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Formal Statistical Replication Analysis in Lung Cancer Genome-Wide Association Studies
Yung-Han Chang1, Jinyoung Byun2,3,4, Bryan R Gorman5
1Department of Biostatistics, University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
Medrxiv : the Preprint Server for Health Sciences
|November 19, 2025
Summary
Statistical model-based replication analysis significantly reduces false positives in genome-wide association studies (GWAS) for lung cancer. This approach identifies key single nucleotide polymorphisms (SNPs) more efficiently, improving polygenic risk score development.
Area of Science:
- Genetics
- Cancer Research
- Statistical Genomics
Background:
- Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) linked to lung cancer risk.
- Translating GWAS findings into clinical applications is hindered by high false positive rates (Type I errors).
- Existing methods like p-value thresholds and meta-analyses offer limited reduction of spurious associations.
Purpose of the Study:
- To introduce and validate a statistical model-based replication analysis for curating high-quality significant SNPs from GWAS.
- To compare the efficacy of model-based replication against traditional meta-analysis in reducing false discoveries.
- To assess the performance of polygenic risk scores (PRSs) derived from replication-based SNPs.
Main Methods:
- Developed a formal statistical test for the replication composite null hypothesis, ensuring consistent SNP effect direction across cohorts.
- Conducted two-way and three-way simulations to evaluate the false discovery rate (FDR) compared to meta-analysis.
- Replicated SNPs from the International Lung Cancer Consortium GWAS for squamous cell lung cancer and lung adenocarcinoma.
- Constructed polygenic risk scores (PRSs) using both replication-based and GWAS-significant SNPs.
Main Results:
- Model-based replication analysis demonstrated a substantially lower false discovery rate (FDR) than meta-analysis (6.4 times lower in two-way simulations).
- In three-way replication, 9.8% of GWAS-significant SNPs were validated for squamous cell lung cancer and 33.8% for lung adenocarcinoma.
- Replication-based PRSs performed comparably to GWAS-significant PRSs but utilized 87.3% fewer variants.
Conclusions:
- Formal model-based replication analysis effectively reduces spurious findings from GWAS.
- This method enhances the robustness and efficiency of translating genetic discoveries into clinical insights.
- Model-based replication facilitates the development of more efficient and accurate polygenic risk scores for lung cancer.
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