Lawrence, C. G.; Rubenstein, D.; McMillan, O.; Arias, C.

Understanding the repeatability of evolution requires disentangling the roles of constraint, contingency, and convergence in shaping phenotypic diversity. Mullerian mimicry in Heliconius butterflies offers a powerful natural experiment, with co-occurring species independently evolving similar wing color patterns under shared selective regimes. Here, we integrate high-throughput image-based phenotyping, genome-wide association studies (GWAS), and comparative pan-genomics to investigate the genetic architecture underlying convergent wing pattern evolution across parallel hybrid zones in Heliconius erato and H. melpomene. Using automated computer vision pipelines, we extracted and quantified color pattern variation from over 650 butterfly specimens. Principal component analysis (PCA) of recolorized, landmark-aligned wing images captured biologically meaningful axes of variation, which were used as phenotypes in GWAS. We identified strong associations at known patterning loci, including ivory:mir193 (previously coretex), optix, WntA, and vvl, as well as novel regions, including a chromosome 2 inversion in H. erato and a gustatory receptor gene (Gr21a) in H. melpomene. Comparative analyses using a Heliconius pan-genome revealed that while significant associations mapped to homologous regulatory regions across species, the specific variants were lineage-specific, consistent with parallel evolution via distinct cis-regulatory changes. These findings demonstrate that repeated adaptive outcomes can arise through different genetic paths within conserved regulatory architectures. More broadly, our study highlights the power of integrating machine learning, high-resolution phenotyping, and comparative genomics to dissect the molecular basis of convergent evolution in natural populations.