CoNet: Efficient Network Regression for Survival Analysis in Transcriptome-Wide Association Studies-With Applications to Studies of Breast Cancer

• Published in: Genes Vol. 14; no. 3; p. 586
• Main Authors: Han, Jiayi, Zhang, Liye, Yan, Ran, Ju, Tao, Jin, Xiuyuan, Wang, Shukang, Yuan, Zhongshang, Ji, Jiadong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.02.2023; MDPI
• Subjects: Biobanks; Breast cancer; Breast Neoplasms - genetics; Computer Simulation; Disease; Edge effect; Female; Gene expression; Gene mapping; Genetic aspects; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genomics; Genotype & phenotype; Health aspects; Humans; Hypotheses; Hypothesis testing; Methods; Performance evaluation; Quantitative trait loci; Regression analysis; RNA; Survival; Survival Analysis; Transcriptome - genetics; Transcriptomes; China; United Kingdom > UK; biological network; breast cancer; TWAS; survival analysis
• ISSN: 2073-4425; 2073-4425
• DOI: 10.3390/genes14030586

Transcriptome-wide association studies (TWASs) aim to detect associations between genetically predicted gene expression and complex diseases or traits through integrating genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) mapping studies. Most current TWAS methods analyze one gene at a time, ignoring the correlations between multiple genes. Few of the existing TWAS methods focus on survival outcomes. Here, we propose a novel method, namely a COx proportional hazards model for NEtwork regression in TWAS (CoNet), that is applicable for identifying the association between one given network and the survival time. CoNet considers the general relationship among the predicted gene expression as edges of the network and quantifies it through pointwise mutual information (PMI), which is under a two-stage TWAS. Extensive simulation studies illustrate that CoNet can not only achieve type I error calibration control in testing both the node effect and edge effect, but it can also gain more power compared with currently available methods. In addition, it demonstrates superior performance in real data application, namely utilizing the breast cancer survival data of UK Biobank. CoNet effectively accounts for network structure and can simultaneously identify the potential effecting nodes and edges that are related to survival outcomes in TWAS.