Computational Biology
Statistical Genetics
Association methods, heritability estimation and polygenic architecture.
Scientific context
Understanding the field
Methodological work covers genome-wide association analysis, fine-mapping, Mendelian randomisation and the calibration of polygenic scores across ancestrally diverse cohorts.
Statistical genetics develops quantitative methods to identify and characterise relationships between genomic variation and traits. It separates signal from confounding and quantifies uncertainty across large, structured datasets.
Central questions
- Which loci are robustly associated with a trait?
- How transferable are estimates across populations and settings?
Methodological framework
- Genome-wide association studies and fine-mapping
- Mixed models, causal inference and sensitivity analysis
- Polygenic-score validation and calibration
Relevance
Scientific and clinical value
Well-designed analyses illuminate genetic architecture, support target discovery and provide a transparent basis for evaluating risk models rather than treating statistical association as biological certainty.
Limits and responsibility
Population structure, selection bias, phenotype definition and multiple testing can distort estimates. Polygenic scores often lose calibration outside the populations and settings in which they were developed.
Authoritative resources
Public reference resources
These independent resources are provided for scholarly orientation; inclusion does not imply an institutional partnership. This page does not replace medical advice or diagnosis.