In GWAS, how are disease-associated SNPs identified?

In GWAS, how are disease-associated SNPs identified?

• A. They do not require large sample sizes
• B. They sequence entire genomes of individuals to identify rare variants
• C. They prove causation between SNPs and disease
• D. They compare allele frequencies in cases and controls to identify associations

Answer: (D)

In GWAS, the idea is to identify variants that occur more often in people with the disease than in people without it by comparing allele frequencies across large groups. Researchers genotype many individuals, separating them into cases and controls, and test each SNP to see if the frequency of a particular allele differs between the groups in a way that is unlikely to be due to chance. If an allele is more common in cases, it’s flagged as disease-associated, pointing to a genomic region that may influence risk. However, this shows association, not causation, because the signal can arise from nearby causal variants linked to the tested SNP. Because millions of variants are examined, strong statistical corrections for multiple testing are needed and large sample sizes help detect small effects. Traditional GWAS uses genotyping arrays to survey common variants rather than sequencing entire genomes; sequencing-based studies exist but address things a bit differently.

In GWAS, the idea is to identify variants that occur more often in people with the disease than in people without it by comparing allele frequencies across large groups. Researchers genotype many individuals, separating them into cases and controls, and test each SNP to see if the frequency of a particular allele differs between the groups in a way that is unlikely to be due to chance. If an allele is more common in cases, it’s flagged as disease-associated, pointing to a genomic region that may influence risk. However, this shows association, not causation, because the signal can arise from nearby causal variants linked to the tested SNP. Because millions of variants are examined, strong statistical corrections for multiple testing are needed and large sample sizes help detect small effects. Traditional GWAS uses genotyping arrays to survey common variants rather than sequencing entire genomes; sequencing-based studies exist but address things a bit differently.