Fairness-Aware Gaussian Graphical Regression Models with Application to Brain Co-Expression QTL Studies

• 成果类型: Article; Early Access
• 署名作者: Zhou, Xingcai; Xu, Zinan; Jiang, Bei; Kong, Linglong
• 署名单位: Nanjing Audit University; University of Alberta
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2688572
• 发表日期: 2026-07-31
• 关键词: Co-expression quantitative trait locus; Debiased Inference; Fair Multi-objective optimization; fairness; Gaussian graphical models; subtypes

摘要

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with largely ineffective treatment. It is imperative to explore more effective therapies, such as gene-based treatments. For co-expression QTL studies of GBM, we develop fairness-aware Gaussian graphical regression models (Fair RegGGMs), which can determine how genetic variants modulate subject-level gene networks, and recover both population-level and subject-level gene graphs, while ensuring that the developed learning and inference neither propagate biases nor amplify disparities. We introduce pairwise graph disparity risk to quantify fairness and propose fairness-aware multi-task learning (Fair-MTL) via a cross-task group sparsity penalty, within-task element-wise sparsity penalty, and pairwise fairness regularization. We also develop a projected Fair-SAGE debiasing method for statistical inference. In Fair-MTL, we strive for a balance among group-/element-wise sparsity of graphical network structures, fairness across different subgroups, and statistical effectiveness of RegGGMs. For the ultrahigh-dimensional overparameterized models, an efficient nonsmooth fair multi-objective optimization algorithm (Fair-MOO) is developed. Both Fair-MOO and the projected Fair-SAGE dramatically reduce computational costs. Under a general dependence structure, the nonasymptotic l2 convergence rate of Fair-MTL, asymptotic normality of Fair-SAGE debiased estimator, and Pareto optimality of Fair-MOO algorithm are established. The simulation study and brain co-expression QTL analysis confirm the fairness and effectiveness of our Fair RegGGMs and provide valuable insights for the gene graph of GBM. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.