LAUSR

Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) mapping studies for exploring the gene regulatory mechanisms underlying diseases. Existing TWAS methods primarily focus on one gene at a time. However, complex diseases are seldom resulted from the abnormality of a single gene, but from the biological network involving multiple genes. In addition, binary or ordinal categorical phenotypes are commonly encountered in biomedicine. We develop a proportional odds logistic model for network regression in TWAS, PoLoNet, to detect the association between a network and binary or ordinal categorical phenotype. PoLoNet relies on two-stage TWAS framework. It first adopts the distribution-robust nonparametric dirichlet process regression model in eQTL study to obtain the SNP effect estimate on each gene within the network. Then, PoLoNet uses pointwise mutual information to represent the general relationship among the network nodes of predicted gene expression in GWAS, followed by the association analysis with all nodes and edges involved in proportional odds logistic model. A key feature of PoLoNet is its ability to simultaneously identify the disease-related network nodes or edges. With extensive realistic simulations including those under various between-node correlation patterns, we show PoLoNet can provide calibrated type I error control and yield higher power than other existing methods. We finally apply PoLoNet to analyze bipolar and major depression status and blood pressure from UK Biobank to illustrate its benefits in real data analysis.