Experimental Design, Statistical Inference, and Reproducibility in Genetics
Study design principles
Inference workflow
Genetic studies require careful control of confounding, population structure, batch effects, relatedness, and multiple hypothesis testing. Strong studies predefine endpoints, include appropriate controls, estimate effect sizes with confidence intervals, and validate findings in independent cohorts or orthogonal assays.
Why is multiple-testing correction essential in genome-wide studies?
Genome-wide scans test thousands to millions of hypotheses, so raw p-values are misleading without correction.
Correct answer: Because testing many hypotheses inflates false positives
What is a confounder in a genetic association study?
Population structure is a classic confounder in association genetics.
Correct answer: A variable associated with both genotype and phenotype that can produce a spurious association if unaccounted for.
Statistical genetics
Common analyses
Common frameworks include linear mixed models, logistic regression, Bayesian fine-mapping, Mendelian randomization, and colocalization analysis. These methods use genotype, phenotype, and sometimes functional annotations to estimate association, causality, and shared signal.
Analytical approaches
GWAS
- Maps common-variant associations
- Powerful for large cohorts
Fine-mapping
- Narrows candidate causal variants
- Uses LD and prior information
What is the main purpose of a genetic instrument in Mendelian randomization?
A valid genetic instrument serves as a proxy for an exposure under defined assumptions.
Correct answer: To proxy an exposure using inherited variation
Why is replication in an independent cohort important?
Replication is central to credible genetic inference.
Correct answer: It tests whether a finding is robust, generalizable, and not an artifact of sampling or analytic choices.
Reproducibility warning
Analytic flexibility can produce irreproducible findings; preregistration, code sharing, and transparent QC improve reliability.