An application of the MR-Horse method to reduce selection bias in genome-wide association studies of disease progression.
Abstract: Genome-wide association studies (GWAS) of disease progression are vulnerable to collider bias caused by selection of participants with disease at study entry. This bias introduces spurious associations between disease progression and genetic variants that are truly only associated with disease incidence. Methods of statistical adjustment to reduce this bias have been published, but rely on assumptions regarding the genetic correlation of disease incidence and disease progression which are likely to be violated in many human diseases. MR-Horse is a recently published Bayesian method to estimate the parameters of a general model of genetic pleiotropy in the setting of Mendelian Randomisation. We adapted this method to provide bias-reduced GWAS estimates of associations with disease progression, robust to the genetic correlation of disease incidence and disease progression and robust to the presence of pleiotropic variants with effects on both incidence and progression. We applied this adapted method to simulated GWAS of disease incidence and progression with pleiotropic variants and varying degrees of genetic correlation. When significant genetic correlation was present, the MR-Horse method produced less biased estimates than unadjusted analyses or analyses adjusted using other existing methods. Type 1 error rates with the MR-Horse method were consistently below the nominal 5% level, at the expense of a modest reduction in power. We then applied this method to summary statistics from the CKDGen consortium GWAS of kidney function decline. MR-Horse attenuated the effects of variants with known likely biased effects in the CKDGen GWAS, whilst preserving effects at loci with likely true effects.
© 2025. The Author(s).
Publication Date: 2025-06-03 PubMed ID: 40461637PubMed Central: 5556366DOI: 10.1038/s41431-025-01845-6Google Scholar: Lookup
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Summary
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The research article presents an adaptation of the MR-Horse method aimed at reducing selection bias in genome-wide disease progression studies. This improved procedure provides less biased estimates of disease progression, especially in cases where genetic correlation is significant.
Introduction to the Problem
- In genome-wide association studies (GWAS) of disease progression, there is a known vulnerability to collider bias, which is a type of selection bias that occurs when subjects with the disease under study are specifically selected.
- This bias leads to false associations between genetic variants and disease progression when these variants are only truly associated with disease incidence.
- Previously, methods used to reduce this bias were based on assumptions regarding the genetic correlation of disease incidence and disease progression, however, these assumptions often do not hold true for numerous human diseases.
Introduction of the MR-Horse Method
- The MR-Horse method is a Bayesian approach aimed at estimating the parameters of a general model of genetic pleiotropy (where a single gene impacts two or more traits independently) in Mendelian Randomisation settings.
- Researchers adapted the MR-Horse method to offer GWAS estimates of associations with disease progression, which are robust against genetic correlation of disease incidence and disease progression.
- This method is also robust in the presence of pleiotropic variants that have an impact on both disease incidence and progression.
Application of the MR-Horse Method and Results
- The researchers applied the adapted MR-Horse method to simulated GWAS with pleiotropic variants and varying degrees of genetic correlation.
- In situations where a significant genetic correlation existed, the MR-Horse method provided less biased estimates compared to analyses that were neither adjusted nor adjusted using other existing methods.
- The MR-Horse method produced Type 1 error rates below the nominal 5%, although this was at the expense of a slight reduction in power.
Application to Real-World Data
- The method was then applied to real-world data using summary statistics from the CKDGen consortium GWAS of kidney function decline.
- The MR-Horse method was successful in reducing the effects of variants believed to have biased effects in the CKDGen GWAS while preserving the effects at locations with likely true effects.
Cite This Article
APA
Donovan K, Torres J, Zhu D, Herrington WG, Staplin N.
(2025).
An application of the MR-Horse method to reduce selection bias in genome-wide association studies of disease progression.
Eur J Hum Genet.
https://doi.org/10.1038/s41431-025-01845-6 Publication
Researcher Affiliations
- Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK. killian.donovan@ndph.ox.ac.uk.
- Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
- Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
- Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
- Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Grant Funding
- TF_001_20220708 / Kidney Research UK
Conflict of Interest Statement
Competing interests: The authors declare no competing interests. Ethical approval: No ethical approval was required for this work as we used only simulated data or publicly available summary statistics.
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