Genetics of Common Disease (Genomics)

Environment is not essential in causing the disease.

Single gene (variant) is responsible for the disease risk in families, though multiple genes can be associated with the condition.

Rare, occurring in less than 0.05% of the population.

Clear inheritance patterns such as:

Autosomal dominant

Autosomal recessive

X-linked, etc.

Environmental and clinical co-morbidities, such as smoking, obesity, and diet.

Variants in many genes combine to determine risk in individuals, making them polygenic.

They are much more common than monogenic diseases.

No clear inheritance pattern.

Stroke

Schizophrenia

Duchenne Muscular Dystrophy (Purely genetic)Mesothelioma (Purely environmental)

There is a spectrum of genetic vs. environmental risk factors, not discrete categories.

Yes, there can be different forms of the same disease or condition, influenced by both genetic and environmental factors.

No, more than 50% of cases have no monogenic cause.

Yes, even complex diseases like coronary artery disease may have rare monogenic forms.

Twin studies compare the frequency of identical twins having the same trait/disease with that in non-identical twins.

Identical twins have the same genomes and similar environments.

Non-identical twins have different genomes but share similar environments (like siblings).

If the trait/disease is higher in identical twins, it suggests a genetic effect.

If the frequency is the same, it suggests environmental effects are more important

Genome-wide association studies (GWAS) can now estimate genetic heritability.

Genetic association refers to the presence of an allele at a higher frequency in unrelated individuals with a particular disease compared to those without the disease.

Case-control studies compare individuals with a disease (cases) to those without it (controls) to identify genetic differences between the groups.

Linkage disequilibrium occurs when genetic marker alleles that are close to a disease-causing allele on the same chromosome are more likely to be inherited together.

Close alleles are more likely to be inherited together because they are physically close on the chromosome and less likely to be separated by recombination.

Marker alleles that are typically within 100kb of a disease-causing allele can be detected in population-based studies, compared to >10Mb in linkage studies.

In population-based studies, recombination can happen in various regions of the chromosome across different individuals, increasing its likelihood. In linkage studies, recombination is less frequent because the focus is on specific regions within families, limiting the variability in recombination events.

Large numbers of well-defined cases and controls

Cases and controls should be matched as closely as possible, especially for ancestry

Reliable and cost-effective genotyping technology, such as SNP microarrays

Matching cases and controls, particularly for ancestry, helps reduce confounding factors that could distort genetic associations.

SNP microarrays are commonly used due to their dense coverage and cost-effectiveness.

Standard quality control workflows should be applied to ensure accurate data.

Statistical analysis should include multiple testing correction due to the large number of independent loci (approx. 1 million).

Replication of positive associations in independent datasets helps confirm the validity and reliability of the findings.

CAD is caused by atherosclerosis (lipid deposition and inflammation) in blood vessels that supply the heart.

This can lead to myocardial infarction (MI) or heart attacks, a leading cause of death in the developed world.

CAD affects approximately 5% of individuals.

Early family studies (1950s) indicated a heritable component.

Twin studies (1994) estimated the heritability of CAD at around 50%, and genome-wide approaches suggest 40-50% heritability.

Yes, familial hypercholesterolaemia (FH) is a rare monogenic form of CAD.

Mutations in LDLR, APOB, and PCSK9 can increase CAD risk by ~4-fold.

While rare monogenic forms exist, the majority of CAD cases are explained by polygenic (common variants) and environmental factors, making CAD a complex disease.

CAD was one of the first diseases studied by GWAS in 2007 by the Wellcome Trust Case Control Consortium.

Three other studies published the same year in Nature, Science, and New England Journal of Medicine showed similar results.

A single locus was identified on chr 9p21, associated with a 30% increase in CAD risk per copy of the risk allele.

The risk variants at chr 9p21 alter the expression of the non-coding RNA ANRIL, which affects the activity of cyclin-dependent kinase inhibitors CDKN2A and CDKN2B.

You don’t visualize them all directly, but you circularize the Manhattan Plot.

A Manhattan Plot displays genome-wide association results with each point representing a genetic variant. The x-axis is chromosome position, and the y-axis shows the significance (p-value) of association.

You can include different tracks for different analyses or ancestry groups to provide a more detailed view of the results.Example: Million Veterans Project dataset uses this approach.

These studies have identified numerous SNPs/loci associated with the risk of developing CAD.

By typing (identifying) the SNPs in a particular individual, we can determine which risk variants they carry.

Common SNPs (~400 at genome-wide significance) means that everyone carries many risk variants for CAD.

A PRS/PGS is a quantitative assessment of an individual's genetic risk/predisposition to a disease by combining data for all identified risk variants.

Measurement of associated disease risk for every SNP or locus in the genome.

For each individual, calculate the number of risk alleles for each SNP.

This is weighted according to the effect size from GWAS summary statistics.

Polygenic scores can include genome-wide significant SNPs that have strong evidence of association with the trait.

They can also include SNPs below the significance threshold, which are still associated with the trait but with weaker evidence, as long as these SNPs are independent (not in linkage disequilibrium with others).

The population distribution of polygenic scores follows a normal distribution.

By comparing an individual's polygenic score to the population distribution, we can assess their relative risk of the disease.

PRS can be measured from birth, unlike other CAD risk factors that develop over several decades.

Early identification of individuals at high genetic risk for CAD enables early intervention before the disease develops.

Statin therapy can reduce cholesterol levels and lower CAD risk.

Lifestyle interventions could be implemented if an individual is at high genetic risk.

Yes, measuring PRS is relatively cheap, especially using SNP microarrays, and the same dataset can be used for PRS in other diseases (e.g., breast cancer, diabetes).

Yes, PRS does stratify risk for CAD by identifying individuals at higher or lower genetic risk for developing the disease.

PRS uses genetic data from multiple SNPs to calculate an individual’s relative genetic risk, allowing for personalized risk assessment and more targeted interventions.

Yes, PRS provides independent predictive value above clinical risk factors by identifying genetic predisposition that is not captured by traditional clinical measures.

While clinical risk factors like smoking, diabetes, BMI, and others are important, PRS adds genetic information that helps identify additional risk for CAD that may not be visible through clinical factors alone.

metaGRS is a CAD Polygenic Risk Score (PRS) developed to predict the occurrence of CAD by using genetic information from a large dataset, like the UK Biobank.

metaGRS was tested by comparing its ability to predict CAD occurrence in individuals with clinical risk factors using data from the UK Biobank, allowing researchers to assess its effectiveness in improving prediction beyond clinical factors alone.

Yes, PRS has shown potential in predicting CAD risk in the real world by identifying individuals at high genetic risk even before clinical symptoms appear, enabling early intervention.

PRS can be integrated into clinical practice alongside traditional risk factors (e.g., smoking, hypertension) to offer more personalized risk stratification and guide preventive measures, such as lifestyle changes or statin therapy.

Yes, PRS has shown potential in predicting CAD risk in the real world by identifying individuals at high genetic risk even before clinical symptoms appear, enabling early intervention.

PRS can be integrated into clinical practice alongside traditional risk factors (e.g., smoking, hypertension) to offer more personalized risk stratification and guide preventive measures, such as lifestyle changes or statin therapy.

European ancestry bias: PRS is less effective for individuals of non-European ancestry due to limited representation in GWAS.

Standardization issues: Multiple PRS tools exist with different datasets and methodologies, creating inconsistency.

Cost and integration: While SNP arrays are inexpensive, setting up a program to use PRS in clinical practice is costly.

Education: Both healthcare professionals and patients need to be educated on the value of genetic risk to motivate behavioural changes.

PRS is derived from GWAS data, calculating the number of risk alleles for common genetic variants associated with diseases like CAD.

It provides a quantitative risk assessment, helping healthcare professionals assess disease predisposition and decide on preventive measures.

Diet: Poor dietary habits (high-fat, high-sugar diets) contribute to diseases like type 2 diabetes, heart disease, and obesity.

Air pollution: Long-term exposure to air pollutants is associated with respiratory diseases like asthma and cardiovascular diseases.

Penetrance refers to the proportion of individuals with a specific genetic variant who actually express the associated phenotype or disease. If a disease-causing mutation has 100% penetrance, every person with the mutation will develop the disease, whereas lower penetrance means not everyone with the mutation will show symptoms.

(C) Rare single nucleotide variation in intergenic region of genome: Variations in intergenic regions are less likely to have a direct impact on a protein function, which is essential for monogenic diseases.

(D) Common missense variation: Common variants are generally less likely to cause severe, early-onset lethal diseases since they do not have a large impact on protein function.

(A) It is a SNP: TRUE. A missense variant is a single nucleotide polymorphism (SNP), as it involves a change in one nucleotide, leading to an amino acid change.

(C) It results in a change in amino acid sequence: TRUE. A missense mutation changes one amino acid in the protein sequence.

Explanation for incorrect options:

(B) It is likely to cause muscular dystrophy: FALSE. Given that 30% of Europeans carry this variant, it is likely a common polymorphism, not a rare, disease-causing mutation.

(D) It is a deletion: FALSE. A missense variant is a single nucleotide change, not a deletion.

(E) It is a mutation: FALSE. While a missense variant is technically a mutation, it's a common polymorphism, not a rare variant associated with disease.