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Applying weighted Cox regression to genome-wide association studies of time-to-event phenotypes

Nature Computational Science volume 5, pages 1064–1079 (2025)Cite this article

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Abstract

With the growing availability of time-stamped electronic health records linked to genetic data in large biobanks and cohorts, time-to-event phenotypes are increasingly studied in genome-wide association studies. Although numerous Cox-regression-based methods have been proposed for a large-scale genome-wide association study, case ascertainment in time-to-event phenotypes has not been well addressed. Here we propose a computationally efficient Cox-based method, named WtCoxG, that accounts for case ascertainment by fitting a weighted Cox proportional hazards null model. A hybrid strategy incorporating saddlepoint approximation largely increases its accuracy when analyzing low-frequency and rare variants. Notably, by leveraging external minor allele frequencies from public resources, WtCoxG further boosts statistical power. Extensive simulation studies demonstrated that WtCoxG is more powerful than ADuLT and other Cox-based methods, while effectively controlling type I error rates. UK Biobank real data analysis validated that leveraging external minor allele frequencies contributes to the power gains of WtCoxG compared with ADuLT in the analysis of type 2 diabetes and coronary atherosclerosis.

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Fig. 1: Pipeline of the WtCoxG method.
Fig. 2: Empirical power comparisons under different batch effect proportions.
Fig. 3: Empirical powers with varying sample sizes.
Fig. 4: Empirical power with misspecified prevalence rates, with true prevalence of 1%.
Fig. 5: GWAS results for type 2 diabetes.
Fig. 6: GWAS results for coronary atherosclerosis.

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Data availability

This work used genotype and phenotype data from the UK Biobank (http://www.ukbiobank.ac.uk/). Source data are provided with this paper. The source data are also available via Zenodo at https://doi.org/10.5281/zenodo.16314727 (ref. 53).

Code availability

The method WtCoxG is available in the R package GRAB (version 0.2.0) via GitHub at https://github.com/GeneticAnalysisinBiobanks/GRAB. This package is also available via Zenodo at https://doi.org/10.5281/zenodo.16316316 (ref. 54).

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Acknowledgements

This research was supported by Beijing Natural Science Foundation (grant no. F251049, W.B.) and National Natural Science Foundation of China (grant nos. 62273010 and 82441004, W.B.; 82441005, W.Y.). UK Biobank data were accessed under accession number 78795 (https://biobank.ndph.ox.ac.uk/crystal/app.cgi?id=78795). This research is supported by high-performance computing platform of Peking University. We acknowledge L. Miao’s significant contributions to the development of the R package GRAB.

Author information

Authors and Affiliations

  1. Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China

    Ying Li, Yuzhuo Ma, He Xu & Wenjian Bi

  2. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China

    Yaoyao Sun & Weihua Yue

  3. Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada

    Min Zhu

  4. PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

    Weihua Yue

  5. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Wei Zhou

  6. Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Wei Zhou

  7. Center for Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China

    Wenjian Bi

  8. Medicine Innovation Center for Fundamental Research on Major Immunology-Related Diseases, Peking University, Beijing, China

    Wenjian Bi

  9. Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, China

    Wenjian Bi

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Contributions

Y.L., W.Z. and W.B designed the experiments. Y.L. and W.B performed the experiments. Y.M. gave valuable suggestions on the retrospective idea. H.X. and W.Z. contributed to analysis using GATE. Y.S. and M.Z. contributed to the code programming. Y.L. and W.B. wrote the manuscript with the assistance of W.Y. and W.Z. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Yaoyao Sun, Wei Zhou or Wenjian Bi.

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Nature Computational Science thanks Tomas Fitzgerald and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Manhattan plot for Type 2 diabetes.

Manhattan plots of GWAS results for WtCoxG0k, SPACoxALL, SPACox, GATE and GLM, with the numbers on the top left in each subplot representing the total count of detected loci, significant level α = 5 × 10−8.

Source data

Extended Data Fig. 2 Manhattan plot for coronary atherosclerosis.

Manhattan plots of GWAS results for WtCoxG0k, SPACoxALL, SPACox, GATE and GLM, with the numbers on the top left in each subplot representing the total count of detected loci, significant level α = 5 × 10−8.

Source data

Supplementary information

Source data

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Li, Y., Ma, Y., Xu, H. et al. Applying weighted Cox regression to genome-wide association studies of time-to-event phenotypes. Nat Comput Sci 5, 1064–1079 (2025). https://doi.org/10.1038/s43588-025-00864-z

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