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

Authors

Zhonggang Li
James A Votava
Gregory J M Zajac
Jenny N Nguyen
Fernanda B Leyva Jaimes
Sophia M Ly
Jacqueline A Brinkman
Marco De Giorgi
Sushma Kaul
Cara L Green
Samantha L St Clair
Sabrina L Belisle
Julia M Rios
David W Nelson
Mary G Sorci-Thomas
William R Lagor
Dudley W Lamming
Chi-Liang Eric Yen
Brian W Parks

Publication Date

4-7-2020

Journal

Cell Metabolism

DOI

10.1016/j.cmet.2020.02.015

PMID

32197071

PMCID

PMC7184639

PubMedCentral® Posted Date

4-7-2021

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

Animals, Cholesterol, Databases, Genetic, Genome-Wide Association Study, Heat-Shock Proteins, Humans, Mice

Abstract

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.

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