Abstract
Genotype–environment association (GEA) is widely used for identifying genetic variation linked to environmental pressures. Their proliferation over the past decade offers a critical opportunity to synthesize how adaptive variation is identified and distributed across broad taxonomic groups. Here, we reviewed 194 GEA studies from the Kingdom Animalia to summarize the analytical methods employed and the key predictors of adaptive variation. Our review revealed that latent factor mixed models (LFMM) and redundancy analyses (RDA) are the most frequently used methods, with most studies employing multiple analytical approaches. On average, studies sampled approximately 0.05% of the focal species' genome (SD = 0.14%). Across studies using genome‐representative markers, we identified a non‐linear relationship between genome coverage and candidate loci detected, indicating diminishing returns beyond 0.45% genomic coverage, supporting the importance of factors beyond dataset size. Climatic variables reflecting extremes and variation were most consistent for detecting candidate loci, but these patterns varied greatly across taxa. We also identified influential taxon‐specific environmental relationships for bony fishes, arthropods, mammals, birds, herpetofauna, and molluscs and highlighted influential variables to inform future research efforts. This synthesis confirms the tight linkage between adaptive variation and species' ecology, offering a quantitative guide for future study design to improve statistical detection power, and prioritize the environmental drivers shaping evolution.