Predicting fitness from genomic offset: a multivariate latent-trait framework

Abstract

Genomic offset methods quantify the genetic consequences of environmental change using genotype environment associations (GEA), but the conditions under which they predict fitness remain poorly understood. Here, we develop a theoretical framework linking genomic offset to relative fitness loss through multivariate latent adaptive traits under stabilizing selection. We show that squared genomic offset is linearly related to expected log relative fitness loss when averaged over genetic architectures, provided that direct environmental effects are absent. However, this relationship does not necessarily hold for a realized genetic architecture. We identify two complementary mechanisms that can explain high predictive accuracy of genomic offset statistics. First, when effective adaptive dimensionality is sufficiently large, the genetically mediated fitness response concentrates around its marginal expectation, reducing the dependence on the particular genetic architecture. Second, even with few adaptive traits, accurate predictions can arise from geometric alignment between the complete fitness loss matrix and the GEA metric. This second mechanism also encompasses direct environmental effects and their interactions with genetically mediated responses, which may preserve or weaken predictive accuracy depending on their alignment. We further derive bounds on predictive accuracy that characterize these effects. Our results clarify the assumptions and limitations underlying genomic offset predictions and suggest that genomic offset is best interpreted as a measure of genetically informed adaptive mismatch, rather than a direct predictor of realized fitness or any particular phenotypic trait.