eQTLanalyses

EQuL analyses are studies that seek to identify genetic variants that influence gene expression levels. By linking genotype to transcript abundance, they help elucidate molecular mechanisms by which genetic variation contributes to phenotypic diversity. eQTL analyses are typically categorized as cis eQTLs, where the regulatory variant is located near the gene (often within 1 Mb), or trans eQTLs, where the variant influences distant genes or acts in trans networks. Analyses are usually performed in one or more tissues, reflecting tissue-specific regulation, or in bulk tissues and single-cell–derived aggregates.

Data for eQTL analyses require matched genotype data and expression measurements. Expression data are normalized and transformed to stabilize variance; covariates and hidden factors are accounted for, for example through principal components, batch effects, or inferred factors. Statistical models, commonly linear regression or linear mixed models, test associations between genotype dosage and expression levels, adjusting for covariates and population structure. Multiple testing corrections, such as false discovery rate or Bonferroni, are applied to control type I error.

Common considerations include statistical power, driven by sample size, effect size, and expression heritability; cis eQTLs are generally more detectable than trans. Data quality issues such as imputation accuracy and batch effects must be managed. Replication in independent cohorts and cross-tissue analyses help distinguish shared versus tissue-specific eQTLs. Colocalization with genome-wide association study signals and fine-mapping are used to prioritize likely causal variants and genes.

Extensions of eQTL analyses include single-cell approaches, which map cell-type–specific regulatory variants using scRNA-seq data or pseudobulk aggregation, and dynamic eQTL analyses across developmental stages or conditions. Outputs of eQTL analyses link genetic variation to gene regulation, aiding interpretation of disease loci and guiding functional follow-up. Public resources such as GTEx provide large-scale eQTL maps for multiple tissues.