Emma Jingfei Zhang

Goizueta Business School, Emory University

Title: High-dimensional Gaussian graphical regression models with covariates
Date: Friday, October 25th, 2024
Time: 1:00PM (PDT)
Location: ASB 10900

Abstract: Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to flexibly link graph structures to external covariates. In this talk, we describe a new Gaussian graphical regression model that relates the conditional dependence structure to covariates, discrete and continuous, of high dimensions. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants, and clinical conditions modulate the subject-level gene co-expressions and recover both the population-level and subject-level gene co-expression networks. Under the proposed framework, we address problems including efficient computation with a simultaneous sparsity structure, and error rate and variable selection consistency quantification. Finally, the utility of our proposed method is demonstrated through an application to a co-expression QTL study with brain cancer patients.