A genome-wide association study tests whether genetic variants are statistically associated with a trait or disease across many individuals. The resulting signal can identify an informative region of the genome. It does not automatically reveal the causal nucleotide, the relevant gene or the mechanism connecting that region to the phenotype.
Distinguish a marker from a mechanism
NHGRI describes GWAS as an approach for finding statistical associations. Nearby variants can be correlated through linkage disequilibrium, so the strongest measured association may tag another variant with the causal effect. Naming a locus after the nearest gene is useful shorthand only if readers understand that it is not a demonstrated functional assignment. Read the NHGRI explanation.
Interpret effect size, uncertainty and study design alongside statistical significance. A small P value can reflect a precise estimate of a small effect in a large sample. It does not establish that the variant is useful for predicting an individual’s outcome or that changing the implicated pathway will provide clinical benefit.
Build several independent links
The 2015 study of the FTO obesity-associated region illustrates the work required to investigate a mechanism. The investigators combined regulatory information and functional experiments to connect variation in the region with IRX3 and IRX5 activity and adipocyte biology. This example demonstrates why a locus name and a mechanistic target need not be the same thing. It does not imply that one pathway explains all obesity. Read the FTO-region study.
A strong causal argument asks whether candidate variants can be narrowed, whether a relevant molecular trait shares the association, and whether perturbing the proposed element produces the expected effect in a suitable context. Concordance is most convincing when the evidence sources have different vulnerabilities to bias.
Report the level of inference reached
- Association: a region is linked statistically to the phenotype.
- Prioritization: particular variants or genes are stronger candidates.
- Mechanism: experiments support a specific biological connection.
- Translation: an intervention has demonstrated relevant benefit.
These stages can overlap, but none should be assumed from the previous one. Clear language helps readers distinguish a new hypothesis from an established causal result. It also makes follow-up more efficient: the next experiment should address the weakest important link in the chain rather than merely reproduce the original association.