Genomic Basis of Common Genetic Cardiovascular Disorders Flashcards
List some contributingpathologies to CVD that have a genetic underpinning
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- Vascular disease are very common
- Cardiomyopathies are common to rare
- Arrhythmias are rare
Cardiovascular Disease (CVD) is a leading cause of death and morbidity in Australia. It affects a significant portion of the population and results in numerous hospitalizations annually. Contributing pathologies to CVD that have a genetic basis include vascular diseases like atherosclerosis and coronary artery disease, cardiomyopathies and arrhythmias
Describe the pathogenesis of CVD in brief
CVD Pathogenesis: Formation of atherosclerotic plaque
Disease outcomes include:
- angina
- acute MI
- embolism and stroke
Formation of atherosclerotic plaque leads to blockage or a breakaway, either of which causes issues.
The genetic and environmental factors at any point in the pathway significantly contributes to the development of disease.
Describe the relationship between genetics and CVD
CVD is a complex disease with contributions from both genetic and environmental factors. Multiple genes and molecular pathways play a role in its pathogenesis, making the genetic contribution variable and complex.
Complex Inheritance of CVD
Many genes or gene products are involved in CVD.
They can be grouped according to some of the major components of the disease pathogenesis:
- Genetic factors controlling serum lipid transport and metabolism - cholesterol synthesis, uptake and removal
- Cholesterol, apolipoprotein E, apolipoprotein C-III, the low-density lipoprotein (LDL) receptor, and lipoprotein(a)—as well as total cholesterol level
- LDL, cholesterol and HDL levels are themselves quantitative traits with strong genetic determinants
- Vasoactivity a.k.a tone of endothelial cells, affecting constriction and relaxation, e.g. ACE
- Blood coagulation, platelet adhesion, fibrinolysis e.g. plasminogen activator inhibitor 1, and the platelet surface glycoproteins Ib and IIIa
- Inflammatory and immune pathways: immune cell regulation and function
- Arterial wall components: cellular structure, growth and repai
Describe the genetic and environmental CVD risk factors and their contribution
CVD is a complex disease
Genetic risk factors involves both monogenic and polygenic factors. Monogenic mutations lead to Mendelian inheritance, where single gene mutations have large effects but are relatively rare. Additionally, monogenic mutations have a more obvious effect and are thus easier to study. In contrast, polygenic factors involve multiple genetic variants, each having a small effect but being more common in the population. They have a less obvious effect and are thus harder to study.
Important questions regarding genetic risk factors for CVD include:
- how large is the contribution?
- What are they?
And broader questions:
- the relative contributions of genetic and non-genetic risk factors
- the interaction within and between the risk factors
Genetic Risk Factors and their Contribution, Environmental Risk Factors and their Contribution
Genetic risk factors are part of a complex interplay with non-genetic risk factors, such as smoking, age, gender, lifestyle, blood pressure, obesity, diabetes mellitus, and LDL/HDL cholesterol levels. Environmental risk factors, with no genetic involvement, are better understood than Mendelian contributors.
Understanding the contributions of genetic and environmental factors to CVD is essential for identifying therapeutic targets by finding the molecular pathways leading to CVD; as well as informing risk assessment, and personalized management.
Describe familial hypercholesterolemia
Familial Hypercholesterolemia (FH) is a monogenic cause of CVD, resulting from mutations in the LDL receptor gene.
Families were originally identified by their high LDL levels and high mortality e.g. MIs.
Mutations affect receptor expression and function.
Individuals with FH lack sufficient LDL receptors, leading to hypercholesterolemia and increased CVD risk.
Studies on FH, uncovered the biochemical pathways and their relationship to genetics, have contributed significantly to the development of cholesterol biosynthesis inhibitors like statins and bisphosphonates.
Most other genes show complex inheritance patterns and have significant environmental influence.
Describe the role of PCSK9
Introduction
- PCSK9 plays a role in cardiovascular disease (CVD) risk.
- Inactivating mutations in PCSK9 reduce CVD risk.
- Genetic variants of PCSK9 have been identified in different populations.
- Note: rare homozygous individuals have been identified that lack PCSK9 altogether and have very low serum LDL levels, but are otherwise healthy
PCSK9 Variants and CVD Risk
- European-descent: 3.2% carry R46L variant in PCSK9, resulting in 15% reduction in mean plasma LDL and 47% reduced risk of CAD.
- African-descent: 2.6% carry nonsense mutations in PCSK9, resulting in 28% reduction in mean plasma LDL and 88% reduced risk of CAD.
PCSK9 as a Therapeutic Target for CVD
- PCSK9 regulates cell surface expression of the LDL/LDL-C receptor.
- PCSK9 blockade increases LDL-C uptake, leading to lower plasma LDL-C.
- Monoclonal anti-PCSK9 antibodies like Alirocumab and Evolocumab are effective at lowering LDL-C, especially in refractory patients.
- Inclisiran
In Vivo CRISPR Base Editing of PCSK9
- Gene-editing technologies like CRISPR-Cas nucleases and CRISPR base editors can modify PCSK9 and durably lower cholesterol in primates.
True or False: there aren’t any other mendelian CVD genes
There are many other mendelian CVD and MI genes, although most are very rare. They related to low and high blood pressure, hypertrophic cardiomyopathy, Marfan’s syndrome, atrial or ventricular septal defects, and bicuspid aortic valve/calcific aortic valve disease.
Describe heritability between relatives
Heritability refers to the proportion of total phenotypic variance in a disease or trait that is due to genetic variation. Heritability represents a measure or estimate of how much genetic variation in a given population contributes to the disease or trait.
Family and twin studies are commonly used to estimate heritability, by trying to control for environemtnal influence.
Heritability between relatives
25% of genetic material is shared between an individual and their aunt, uncle, grandparent.
12.% of genetic material is shared between first cousins.
Family or population relative risk ratios are defined as frequency or prevalence of the disease in siblings or relatives divided by the population prevalence.
If prevalence in family is larger than in population, then the ratio is larger than 1, which implies a contribution of genetics to the disease.
This assumes that
- both study groups live in or experience equivalent environments
- have comparable genetic backgrounds e.g. ethnicity
Describe familial relative risks for males and female for CVD
Some familial relative risks for CVD
- for males, there is a 3-fold risk for CAD in male first degree relatives, or 2.5-fold risk in female first degree relatives
- for females, there is a 7-fold risk in male first degree relatives
- for females under 55 yrs, 11.4-fold risk in male first degree relatives
- for two male relatives under 55 years of age, 13-fold risk in first degree relatives
i.e. increases in younger age, and with more relatives
Describe twin studies and their results
Twin studies
Twin studies measure concordance or the proportion of sharing the same characteristics or disease traits.
Dizygotic twins share on average 50% of their genes in common, while monozygotic twins share 100% of their genes, excluding de novo mutations and epigenetic changes.
Dizygotic twins, as compared to siblings, share the same uterine environment and more than likely, exposures during early childhood.
Falconer’s formula reflects the fact that monozygotic twins have twice as much concordance in their DNA as compared to dizygotic twins.
If concordance for MZ is greater than concordance for DZ, this is a hallmark of the evidence for a genetic influence on disease or trait. Importantly, twin studies assume than when rMZ > rDZ, it is because MZ twins share more DNA in common, rather than experience a more similar environment.
If concordance is equal, there is no genetic influence on the disease or trait.
Twin studies have shown that heritability for CVD is higher in males than females and is greater for individuals with young-onset disease. Heritability estimates for fatal coronary events have been around 57% for males and 38% for females.
Limitations of Familial Aggregation and Heritability Estimates
Twin studies provide valuable insights into heritability, but they have limitations, particularly in separating genetic and environmental influences. The assumptions of twin studies may not always hold true, and other factors could influence concordance rates between MZ and DZ twins.
Describe GWAS and important requirements
Genome Wide Association Studies (GWAS)
- Simultaneous comparison of the frequencies of many common Single Nucleotide Polymorphisms (SNPs) with measurable disease traits.
- Uses variants that are common in the population (>5%) to enable large statistical comparisons.
- Mainly used to investigate common diseases with high heritability and measurable traits (e.g., MI, plasma LDL cholesterol, blood pressure).
- Conducted in multiple phases: discovery, replication, and meta-analysis.
- Larger cohorts provide greater power to test rarer alleles and detect smaller effect sizes.
Important Requirements for GWAS
- Reproducible phenotype measures.
- Equivalent genetic background (ethnicity and admixture).
- Corrections for multiple testing.
- SNP independence (“linkage equilibrium”).
- Conducting in multiple step-wise phases: discovery, replication and meta analysis
List and describe the requirements for GWAS
Important Requirements for GWAS
- Reproducible phenotype measures.
- Equivalent genetic background (ethnicity and admixture).
- Corrections for multiple testing.
- SNP independence (“linkage equilibrium”).
- Conducting in multiple step-wise phases: discovery, replication and meta analysis
Steps of GWAS
1. Discovery Study (Case-control) - Test 1-2 million SNPs in large cohorts.
- determine status of SNPs
- individuals are grouped based on phenotype
- check frequency of SNP between groups – aka compare the proportions of carriers.
2. Replication Study - Confirm associations of multiple SNPs (loci) in independent cohorts.
- meta-analysis
Genetic Risk Variants Associated with Coronary Artery Disease or Myocardial Infarction
- Listed in chromosomal locations, SNPs, nearby genes, frequency in the population, and odds ratios.
Describe the findings from CVD GWAS studies
Findings from CVD GWAS studies
At least 150 “loci” contribute independently to CVD/MI risk. Most associated SNV are markers, not located in genes. The gene is located close or ”linked” to the SNV. Several SNV are located in/near genes involved in
pathways that make biological and clinical sense. Informs disease pathogenesis and intervention targets.
Eg. Metabolism of LDL-C, HDL-C, triglycerides; roles in hypertension or thrombosis.
Note that for alleles with high frequency (common alleles), odds ratios are low.
Individual contributions are low and alleles have a low effect size.
This collection includes LDL-R, and PCSK9.
Describe the challenges, problems and significance of GWAS
Current challenges and problems.
- Many SNV are in genes with unknown functions.
- Many other CVD-associated SNVs are not close to any known gene and/or do not result in obvious genetic changes. Majority of large cohort studies focus on populations with European ancestry – are they informative for other populations?
Significance of GWAS Discoveries
- Large meta-analyses have identified multiple loci with smaller effect sizes but genome-wide significance.
- GWAS loci explain only a fraction of the estimated heritability of complex diseases.
- Many novel loci have been identified, highlighting the discovery potential of GWAS.
What are the options for CVD prediction and prognosis based on genetics? What are the limitations?
- The aim is to identify high risk individuals to prevent progression to ACS
- Framingham risk score is the current gold standard for CVD risk prediction but has limited accuracy.
- input lifestyle variants
- 50-80% accurate
- note: simple additive approaches only modestly improve risk classification
- NOTE: GWAS findings are problematic for population risk prediction – population risk predictors comes with inherent problems, such as under-representation of cases and assumptions about constant effects across different ethnicities
- Most GWAS-based discoveries based on extreme phenotypes
- Under-representation of cases (MI cases occurring before enrolment)
- Restricted time span of the effect
- Assumptions that effects are constant across different ethnicities
- Therefore, true effects in population may be quite different to GWAS estimates
