ArticlePLoS genetics2025
Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures.
Article in PLoS genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Locus-specific stratification and prioritization unveil genetic risk mechanism underlying complex diseases.Nature communications · 2026Article
- Comparative fine-mapping of breast cancer susceptibility loci using summary statistics methods and multinomial regression.medRxiv : the preprint server for health sciences · 2026Article
- Genome-wide fine-mapping improves identification of causal variants.Nature genetics · 2026Article
- mLeveraging genetic correlations to prioritize drug groups for repurposing in type 2 diabetes.The pharmacogenomics journal · 2025Article
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8 authors.
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Abstract
We evaluated Bayesian Linear Regression (BLR) models with BayesC and BayesR priors as statistical genetic fine-mapping tools, comparing their performance to established methods such as FINEMAP and SuSiE. Through extensive simulations and analyses of UK Biobank (UKB) phenotypes, we assessed F1 classification scores and predictive accuracy across models. Simulations encompassed diverse genetic architectures varying in polygenicity, heritability, causal SNP proportions, and disease prevalence. In the empirical analyses, we used over 6.6 million imputed SNPs and phenotypic data from more than 335,000 UKB participants. Our results show that BLR models, particularly those using the BayesR prior, consistently achieved higher F1 scores than the external methods, but having comparable predictive accuracy. Applying the BLR model at the region-wide level generally yielded better F1 scores than the genome-wide approach, except for traits with high polygenicity. These findings highlight BLR models as accurate and robust tools for statistical fine mapping in both simulated and empirical genetic datasets.
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