Slope-Hunter: A robust method for index-event bias correction in genome-wide association studies of subsequent traits
Open accessAbstract
Background: Studying genetic associations with prognosis (e.g. survival, disability, subsequent disease events) is problematic due to selection bias - also termed index event bias or collider bias - whereby selection on disease status can induce associations between causes of incidence with prognosis. A current method for adjusting genetic associations for this bias assumes there is no genetic correlation between incidence and prognosis, which may not be a plausible assumption. Methods: We propose an alternative, the ‘Slope-Hunter’ approach, which is unbiased even when there is genetic correlation between incidence and prognosis. Our approach has two stages. First, we use cluster-based techniques to identify: variants affecting neither incidence nor prognosis (these should not suffer bias and only a random sub-sample of them are retained in the analysis); variants affecting prognosis only (excluded from the analysis). Second, we fit a cluster-based model to identify the class of variants only affecting incidence, and use this class to estimate the adjustment factor. Results: Simulation studies showed that the Slope-Hunter method reduces type-1 error by between 49%-85%, increases power by 1%-36%, reduces bias by 17%-47% compared to other methods in the presence of genetic correlation and performs as well as previous methods when there is no genetic correlation. Slope-Hunter and the previous methods perform less well as the proportion of variation in incidence explained by genetic variants affecting only incidence decreases. Conclusions: The key assumption of Slope-Hunter is that the contribution of the set of genetic variants affecting incidence only to the heritability of incidence is at least as large as the contribution of those affecting both incidence and prognosis. When this assumption holds, our approach is unbiased in the presence of genetic correlation between incidence and progression, and performs no worse than alternative approaches even when there is no correlation. Bias-adjusting methods should be used to carry out causal analyses when conditioning on incidence.
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Approaches such as estimating the causal effects of a biomarker at different points in life using multivariable MR may help provide evidence about timing, but this approach has not been used for drug targets and may not be possible owing to the limited repertoire of genetic instruments.
“Approaches such as estimating the causal effects of a biomarker at different points in life using multivariable MR may help provide evidence about timing (as has been conducted for BMI 91 ), however this approach has not been used for drug targets, and may not be possible owing to the limited repertoire of (particularly cis-acting) genetic instruments.”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [91]
The optimal timing for lower LDL-C is likely to be different to the optimal timing for BMI lowering interventions.
“the optimal timing for lower LDL-C is likely to be different to the optimal timing for BMI lowering interventions 91 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [91]
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- Authors
- Mahmoud, O., Dudbridge, F., Smith, G. D., Munafo, M., & Tilling, K.
- Affiliations
- University of Bristol (GB)
- University of Leicester (GB)
- Corresponding author
- Mahmoud, Osama
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2020-01-31
- OpenAlex
- W3004222339
- Cited by
- 15
- References
- 23
- Citations by year
- 2024: 1 · 2022: 5 · 2021: 8 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- collider bias, selection bias, genome-wide association study, prognosis, incidence, genetic correlation, bias correction, heritability, type I error, statistical power, causal inference
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