Simulation of CRISPR/Cas9 Mediated Gene Editing for Vitellogenin Gene (Vg) in Honeybees   Short title: In Silico CRISPR/Cas9 Targeting of Vitellogenin in Honey Bees

CRISPR/Cas9 genome editing provides a powerful framework for interrogating gene function in honey bees (Apis mellifera). Yet, empirical application remains challenging due to biological constraints, including haplodiploid genetics, narrow embryonic injection window, and the social rearing requirements that complicate functional validation. These constraints necessitate in silico pre-screening to maximize editing success before resource-intensive wet-lab implementation. Within the omnigenic framework, which distinguishes core regulatory genes from peripheral loci buffered by network effects, vitellogenin ( Vg ) represents an optimal target which ancestrally dedicated to yolk provisioning, it has been co-opted to orchestrate diverse non-reproductive functions including longevity, stress resistance, immunity, and social behavior. We developed a computational pipeline to design guide RNAs for targeted Vg knockout, evaluating candidate sites in functional exons based on RNA secondary structure thermodynamics and frameshift potential. Comparative analysis revealed complementary strengths in two lead candidates. The exon 2 target site exhibits markedly weaker secondary structure (ΔG = − 0.25 kcal/mol versus − 2.10 kcal/mol for exon 3), aligning with empirical evidence that sites with ΔG > − 1.0 kcal/mol achieve 2–5× higher Cas9 binding efficiency. This site yielded moderate frameshift frequency (77.8%; 61.9 percentile). Conversely, the exon 3 target, despite stronger structural constraints, demonstrated superior functional disruption metrics demonstrating very high frameshift frequency (88.3%; 95.2 percentile), high editing precision, minimal microhomology-mediated repair bias, and reproducible outcomes wherein nearly all predicted indels disrupt the coding sequence. We recommend parallel empirical validation of both exon 2 and exon 3 targets to resolve the trade-off between structural accessibility (favoring higher editing rates) and frameshift efficacy (favoring complete loss-of-function). This dual-target strategy accommodates uncertainty in in vivo performance while maximizing the probability of generating informative phenotypes. Our in silico framework enables rational CRISPR design in non-model organisms by computationally balancing biophysical accessibility with functional impact, accelerating functional genomics in species where empirical optimization faces substantial biological constraints.