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Causality

Definition
AI-generated

Causality is the relation by which changes in one process or variable bring about, or help explain, changes in another—distinct from mere association.

Topics
Core genetics Epidemiology GWAS
Related
Biological Causality Causal Inference Colocalization

Why it matters in GWAS

GWAS yields robust associations; interpreting them as causal risk mechanisms, drug targets, or mediators requires extra evidence (fine-mapping, functional assays, family designs, Mendelian randomization, or experiments). Conflating association with causation is a common source of misinterpretation in follow-up and reporting.

Example usage

"The review separated statistical evidence for a causal effect of the biomarker from biological causality at the locus, noting that the lead SNP could tag several correlated changes."

Biological Causality Causal Inference Colocalization Fine-Mapping Genome-Wide Association Study (GWAS) Mendelian Randomization Statistical Causality Triangulation
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Pearl J. (2009). Causality: models, reasoning, and inference. Cambridge University Press.
  • Hernán MA, Robins JM. (2020). Causal inference: what if. Chapman & Hall/CRC.
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