Parameterizing the genetic architecture under stabilizing selection

Across many complex traits, genetic variants with larger effect sizes tend to occur at lower frequen cies, which is often interpreted as a signature of stabilizing selection. In statistical genetics, the so-called α-model captures this relationship by assuming that effect size variance is inversely proportional to het erozygosity raised to a power $0⩽\alpha ⩽1$ . Although empirically useful, the α-model is phenomenolog ical rather than mechanistic and lacks a direct population-genetic interpretation. In this paper, we derive an alternative to the α-model based on evolutionary theory. Our approach yields a linear mixed model in which the frequency dependence of effect size emerges naturally as a function of interpretable evolu tionary quantities describing mutational variance, selection intensity, and coupling between the focal and selected traits. These quantities enter through two identifiable variance components that can be estimated by restricted maximum likelihood (REML). The resulting framework links a fitness-landscape model to standard mixed-model methodology, enabling both inference on evolutionary parameters and downstream prediction by best linear unbiased prediction (BLUP). In forward simulations, the model accurately re covers the focal-trait variance and generally improves genetic prediction relative to conventional α-model baselines.