Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome - Qiaoxing Liang; Guo‐Wang Lin; Xiaohu Ding; Bin Zou; Xiaomin Liu; Jing Li; Yuxin Zhang; Xiaofeng Wen; Lingyi Liang; Jin‐Xin Bei; Mingguang He; Huijue Jia; et al. (iMetaOmics)
Abstract Gene expression is a molecular phenotype shaped by the interplay between genotype and environment. The microbiome represents a critical environmental exposure for the host. However, the genotype‐microbiome interactions (GMIs) shaping the host transcriptome remain largely unexplored. Here, we integrated conjunctival multiomics data from 120 pairs of twins to investigate GMIs. We identified 272,972 expression quantitative trait loci associated with 5946 genes and 241,073 genotype‐controlled correlations between gene expression and microbial abundance. We developed a modeling approach, MicroGenix, that screens for GMIs from host genome, transcriptome, and microbiome data and identifies GMIs associated with the disease through gene‐based association tests. We applied MicroGenix to the conjunctival data set and found that incorporating GMIs into gene expression prediction models significantly increased prediction accuracy. The genes with increased accuracy were overrepresented by those encoding cell adhesion molecules. We further used MicroGenix to predict the transcriptome from conjunctival metagenomes and identified GMIs associated with ocular surface disease. Our work provides a resource for studying host‐microbiome interactions at the conjunctiva and a computational approach to investigate GMIs using multiomics data.