Study of Cis-regulatory Effects at the Population Level

Regulatory genetic variation plays a fundamental role in shaping phenotypic diversity and adaptive potential within populations. While allele-specific expression (ASE) enables the detection of cis-regulatory effects at the individual level, extending this framework to population-scale inference remains challenging. In this study, we introduce a computational framework to quantify cis-regulatory variability at the population level by integrating Estimated Genetic Contribution (EGC) of alleles with classical population genetics principles. We then define a population-level imbalance statistic, named, which combines EGC and allele frequencies under Hardy-Weinberg Equilibrium (HWE), enabling formal testing of cis-regulatory imbalance. To test whether there is a correlation between and the HWE, we employ a Monte Carlo simulation framework, along with both classical and EGC-weighted HWE tests. Overall, this framework provides a robust approach for studying regulatory diversity in natural populations, overcoming key limitations of eQTL mapping and enabling evolutionary interpretations of gene expression variation.