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Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR
Anna Hutchinson1, Guillermo Reales2,3, Thomas Willis1
1MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
Plos Genetics
|October 20, 2021
Summary
A new Flexible conditional false discovery rate (cFDR) method enhances genome-wide association studies (GWAS) power by using auxiliary data. This approach increases genetic discovery for complex traits while controlling false discoveries.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) identify genetic variants for complex traits but require stringent thresholds.
- Leveraging auxiliary data like related traits or functional genomics can increase GWAS statistical power.
- Existing conditional false discovery rate (cFDR) methods have limitations on the types of auxiliary data they can use.
Purpose of the Study:
- To develop a more flexible cFDR method (Flexible cFDR) that accommodates arbitrary continuous distributions for auxiliary covariates.
- To enable iterative application of Flexible cFDR for multi-dimensional covariate data.
- To improve statistical power and enhance genetic discovery in GWAS.
Main Methods:
- Developed Flexible cFDR, relaxing parametric distribution assumptions for auxiliary covariates.
- Implemented iterative application of Flexible cFDR for multi-dimensional data.
- Validated the method through simulations and application to an asthma GWAS using functional genomic data.
Main Results:
- Flexible cFDR demonstrated increased sensitivity while maintaining false discovery rate (FDR) control in simulations.
- The method successfully identified additional genetic associations for asthma.
- These novel associations were validated in an independent UK Biobank dataset.
Conclusions:
- Flexible cFDR offers a powerful and adaptable approach to boost GWAS discovery by effectively utilizing diverse auxiliary data.
- This method advances the field of statistical genetics by overcoming limitations of previous cFDR implementations.
- The findings highlight the potential of Flexible cFDR for uncovering novel genetic associations in complex traits.
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