Identification of Genetic Factors in Atherosclerosis Using an Apoe Mouse Model

Author: ORCID icon
Grainger, Andrew, Biochemistry and Molecular Genetics - School of Medicine, University of Virginia
Shi, Weibin, MD-RADL Rad Research, University of Virginia

Atherosclerosis is the primary cause of coronary artery disease (CAD), ischemic stroke and peripheral arterial disease. Despite major achievements made in the past few decades, CAD and atherosclerosis-related events remain the number one cause of death in the United States and other developed countries. Therefore, there is a critical medical need to develop novel and effective therapies.

An effective way to find new targets for intervention is through conducting genetic studies in animal models. When deficient in Apoe, mouse strains BALB/cJ and SM/J exhibit distinct differences in atherosclerosis and its associated risk factors. We hypothesized that linkage analysis of progeny derived from these inbred strains would lead to the discovery of new genes and new pathways in atherosclerosis and its associated cardiometabolic phenotypes. F2 mice were generated from an intercross between the two Apoe-/- strains and fed 12 weeks of Western diet. Many QTL loci were mapped for plasma lipids and glucose, carotid lesion size, and aortic lesion size. This included a significant QTL for aortic atherosclerosis, Ath49, which was mapped to the major histocompatibility region. Moreover four novel QTLs for carotid atherosclerosis, two significant QTLs named Cath7 on chromosome 5 and Cath8 on chromosome 9 and two suggestive QTLs, Cath5 and Cath6 on chromosomes 15 and 18 respectively, were mapped. Through a combination of haplotype analysis and a novel strategy employing gene expression, aortic lesion size correlation, and eQTL data, we prioritized Mep1α as a promising candidate gene for Ath49. We generated double knockouts and found that Mep1α is a novel gene negatively affecting plaque formation. Finally, we developed a method utilizing machine learning-based segmentation to accurately quantify subcutaneous and visceral fat volumes in mice using MRI and humans using CT. We found that BMI, a commonly used measure for diagnosing obesity, is only moderately associated with subcutaneous fat and has no association with visceral fat volume in humans.

PHD (Doctor of Philosophy)
Atherosclerosis, Genetics, Quantitative Trait Loci, Mouse, Bioinformatics, Abdominal Fat, Machine Learning
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