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Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer’s disease

Nature Genetics volume 56, pages 2696–2703 (2024)Cite this article

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Abstract

Almost every recent Alzheimer’s disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and nonrandom participation in parental illness surveys, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that the current AD GWAX provide highly misleading genetic correlations between AD risk and higher education, which subsequently affects a variety of genetic epidemiological applications involving AD and cognition. Our study sheds light on potential issues in the design and analysis of middle-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

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Fig. 1: Comparing top association findings and genetic correlation results of AD GWAS and GWAX.
Fig. 2: AD GWAX biases risk prediction and causal inference.
Fig. 3: Schematic diagram for GWAS-by-subtraction.
Fig. 4: Genetic correlation of the AD and non-AD factors in GWAX with other complex traits.
Fig. 5: Genetic correlation of AD GWAS and GWAX with EA and coronary artery disease.
Fig. 6: Genetic correlation of meta-analyzed AD with EA and coronary artery disease.

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Data availability

Summary statistics for the AD GWAX are freely available at http://qlu-lab.org/data.html and the GWAS Catalog (parental AD status: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90448001-GCST90449000/GCST90448951/; parental AD status following the approach of Jansen et al.6: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90448001-GCST90449000/GCST90448949/; parental AD status following the approach of Marioni et al.5: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90448001-GCST90449000/GCST90448950/; parental health awareness in UKB: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90448001-GCST90449000/GCST90448952/; parental health awareness in AllofUs: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90448001-GCST90449000/GCST90448947/; participation in personal and family medical history survey in AllofUs: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90448001-GCST90449000/GCST90448948/). The HRS genetic data were accessed through NIAGADS with accession number NG00119.v1. UKB individual-level data used in the present work were obtained under application no. 42148.

Code availability

The code used in this study is available from the following websites: GSUB, GitHub (https://github.com/qlu-lab/GSUB) or Zenodo50 (https://doi.org/10.5281/zenodo.13845422); PLINK1.9 (https://www.cog-genomics.org/plink2/) and 2.0 (https://www.cog-genomics.org/plink/2.0/); Regenie, https://github.com/rgcgithub/regenie; Hail, https://hail.is/docs/0.2/index.html; GNOVA, https://github.com/qlu-lab/GNOVA-2.0; LDSC, https://github.com/bulik/ldsc; METAL, https://github.com/statgen/METAL; PRS-CS, https://github.com/getian107/PRScs. We also used the following R packages: GenomicSEM v0.0.4, tidyverse v2.0.0, data.table v1.14.8, mcr v1.3.3, WinCurse v0.0.1, lme4 v1.1.35.3, TwoSampleMR v0.5.6, sandwich v3.0.2 and glmnet v4.1.6.

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Acknowledgements

The authors gratefully acknowledge research support from National Institute on Aging (NIA) grant R21 AG085162 (Q.L.), NIA Center grant P30 AG017266 (Q.L.) and the University of Wisconsin−Madison Office of the Chancellor and the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation (WARF; Q.L.). The All of Us Research Program is supported by the National Institutes of Health (NIH), Office of the Director, Regional Medical Centers: 1 OT2 OD026549, 1 OT2 OD026554, 1 OT2 OD026557, 1 OT2 OD026556, 1 OT2 OD026550, 1 OT2 OD 026552, 1 OT2 OD026553, 1 OT2 OD026548, 1 OT2 OD026551, 1 OT2 OD026555; IAA AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337 and 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants. The Health and Retirement Study (HRS) genetic data were accessed through NIAGADS with accession no. NG00119.v1. These data were collected with financial support from NIH Director’s Opportunity for Research awards using American Reinvestment and Recovery Act funds (RC2 AG036495-01, RC4 AG039029-01). With these funds, the HRS has genotyped almost 20,000 respondents who provided DNA samples and signed consent forms in 2006–2012. The HRS data were produced and distributed by the University of Michigan under the directorship of D. R. Weir, with funding from the NIA (NIA U01AG009470). This research was conducted using the UKB Resource under application no. 42148. We acknowledge the participants and investigators of the FinnGen study. We thank the members of the Social Genomics Working Group at the University of Wisconsin−Madison for helpful comments.

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  1. These authors contributed equally: Yuchang Wu, Zhongxuan Sun.

Authors and Affiliations

  1. Department of Biostatistics and Medical Informatics, University of Wisconsin−Madison, Madison, WI, USA

    Yuchang Wu, Zhongxuan Sun, Qinwen Zheng, Jiacheng Miao, Stephen Dorn & Qiongshi Lu

  2. Department of Medicine, University of Washington, Seattle, WA, USA

    Shubhabrata Mukherjee

  3. Department of Population Health Sciences, University of Wisconsin−Madison, Madison, WI, USA

    Jason M. Fletcher

  4. La Follette School of Public Affairs, University of Wisconsin−Madison, Madison, WI, USA

    Jason M. Fletcher

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Contributions

Q.L. conceived and designed the study. Y.W. and Z.S. performed the analyses. Y.W. and Q.L. wrote the manuscript. S.D. implemented the software package for GWAS-by-subtraction. Q.Z. performed the GWAS on parental illness awareness in UKB. J.M. assisted with the mathematical derivations for GWAS-by-subtraction. S.M. advised on the genetics of AD and cognition. J.M.F. advised on the HRS cohort and social science issues. All authors revised and approved the manuscript.

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Correspondence to Qiongshi Lu.

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Wu, Y., Sun, Z., Zheng, Q. et al. Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer’s disease. Nat Genet 56, 2696–2703 (2024). https://doi.org/10.1038/s41588-024-01963-9

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