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

机译:将鼠标和人类遗传数据集成到GWA之外,识别胆固醇代谢中的因果基因

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摘要

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.
机译:鉴定将遗传变异与表型连接到表型的因果基因是基因组 - 宽协会研究(GWASS)中有挑战性问题。在这里,我们开发一种系统的方法,其将小鼠肝共表达网络与人脂质GWAS数据集成,以识别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个基因,显示小鼠的复制,与人类的血浆脂质特征和六个基因相关的X染色体。在这些54个基因中,25例未在脂质代谢中鉴定出在脂质代谢中的作用。基于功能研究和与额外的人脂质GWAS数据集的整合,我们将Sestrin1定位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影响多种小鼠模型中的血浆胆固醇并调节胆固醇生物合成。我们的结果突出了组合鼠标和人类数据集的力量,以便为人类脂质GWAS基因座的优先排序和脂质基因的发现。

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