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Published on: June 23, 2012
Quantifying the underestimation of relative risks from genome-wide association studies.
Chris Spencer1, Eliana Hechter, Damjan Vukcevic
1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. chris.spencer@well.ox.ac.uk
Plos Genetics
|March 26, 2011
Summary
Genome-wide association studies (GWAS) may underestimate the true genetic risk of common diseases. Fine mapping causal variants reveals potentially larger effect sizes, impacting disease risk predictions and heritability estimates.
Area of Science:
- Genetics
- Genomics
- Disease Risk Prediction
Background:
- Genome-wide association studies (GWAS) identify numerous disease-associated loci.
- Most GWAS risk variants are likely tags for unknown causal variants.
- Current effect size estimates may not reflect the true risk at causal variants.
Purpose of the Study:
- To assess the potential underestimation of relative risks (RR) for causal variants identified through GWAS.
- To investigate the impact of underestimation on disease risk prediction.
- To inform the design of fine mapping experiments.
Main Methods:
- Statistical modeling under plausible assumptions.
- Analysis of relative risks (RR) and minor allele frequency (MAF).
- Simulation of genetic risk under varying variant effects.
Main Results:
- A significant proportion of GWAS-estimated RRs may underestimate the true risk at causal variants.
- For an estimated RR of 1.2-1.3, there's a 38% chance the true RR exceeds 1.4.
- Probabilities of underestimation vary with low-frequency variant effects and SNP MAF.
Conclusions:
- Fine mapping may uncover causal variants with larger effect sizes than currently estimated.
- Underestimation of effect sizes impacts heritability explanations and disease risk predictions.
- Results provide insights for optimizing fine mapping study designs.
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