Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis
纵向数据的超高维分位数回归：血压分析应用AI 解读 ↓
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纵向数据的超高维分位数回归：血压分析应用
- 研究问题
- 如何在重复血压测量中同时筛选大量遗传变异与时变表型变量，并识别高条件分位数及不同分位数的风险因素差异？
- 方法与思路
- 作者提出分位数惩罚广义估计方程，将超高维遗传变量、纵向测量及时变协变量纳入联合建模与变量选择。
- 材料陈述的贡献
- 摘要称，估计量具有一致性、渐近正态性和预言性质，高维BIC具有选择一致性，并报告不同血压分位数的关联差异。
- 阅读关注点
- 应核对纵向相关性的处理、条件分位数定义、维度增长与惩罚条件，以及高维BIC规则和遗传变量筛选的验证设计。
- 阅读边界
- 仅据版本未注明的OpenAlex索引摘要，不能推断刊本变化；具体理论条件与新遗传关联的验证未说明，分位数关联不等于因果风险效应。
原文摘要
Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphism (SNP) at a time. We aim to select among over half a million SNPs along with time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. Taking advantage of rich data from a large-scale public health study, we develop and apply a novel quantile penalized generalized estimating equations (GEE) approach, incorporating several key aspects including ultra-high dimensional genetic SNPs, the longitudinal nature of blood pressure measurements, time-varying covariates, and conditional high quantiles of blood pressure. Importantly, we identify interesting new SNPs for high blood pressure. Besides, we find blood pressure levels are likely heterogeneous, where the important risk factors identified differ among quantiles. This comprehensive picture of conditional quantiles of blood pressure can allow more insights and targeted treatments. We provide an efficient computational algorithm and prove consistency, asymptotic normality, and the oracle property for the quantile penalized GEE estimators with ultra-high dimensional predictors. Moreover, we establish model-selection consistency for high-dimensional BIC. Simulation studies show the promise of the proposed approach. Supplementary materials for this article are available online.