|Marian, A -|
|Belmont, John -|
Submitted to: Circulation Research
Publication Type: Review Article
Publication Acceptance Date: March 31, 2011
Publication Date: May 1, 2011
Citation: Marian, A.J., Belmont, J. 2011. Strategic approaches to unraveling genetic causes of cardiovascular diseases. Circulation Research. 108(10):1252-1269. Technical Abstract: DNA sequence variants are major components of the "causal field" for virtually all medical phenotypes, whether single gene familial disorders or complex traits without a clear familial aggregation. The causal variants in single gene disorders are necessary and sufficient to impart large effects. In contrast, complex traits are attributable to a much more complicated network of contributory components that in aggregate increase the probability of disease. The conventional approach to identification of the causal variants for single gene disorders is genetic linkage. However, it does not offer sufficient resolution to map the causal genes in small families or sporadic cases. The approach to genetic studies of complex traits entails candidate gene or genome-wide association studies. Genome-wide association studies provide an unbiased survey of the effects of common genetic variants (common disease–common variant hypothesis). Genome-wide association studies have led to identification of a large number of alleles for various cardiovascular diseases. However, common alleles account for a relatively small fraction of the total heritability of the traits. Accordingly, the focus has shifted toward identification of rare variants that might impart larger effect sizes (rare variant–common disease hypothesis). This shift is made feasible by recent advances in massively parallel DNA sequencing platforms, which afford the opportunity to identify virtually all common as well as rare alleles in individuals. In this review, we discuss various strategies that are used to delineate the genetic contribution to medically important cardiovascular phenotypes, emphasizing the utility of the new deep sequencing approaches.