Flashcards: Association Analysis (Genomics)

Genetic association refers to the relationship between a genetic variation (such as a SNP) and a particular trait or disease in a population.

A genetic association study seeks to identify whether specific genetic variants are statistically linked to a particular phenotype (disease or trait).

Select a population: Individuals are grouped based on the presence or absence of the phenotype or disease.

Collect genetic data: DNA samples are collected from individuals, and genetic variants (e.g., SNPs) are genotyped.

Statistical analysis: The frequency of genetic variants is compared between individuals with and without the trait to identify associations.

Interpretation: Identify variants that are significantly associated with the disease or phenotype.

Common variants: The marker should be common in the population to have enough statistical power.

Biallelic: The marker should have two alleles to simplify analysis.

Located in or near a gene: To help correlate the variant with traits or diseases.

SNPs: SNPs (single nucleotide polymorphisms) fit these criteria, being common, biallelic, and located throughout the genome, making them useful in genetic studies.

GWAS aims to identify common genetic variants associated with diseases or traits.

Large sample sizes: Involves comparing the genomes of thousands of individuals with and without the condition.

Markers analyzed: SNPs are typically used as genetic markers for detecting associations.

Statistical significance: Associations are determined through statistical analysis, identifying variants significantly linked to the phenotype.

No predefined hypothesis: GWAS scans the whole genome without prior assumptions about which genes might be involved.

A Manhattan plot is a graphical representation of GWAS results, showing the association between SNPs and a phenotype.

X-axis: Chromosomal positions of the SNPs.

Y-axis: Statistical significance of the associations (often in -log10 scale). A peak indicates a significant association with a trait.

A regional association plot shows the association between genetic variants in a specific genomic region.

X-axis: Genomic position within the region of interest.

Y-axis: Statistical significance of the associations.

Interpretation: A large peak suggests that a genetic variant in this region is strongly associated with the trait or disease.

Meta-analysis is the statistical technique of combining results from multiple studies to increase the overall sample size and statistical power.

In genetic studies, meta-analysis is used to combine GWAS results from different populations or cohorts.

It improves the reliability of findings and helps identify consistent genetic associations across different studies.

Confounding factors: Environmental and lifestyle factors can obscure genetic findings.

Population stratification: Differences in ancestry between populations can lead to false associations.

Rare variants: GWAS may not effectively detect rare variants with large effects.

Missing heritability: GWAS may fail to explain the full genetic contribution to complex traits

Example: GWAS has identified FTO gene variants associated with obesity. These variants influence body mass index (BMI) and obesity susceptibility.

GWAS has also identified variants in the APOE gene associated with Alzheimer’s disease susceptibility.

Linkage disequilibrium (LD) is the non-random association of alleles at different loci.

In genetic association studies, LD can influence the identification of disease-related variants, as alleles at loci in high LD may co-occur.

A genetic association can occur due to LD between the SNP and the true causal variant, even if the SNP itself is not the causative variant.

A case-control study compares individuals with a specific disease (cases) to individuals without the disease (controls).

Cases: Individuals who have the disease or condition of interest (e.g., obesity, schizophrenia, hypertension).

Disease Definition: The disease must be defined rigorously and consistently across all cases.

Controls: Individuals without the disease, carefully matched for factors like age, sex, ancestry, and other non-disease traits.

Platform for data generation: Data from both cases and controls should be collected using the same methodology to reduce bias.

Large Numbers of Well-Defined Cases: A large sample size of cases, typically in the tens of thousands, to ensure statistical power.

Matched Controls: An equal or greater number of unrelated controls matched for factors like age, sex, and ethnicity.

Reliable Genotyping Technology: Technology such as SNP microarrays to accurately measure genetic variations.

Standard Statistical Analysis: Tools like PLINK for analyzing genetic data and identifying associations.

Replication of Positive Associations: To confirm the validity of results, any positive associations should be replicated in independent studies.

Polymorphic

Randomly distributed across the genome

Fixed location in the genome

Frequent in the genome

Frequent in the population

Stable with time

Easy to assay (genotype)

Common in the genome (~1/300 nucleotides)

~12 million common SNPs identified in the human genome

Generated by mismatch repair

Gene - coding region (1% of genome)

No amino acid change (synonymous)

Amino acid change (non-synonymous)

New stop codon (nonsense)

Gene - non-coding region

Promoter – mRNA and protein level changed

Enhancer – mRNA and protein level changed

Splice site – altered mRNA, altered protein

Intergenic region (50% of genome)

MAFs can differ significantly across different populations or ancestry groups

The allele frequency for a particular SNP, such as rs6801957, can vary in populations with different genetic backgrounds

This variation can impact the association of certain genetic traits or diseases, such as Brugada syndrome, which is linked to arrhythmia

Understanding MAFs across diverse populations is important for accurate disease association studies

SNPs are selected for genetic studies based on their MAF, with common diseases often linked to common variants

The rarer the variant, the more samples are needed to detect significant associations

SNPs with MAF > 0.05 (5%) are typically used in studies, although this is now reducing to > 0.01 (1%) with larger sample sizes like in the UK Biobank

The power of the study increases as the frequency of the variant becomes more common

Studies are trending toward examining rare variants as large-scale studies continue to expand

SNPs in close proximity on a chromosome tend to be inherited together, a phenomenon known as linkage disequilibrium (LD)

LD leads to SNPs being associated with each other more often than would be expected by chance

In GWAS, multiple SNPs in LD at the same locus might show an association, but it is often unclear which SNP is causal

LD is crucial for identifying genomic regions associated with diseases, as many SNPs just "tag" the causal variant

LD complicates pinpointing the exact causal variant, as it could be one of several in close proximity

Define your samples of interest

Recruit large numbers of cases (affected individuals) and matched controls (unaffected individuals)

Use existing datasets like the UK Biobank to define cases and controls based on clinical and demographic characteristics

2. Generate data and clean/quality control

Use reliable genotyping technology, such as SNP microarrays (e.g., Illumina Global Screening Array) to genotype markers across the whole genome

Conduct data quality control (QC) using standard analysis software (e.g., PLINK) to exclude poor-quality SNPs and samples

3. Statistical analysis to identify associated SNPs

Perform statistical tests, like the chi-squared test or more complex logistic regression, to identify associations between disease and allele variations at each marker

A positive association is typically considered at a p-value < 5x10^-8 after correcting for multiple testing

Findings should be replicated in independent datasets to confirm the robustness of the association

The peak of association in GWAS often does not identify the specific gene causing the disease.

The peak indicates the genomic region associated with the disease but does not pinpoint the exact causative variant.

Due to linkage disequilibrium (LD), positive associations can be seen over large regions (>100kbp), making it difficult to identify the exact gene involved.

GWAS has identified numerous statistically significant and reproducible genetic associations, but these do not fully explain the heritability of most diseases.

One challenge is the missed contribution of lower frequency and rare genetic variants, which are often not included in GWAS.

Copy Number Variations (CNVs) may also contribute to disease, but are not always captured in standard GWAS.

Epigenetic variation, such as DNA methylation or histone modification, could play a role in heritability but is not typically covered by GWAS.

Difficulty identifying causal variants: GWAS can find statistical associations, but pinpointing the exact causal variant and understanding the underlying biology is challenging.

European-ancestry bias: Most GWAS are based on populations of European ancestry, limiting our understanding of genetics across diverse populations and creating potential inequities in clinical applications.

Clinical application of disease risk estimates: Using disease risk estimates from GWAS in clinical care remains unclear, and translating findings into practical treatments is challenging.

Inequity in clinical translation: The lack of diverse representation in GWAS can cause skewed results, potentially leading to biased or less effective clinical treatments for non-European populations.

Challenges with large studies: Conducting large studies with over 1,000 cases and controls is difficult.

Combining smaller studies: Meta-analysis combines results from smaller studies to improve statistical power and detect associations.

Pre- and post-experiment meta-analysis: Meta-analysis can be done before experiments (as a consortium) or after experiments to pool results and increase the sample size.

Biobank studies: Large biobank studies, such as the UK Biobank with 500,000 individuals, provide a valuable resource for meta-analysis in genetic research.

Hundreds of loci: GWAS has identified hundreds of loci associated with obesity or BMI-related traits.

Reproducibility: The results are reproducible across different studies.

Understanding remains limited: While many loci are identified, we are still far from fully understanding how all of these genes contribute to obesity.