Modeling the causal mechanism between genotypes and phenotypes using large-scale biobank data and context-specific regulatory networks

The relationship between genetic variation and human phenotypes is crucial for developing effective treatments and personalized medicine. However, our understanding of the regulatory mechanisms by which variants influence human traits and diseases is far from complete. Context-specific regulatory network is a typical tool that provides detailed understanding of gene regulation in specific biological contexts, allowing us to identify key regulators and pathways that are important for a particular phenotype. In this review, we summarize the large international biobanks and reference omics data that provide diverse datasets for the genotype-phenotype analysis and the construction of context-specific regulatory networks, and discuss the importance of context-specific regulatory networks in explaining the underlying causal mechanism between genotypes and phenotypes. We emphasize the significance of quantitative trait locus (QTL) studies in explaining the correlation between genotypes and omics features, and present various computational approaches for the construction of context-specific regulatory networks. With continued advancements in biobanking, genomics, and computational biology, the context-specific regulatory networks may serve as an increasingly powerful tool for modeling the causal mechanisms that underlie the relationship between genotypes and phenotypes.