Title: Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior Show Chinese title

Title: Cox比例风险模型中具有“单效应和”先验的贝叶斯变量选择

Abstract: Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is designed to deal with the specific challenges that arise in genetic fine-mapping, including: the presence of very strong correlations among the covariates, often exceeding 0.99; very large data sets containing potentially thousands of covariates and hundreds of thousands of samples. We accomplish this by extending the "Sum of Single Effects" (SuSiE) method to the Cox proportional hazards (CoxPH) model. We demonstrate the benefits of the new method, "CoxPH-SuSiE", over existing BVSR methods for TTE outcomes in simulated fine-mapping data sets. We also illustrate CoxPH-SuSiE on real data by fine-mapping asthma loci using data from UK Biobank. This fine-mapping identified 14 asthma risk SNPs in 8 asthma risk loci, among which 6 had strong evidence for being causal (posterior inclusion probability greater than 50%). Two of the 6 putatively causal variants are known to be pathogenic, and others lie within a genomic sequence that is known to regulate the expression of GATA3. Show Chinese abstract

Abstract: 受基因精细映射应用的启发，我们引入了一种处理贝叶斯变量选择回归（BVSR）针对生存时间（TTE）结局的新方法。 此新方法旨在应对遗传精细映射中出现的具体挑战，包括：协变量之间存在极强的相关性，通常超过0.99；数据集非常庞大，可能包含数千个协变量和数十万个样本。 我们通过将“单效应求和”（SuSiE）方法扩展到Cox比例风险（CoxPH）模型来实现这一点。 我们在模拟的精细映射数据集中展示了新方法“CoxPH-SuSiE”相较于现有TTE结局BVSR方法的优势。 我们还通过使用UK Biobank的数据对哮喘位点进行精细映射，展示了CoxPH-SuSiE在真实数据中的应用。 这种精细映射确定了8个哮喘风险位点中的14个哮喘风险SNP，其中6个有强有力的因果证据（后验纳入概率大于50%）。 这6个潜在因果变异中有2个已知具有致病性，其余则位于已知调控GATA3表达的基因组序列内。