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.

中文翻译：

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整合小鼠和人类遗传数据以超越 GWAS 并确定胆固醇代谢中的因果基因。

识别将遗传变异与表型联系起来的因果基因是全基因组关联研究 (GWAS) 中的一个具有挑战性的问题。在这里，我们开发了一种系统方法，将小鼠肝脏共表达网络与人类脂质 GWAS 数据相结合，以确定胆固醇和脂质代谢的调节剂。通过我们的方法，我们确定了 48 个在小鼠中显示复制并与人类血浆脂质特征相关的基因和 X 染色体上的六个基因。在这 54 个基因中，25 个在脂质代谢中没有先前确定的作用。基于功能研究和与其他人类脂质 GWAS 数据集的整合，我们将 Sestrin1 确定为与人类血浆胆固醇水平相关的因果基因。我们的验证研究表明，Sestrin1 在多种小鼠模型中影响血浆胆固醇并调节胆固醇生物合成。我们的结果强调了结合小鼠和人类数据集对人类脂质 GWAS 基因座进行优先排序和发现脂质基因的力量。