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.

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

This concept is used to investigate whether variants in the genome are related to a disease or trait.

In genetic studies, case-control studies are conducted to test for genetic associations by comparing cases (disease present) with controls (disease absent).

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.

Affected Cases: Individuals who have the disease or condition of interest.

Unaffected Controls: Individuals who do not have the disease, matched for other risk factors such as age, sex, and ancestry.

Measure Genetic Loci: Both groups (cases and controls) have their genetic loci of interest measured.

Statistical Analysis: Statistical methods are used to compare the genetic data of cases and controls, identifying which genetic loci are associated with the disease.

Identify Genes/Genomic Regions: The analysis helps pinpoint specific genes or genomic regions that may be linked to the disease.

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)

Provides a unique identifier (rs number) for each SNP

Stores information about SNPs and their associated variations

Includes flanking sequence data for each SNP

Maintained by NCBI (National Center for Biotechnology Information)

Refers to the frequency of the less common allele in a population

The minor allele's frequency is less than 0.5, while the major allele is more common

In the example, C has a frequency of 0.567, and G has 0.433

The sum of major and minor allele frequencies equals 1 (Major Allele Frequency + Minor Allele Frequency = 1)

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

MAFs can differ across different populations, which makes matching cases and controls by ancestry essential

Failure to match by ancestry can lead to spurious signals, causing false associations

In practice, studies often focus on populations of European ancestry in regions like Europe and the US

Excluding individuals of other ancestries can prevent confounding factors and improve the accuracy of genetic association results

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

GWAS results are presented as a Manhattan plot.

X-axis represents the position of the SNP on the chromosome.

Y-axis represents the –log10(p-value) of the association between the SNP and the disease.

Typically, more than 1 million SNPs are plotted in the results.

The WTCCC is a major genetic association study involving multiple diseases.

It includes large datasets of cases and controls from different populations.

It was designed to identify genetic variants associated with diseases like diabetes, schizophrenia, and coronary artery disease.

Manhattan plots show the association of SNP markers with seven diseases studied by the WTCCC.

The x-axis represents the position of SNP markers on chromosomes, and the y-axis represents the -log10(p-value).

Green peaks in the plot indicate significant p-values, suggesting strong associations with the diseases.

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.

QT interval is an ECG trait that measures the time between the Q and T waves, reflecting ventricular repolarization.

GWAS of the QT interval can identify genetic variants that influence heart rhythm and are associated with heart disease.

Variants identified can help predict risk for conditions like arrhythmias or sudden cardiac death.

A regional association plot visualizes genetic associations within a specific genomic region, showing SNPs' p-values along the chromosome.

The plot helps identify genes that may be implicated in a trait, such as NOS1AP, which has been linked to cardiac traits like the QT interval.

Further research confirmed NOS1AP's role in regulating QT interval duration, supporting its significance in heart disease.

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.

Genetic basis of obesity: Obesity is strongly genetic, with twin studies showing 70-80% of body shape is genetically determined.

Adoption studies: These studies suggest 30-40% of obesity is genetic, showing the importance of genetics over environmental factors.

Family studies: Show a genetic contribution of 40-60% to obesity, supporting the hereditary link.

GWAS for obesity: GWAS can help identify genetic variants associated with obesity by analyzing large populations, providing insights into genetic predisposition.

Study details: The meta-analysis involved ~322,000 subjects and was published by Locke et al. in 2015 (Nature 518:197–206).

Number of loci: Identified 97 loci associated with BMI.

Collaborators: Included 125 separate studies with over 600 authors and more than 2000 collaborators.

Study scale: The study demonstrates the power of large-scale collaboration in identifying genetic factors associated with complex traits like BMI.

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.