缺失响应数据下的多元回归在区域DNA甲基化数量性状位点（mQTLs）建模中的应用
Multivariate regression with missing response data for modelling regional DNA methylation QTLs
AI总结：
针对多元模型识别DNA甲基化遗传调控因子（mQTLs）时亚硫酸氢盐测序缺失数据的问题，提出missoNet凸估计框架，避免插补偏差，联合估计回归系数与精度矩阵，理论和实验均证明其在预测、稀疏性恢复及真实mQTL分析中优于现有方法。
摘要：
利用多元模型识别DNA甲基化的遗传调控因子（mQTLs）可提高统计效力，但受限于亚硫酸氢盐测序产生的缺失数据。基于插补的标准方法可能引入偏差，限制可靠推断。我们提出missoNet，这是一种新颖的凸估计框架，可从缺失响应的数据中联合估计回归系数和精度矩阵。通过使用无偏替代估计器，我们的三阶段程序避免了插补，同时执行变量选择并学习响应间的条件依赖结构。我们建立了理论误差界，模拟表明missoNet在预测和稀疏性恢复方面持续优于现有方法。在CARTaGENE队列的真实mQTL分析中，missoNet在保留验证集上实现了更优的预测准确性和假发现控制，识别出已知和可信的新遗传关联。该方法为基因组分析提供了稳健、高效且有理论基础的工具，并以R包形式提供。
英文摘要：
Identifying genetic regulators of DNA methylation (mQTLs) with multivariate models enhances statistical power, but is challenged by missing data from bisulfite sequencing. Standard imputation-based methods can introduce bias, limiting reliable inference. We propose \texttt{missoNet}, a novel convex estimation framework that jointly estimates regression coefficients and the precision matrix from data with missing responses. By using unbiased surrogate estimators, our three-stage procedure avoids imputation while simultaneously performing variable selection and learning the conditional dependence structure among responses. We establish theoretical error bounds, and our simulations demonstrate that \texttt{missoNet} consistently outperforms existing methods in both prediction and sparsity recovery. In a real-world mQTL analysis of the CARTaGENE cohort, \texttt{missoNet} achieved superior predictive accuracy and false-discovery control on a held-out validation set, identifying known and credible novel genetic associations. The method offers a robust, efficient, and theoretically grounded tool for genomic analyses, and is available as an R package.
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