A New Method for Correcting Genetic Association Studies
Author Information
Author(s): Yan Ting, Hou Bo, Yang Yaning
Primary Institution: University of Science and Technology of China
Hypothesis
Can a regression-based genomic control method effectively correct for cryptic relatedness in genetic association studies?
Conclusion
The proposed regression-based genomic control method effectively controls type I error in the presence of population substructure.
Supporting Evidence
- The regression-based method allows for using arbitrary null markers in the genomic control correction procedure.
- Simulation results indicate that the RGC method can effectively correct for variance inflation caused by cryptic relatedness.
- The method is robust to the inclusion of linked loci in the selection of null markers.
Takeaway
This study introduces a new way to fix problems in genetic studies caused by hidden relationships between people, making the results more reliable.
Methodology
The study proposes a regression-based genomic control method that adjusts allele frequency variability of null markers.
Potential Biases
Potential inclusion of linked loci as null markers could affect results.
Limitations
The method may be slightly conservative for recessive trend tests and anti-conservative for dominant trend tests when minor allele frequency is close to 0.
Digital Object Identifier (DOI)
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