Shrestha Merina, Bai Zhonghao, Gholipourshahraki Tahereh, Hjelholt Astrid J, Hu Sile, Kjolby Mads, Rohde Palle Duun, Sørensen Peter
Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
Department of Biomedicine, Aarhus University, Aarhus, Denmark.
PLoS Genet. 2025 Jul 30;21(7):e1011783. doi: 10.1371/journal.pgen.1011783. eCollection 2025 Jul.
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.
我们评估了采用BayesC和BayesR先验的贝叶斯线性回归（BLR）模型作为统计遗传精细定位工具，并将它们的性能与FINEMAP和SuSiE等既定方法进行比较。通过对英国生物银行（UKB）表型进行广泛的模拟和分析，我们评估了各模型的F1分类分数和预测准确性。模拟涵盖了在多基因性、遗传力、因果单核苷酸多态性（SNP）比例和疾病患病率方面各不相同的多种遗传结构。在实证分析中，我们使用了来自超过33.5万名UKB参与者的660多万个估算SNP和表型数据。我们的结果表明，BLR模型，尤其是那些使用BayesR先验的模型，始终比外部方法获得更高的F1分数，但预测准确性相当。在全区域水平应用BLR模型通常比全基因组方法产生更好的F1分数，但具有高多基因性的性状除外。这些发现突出了BLR模型作为模拟和实证遗传数据集中统计精细定位的准确且稳健的工具。
