Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism

Identifying the causal gene(s) that connects genetic variation to a phenotype is a challenging problem in genome-wide association studies (GWASs). Here, we develop a systematic approach that integrates mouse liver co-expression networks with human lipid GWAS data to identify regulators of cholesterol and lipid metabolism. Through our approach, we identified 48 genes showing replication in mice and associated with plasma lipid traits in humans and six genes on the X chromosome. Among these 54 genes, 25 have no previously identified role in lipid metabolism. Based on functional studies and integration with additional human lipid GWAS datasets, we pinpoint Sestrin1 as a causal gene associated with plasma cholesterol levels in humans. Our validation studies demonstrate that Sestrin1 influences plasma cholesterol in multiple mouse models and regulates cholesterol biosynthesis. Our results highlight the power of combining mouse and human datasets for prioritization of human lipid GWAS loci and discovery of lipid genes. [Display omitted] •Systematic method to combine mouse liver network and human lipid GWAS for discovery•Identification of a conserved liver cholesterol module across mouse populations•Prioritization of genes replicated in mouse and associated with human lipid traits•Validation of Sestrin1 as a gene that regulates cholesterol biosynthesis Here, Li et al. develop a systematic approach that integrates mouse liver co-expression networks together with human lipid GWAS datasets to identify lipid metabolism genes. Using this approach, they pinpoint Sestrin1 as a gene associated with cholesterol levels in humans and demonstrate that Sestrin1 protein can regulate cholesterol biosynthesis.