Yaoyu Chen, Jarod Rollins, Beverly Paigen and Xiaosong Wang
Atherosclerosis is a complex disease involving genetic and environmental risk factors, acting on their own or in synergy. Within the general population, polymorphisms within genes in lipid metabolism, inflammation, and thrombogenesis are probably responsible for the wide range of susceptibility to myocardial infarction, a fatal consequence of atherosclerosis. Genetic linkage studies have been carried out in both humans and mouse models to identify these polymorphisms. Approximately 40 quantitative trait loci for atherosclerotic disease have been found in humans, and approximately 30 in mice. Recently, genome-wide association studies have been used to identify atherosclerosis-susceptibility polymorphisms. Although discovering new atherosclerosis genes through these approaches remains challenging, the pace at which these polymorphisms are being found is accelerating due to rapidly improving bioinformatics resources and biotechnologies. The outcome of these efforts will not only unveil the molecular basis of atherosclerosis but also facilitate the discovery of drug targets and individualized medication against the disease.
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Trends in Genetics, Volume 21, Issue 12, 1 December 2005, Pages 683-692
Keith DiPetrillo, Xiaosong Wang, Ioannis M. Stylianou and Beverly Paigen
Quantitative trait locus (QTL) analysis is a powerful method for localizing disease genes, but identifying the causal gene remains difficult. Rodent models of disease facilitate QTL gene identification, and causal genes underlying rodent QTL are often associated with the corresponding human diseases. Recently developed bioinformatics methods, including comparative genomics, combined cross analysis, interval-specific and genome-wide haplotype analysis, followed by sequence and expression analysis, each facilitated by public databases, provide new tools for narrowing rodent QTLs. Here we discuss each tool, illustrate its application and generate a bioinformatics strategy for narrowing QTLs. Combining these bioinformatics tools with classical experimental methods should accelerate QTL gene identification.
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Trends in Genetics, Volume 28, Issue 6, 1 June 2012, Pages 267-275
Aldons J. Lusis
Genome-wide association studies (GWAS) from the past several years have provided the first unbiased evidence of the genes contributing to common cardiovascular disease traits in European and some Asian populations. The results not only confirmed the importance of prior knowledge, such as the central role of lipoproteins, but also revealed that there is still much to learn about the underlying mechanisms of this disease, as most of the associated genes do not appear to be involved in pathways previously connected to atherosclerosis. In this review, I focus on the common forms of the disease and look at both human and animal model studies. I summarize what was known before GWAS, highlight how the field has been changed by GWAS, and discuss future considerations, such as the limitations of GWAS and strategies that may lead to a more complete, mechanistic understanding of atherosclerosis.