GWAS

A genome-wide association study (GWAS) is a research approach used to identify genetic variants linked to traits across the genome. GWAS scan hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) in large groups of individuals to detect variants that occur more frequently in those with a trait or disease than in controls.

Most GWAS are case-control or quantitative trait studies requiring large sample sizes to detect small effect sizes. Each variant is tested for association with the trait, typically using regression under an additive model. Because millions of tests are performed, results must meet a stringent significance threshold (commonly P < 5 × 10^-8) to limit false positives. Loci often tag regions in linkage disequilibrium and reflect the polygenic nature of most traits.

Data processing includes quality control, imputation, and control for population structure. Researchers exclude related individuals, adjust for ancestry using principal components or mixed models, and impute missing genotypes against reference panels (e.g., 1000 Genomes, HRC). Follow-up analyses include fine-mapping, functional annotation, colocalization with expression data, and multi-omics integration to pinpoint potential causal genes.

Impact and limitations: GWAS have identified thousands of trait-associated loci, improving understanding of biology, informing drug targets, and enabling polygenic risk scores. Limitations include modest effect sizes, uneven representation of ancestries, challenges interpreting non-coding variants, and distinguishing causation from correlation. Ethical considerations cover privacy, consent, data sharing, and appropriate use of results.