Strategic approaches to unraveling genetic causes of cardiovascular diseases

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.

Figure 1. Genomic and genetic determinants of phenotype

The nuclear genome is comprised of 4 nucleotides that are in tandem and randomly repeated in a complex 2-meter long polymer with a diameter of 2 nM and a volume of ~ 4 × 10⁷ uM. It is packed into the nucleus as 22 pairs of somatic and 2 sex chromosomes. Various components of the genome ranging from compactness of DNA to specific base pair changes could impart phenotypic effects. Examples are: A. Chromosomal abnormalities; B. Modifications of the octomeric histon complex, comprised of two copies H2A, H2B, H3 and H4 proteins, through methylation and acetylation; C. Changes in transcription factors; D. Expression of microRNAs from introns and inter-gene regions; E. Expression of long non-coding RNAs; F. Methylation of the CpG dinucleotides on promoters; G. SNPs; H. SVs/CNVs; I. Changes in telomere structure and function; J. Alternative mRNA splicing; K. Expression of protein isoforms; and L; Post-translation modification of proteins. It is also notable that at least 6% of the human genome is under evolutionary purifying selection, which indicates functional significance. However, the functions and biological impacts of these CNEs remain unknown . To identify and characterize determinants of a phenotype, a comprehensive approach that builds all constituents of the phenotype into the modeling would be necessary. A prototypic comprehensive approach has been completed for two model organisms ^, . (Illustration Credit: Cosmocyte/Ben Smith).

Figure 2. Gradients of disease prevalence, MAFs and effect sizes

The prevalence of disease, number of genetic determinants and the effect sizes of the DSVs are depicted as continuums. Single gene disorders are caused by rare variants with large effect sizes. Typically, several other variants also expected to contribute to phenotypic expression of the diseases. On the opposite end of the spectrum are the common complex traits, which are caused by a very large number of genetic variants, each imparting a modest effect size.

Figure 3. Relationship between effect sizes of DSVs and proximity of the phenotype

The influence of genetic variants is expected to correlate inversely with the proximity of the phenotype to genes. The effect size is greater for the proximal phenotypes, such as mRNA levels than for distant phenotypes, such as mortality, wherein a large number of competing genetic and non-genetic determinants contribute to the phenotype.

Figure 4. Detection of single nucleotide variant by whole-exome sequencing

Panel A illustrates an example of sequence output (anti-sense strand) of a NGS machine from a family member heterozygous for G>A (c.C34T, p.Q12X) mutation in AMPD1. Panel B represents a sequence read out from another family member who is homozygous for the mutation and has skeletal myopathy. The homozygous p.Q12X mutation leads to skeletal myopathy due to AMPD deficiency, which was confirmed biochemically. An arrow indicates the mutation.

Figure 5. Clinical Implications of DSVs

Biological and clinical significance of DSVs is expected to follow a continuum. For simplicity, we have highlighted five classes of DSVs in the continuum of their effects, in terms of their biological and clinical significance: Disease-causing variants: Disease-causing variants when present cause a disease, albeit with variable penetrance and considerable phenotypic variability. They impart large effects and are rare in each genome. The variants co-segregate with inheritance of the phenotype in members of large families or in multiple families and are absent in the clinically unaffected family members – notwithstanding the penetrance – and in the general population. These variants are also expected to impart considerable functional and biological effects. The disease-causing variants could provide insights into molecular pathogenesis of the phenotype and guide the development of new therapeutic and preventive targets. Likewise, they might also serve as diagnostic markers typically in familial situations and help to discern the true phenotype from phenocopy. The absence of a disease-causing variant in a family member at risk renders the likelihood of developing the disease remote. The disease-causing variants have limited utility in prognostication and risk stratification because of the complexity of determinants of the clinical phenotypes. Likely disease-causing variants: Genetic data implies causality but the evidence is inadequate to substantiate it. Statistical evidence indicates a strong association but typically an imperfect penetrance thwarts detecting a perfect co-segregation. This is often the case for rare and private DSVs in small size families and sporadic cases. These variants are typically absent in a large number of ethnically matched independent control individuals. These variants are also expected to impart significant functional and biological effects and be more common in the genome than the disease-causing variants. Clinical implications of the likely disease-causing variants are less robust than those for the disease-causing variants. Phenotype-associated variants: Causality is difficult to establish for this category of DSVs, particularly in sporadic cases and small families. Despite a statistical association additional functional and mechanistic studies are necessary to imply a causal role. The disease-associated variants are typically identified based on differences in MAFs frequencies in the cases and controls, such as through GWAS or candidate gene studies. These variants are often in LD with the true causal allele. The extent of LD in the genome varies but could extent to several million base pairs . Variants that affect structure, function and splicing of the genes carry a higher chance of being causal variants than those located in introns or inter-gene regions. Identification of these variants could provide insights into the molecular pathogenesis of the phenotype but they have no or very limited value in genetic diagnosis or risk stratification. The strength of the statistical association does not translate into clinical significance. A 5% increased in the MAF of a SNP from 0.45 to 0.50 in a large case-control study could result to exceedingly low p values and might have high attributable risk in a population but at an individual level it does not offer much clinical utility. Likewise, the clinical significance of the observed relative risks or Odds ratios should be interpreted in the context of pre-test likelihood of the clinical event. A two-fold increase in the risk of heart failure is not much clinically informative if the a priori risk of heart failure in the study population is exceedingly low. Functional variants not associated with a clinical phenotype: The human genome contains a large number of genetic polymorphisms including insertions, deletions, non-sense variants, splice junction variants, CNVs, etc, many of which exert functional functions. Despite the evidence for biological functions, these variants are not known to influence disease-risk or be associated with any clinical phenotype. These variants have minimal clinical utility or application. Variants with unknown significance: The vast majority of ~ 4 million DSVs in the genome probably fit into this category. Most are located in inter-gene regions and introns and are not known to convey biological functions. These variants have no known clinical utility.