GENOMICS AND SYSTEMS BIOLOGY APPROACHES IN THE STUDY OF LIPID DISORDERS

Abstract

Cardiovascular disease (CVD) is a broad definition for diseases of the heart and blood vessels with high mortality and morbidity worldwide. Atherosclerosis and hypertension are the most common causes of CVD, and multiple factors confer the susceptibility. Some of the predisposing factors are modifiable such as diet, smoking, and exercise, whereas others, including age, sex, and individual's genetic variations contributing to the CVD composition traits, are non-modifiable. This latter group includes serum lipid traits. High serum lipid levels, specifically high levels of serum low-density lipoprotein cholesterol and triglycerides, are well-established key risk factors of atherosclerosis. This review will discuss genomics and systems biology approaches in the study of common dyslipidemias. The non-Mendelian forms of dyslipidemias are highly complex, and the molecular mechanisms underlying these polygenic lipid disorders are estimated to involve hundreds of genes. Interactions between the different genes and environmental factors also contribute to the clinical outcomes; however, very little is known about these interactions and their molecular mechanisms. To better address the complex genetic architecture and multiple properties leading to high serum lipid levels, networks and systems approach combining information at genomic, transcriptomics, methylomics, proteomics, metabolomics, and phenome level are being developed, with the ultimate goal to elucidate the cascade of dynamic changes leading to CVD in humans. (REV INVEST CLIN. 2018;70:217-23).

Keywords: Allele specific expression; Cardiovascular disease; Coronary heart disease; Epigenomics; Genome-wide association study; High-density lipoprotein cholesterol; Low-density lipoprotein cholesterol; Metabolomics; Network; Personal medicine; Protein-protein interaction; Quantitative trait locus; Single nucleotide polymorphism; Systems genetics; Transcriptomics; Triglycerides; Type 2 diabetes; Weighted gene co-expression network analysis.