Microarray-Based Genotyping for GWAS Quiz

Genome-Wide Association Studies (GWAS)

• Single-nucleotide polymorphisms (SNPs) are the most commonly studied genetic variants in GWAS.
• GWAS typically report blocks of correlated SNPs that show a statistically significant association with the trait of interest, known as genomic risk loci.

The HapMap Project

• Mission: Develop a haplotype map of the human genome and describe the common patterns of human DNA sequence variation.
• Major technology used: genotyping microarrays.
• Genomic features assayed: single nucleotide polymorphisms (SNPs).

How GWAS Works

• The first step of a GWAS involves identifying the disease or trait to be studied and selecting an appropriate study population.
• GWAS often require very large sample sizes to identify reproducible genome-wide significant associations.
• The choice of data resource and study design depends on the required sample size, the experimental question, and the availability of pre-existing data or the ease with which new data can be collected.
• GWAS can be conducted using data from resources such as biobanks or cohorts with disease-focused or population-based recruitment.
• Public resources provide access to large cohorts with both genotypic and phenotypic information.

Manhattan Plot

• A Manhattan plot shows the significance of each variant's association with a phenotype.
• Each dot represents a single-nucleotide polymorphism (SNP), with SNPs ordered on the x-axis according to their genomic position.
• The y-axis represents the strength of their association measured as – log10 transformed P values.
• The red line marks the genome-wide significance threshold of P < 5 × 10–8.

Functional Follow-up on GWAS

• GWAS identifies associated variants, which are visualized as a Manhattan plot to show their genomic positions and strength of association.
• To prioritize likely causal variants, specific statistical analyses are applied to identify a set of variants that are likely to include the causal variant.
• Functional annotations of the genome can be integrated with GWAS data to identify epigenetic mechanisms that may be perturbed by the causal variant, including enhancers, promoters, or other functional elements.
• Additional approaches include mapping molecular quantitative trait loci (molQTL) and expression quantitative trait loci (eQTLs).
• To identify pathways whose perturbation may mediate the trait in question, one can analyze the enrichment of multiple GWAS-implicated genes in predefined pathways.

Genotyping Methods

• Microarray-based genotyping is the most commonly used method for obtaining genotypes for GWAS due to the current cost of next-generation sequencing.
• DNA microarrays contain hundreds of thousands or millions of probes that can be used to interrogate genomic sequence.
• Advances in array-based approaches have enabled detection of the main forms of genomic variation: amplifications, deletions, insertions, rearrangements, and base-pair changes.
• Imputation is a key step prior to a genome-wide association study (GWAS) or genomic prediction.
• Genotype imputation is the process of inferring unobserved genotypes in a sample of individuals using a sequenced haplotype reference panel such as the 1000 Genomes Project.