenario discussed in the paper. We have run simulations (Supplementary Fig. 3) and scenarios a and b are less likely to be observed because the effect of the trait on selection would need to be extremely large.

Extended Data Fig. 2 Effect size for association between SNPs and sex in 23andMe.

On the y-axis is the effect in the entire study population (n = 2,462,132), and on the x-axis is the effect only among those younger than 30 years (n = 320,366). Error bars represent the confidence intervals for the effect size estimates.

Extended Data Fig. 3 Effect of sex-differential participation bias on the genetic correlation between y0 and y1 when the phenotypes have h2 = 0.1 or h2 = 0.3.

Each line represents a different degree of participation bias, expressed as the odds ratio (OR) used for the sampling. The higher the OR, the higher the degree of participation bias. The x-axis represents different values for the parameter k that gives the sex-differential effect. The smaller k is, the higher is the degree of the sex-differential effect. Under no partecipation bias or sex-differential effect y0 and y1 have a genetic correlation equal to 0.

Extended Data Fig. 4 Effects of sex differential bias on the BMI→T2D relationship.

The forest plot shows the effect of sampling men and women differentially based on BMI. The x-axis represents different values of bias introduced. For higher values, heavier males and leaner women are randomly picked. The number on top of the segment represents the P-value of the difference in effect between the two sexes using the Z-score method. The bias becomes large enough to be detected as ‘significant’ even at the lower values of bias applied. The straight lines represent the effect of BMI on T2D estimated without any sample selection.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–6

Reporting Summary

Supplementary Tables

Supplementary Tables 1–11

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Pirastu, N., Cordioli, M., Nandakumar, P. et al. Genetic analyses identify widespread sex-differential participation bias. Nat Genet 53, 663–671 (2021). https://doi.org/10.1038/s41588-021-00846-7

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