Using Functional Annotation to Determine Bayes Factors in GWAS
Author Information
Author(s): Jo Knight, Michael R. Barnes, Gerome Breen, Michael E. Weale
Primary Institution: King's College London
Hypothesis
Can functional annotations help prioritize variants in genome-wide association studies (GWAS)?
Conclusion
The study found that GWAS hit SNPs are enriched for specific functional categories, suggesting that these annotations can improve Bayesian analysis of association results.
Supporting Evidence
- GWAS hit SNPs are enriched for non-synonymous SNPs, promoter SNPs, and cis eQTLs.
- The study suggests using Bayes Factors of about 4 for cis eQTL SNPs.
- The findings are consistent across different GWAS datasets.
Takeaway
This study shows that certain types of genetic markers are more likely to be important in diseases, and we can use this information to make better guesses about which markers to follow up on.
Methodology
The study analyzed two GWAS datasets and compared the proportions of functional annotations in GWAS hit SNPs versus GWAS panel SNPs.
Potential Biases
Potential bias due to the subjective weighting schemes used in previous studies.
Limitations
The study's definition of GWAS hits includes SNPs with higher p-values, which may increase false positives.
Participant Demographics
The study primarily involved Caucasian individuals based on the datasets used.
Statistical Information
P-Value
p<2.8×10−5
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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