A haplotype reference panel and genotype imputation framework for the black soldier fly (Hermetia illucens)
Abstract
Low-coverage whole genome sequencing (lcWGS) combined with genotype imputation provides a cost-efficient alternative to high-coverage sequencing for large-scale genotyping. Although widely implemented in human and livestock genomics, this strategy has not yet been systematically optimized for insects of industrial importance. The black soldier fly (BSF, Hermetia illucens) is increasingly used in global waste bioconversion and sustainable protein production, but genomic resources remain limited. Here, we develop the first BSF haplotype reference panel, containing ∼29.8 million high-quality SNPs from 168 high-coverage genomes, and benchmark imputation performance using a validation experiment in which 33 high-coverage individuals were down-sampled to low coverage and imputed against a reference panel of 135 individuals. We evaluated the performance of three imputation tools, QUILT v1.0.5, GLIMPSE2, and STITCH v1.7.2, across multiple sequencing depths (0.5 × -3×) and allele frequency bins. Based on this validation, QUILT v1.0.5 achieved the highest accuracy overall, particularly for rare variants (MAF < 0.05), whereas GLIMPSE2 delivered comparable accuracy for common variants with approximately twofold faster runtimes. STITCH enabled reference-free imputation but exhibited reduced accuracy relative to reference-based approaches. We then applied the optimized framework to 180 low-coverage (∼1×) BSF genomes, demonstrating the practical utility of the reference panel for large-scale genotyping when true genotypes are unavailable. Together, the reference panel, benchmarking results, and accompanying lcWGS pipeline establish a validated framework for cost-effective BSF genotyping, enabling downstream applications in population monitoring, diversity assessment, and selective breeding.
Fields of Study & Subjects
Insect Utilization and Effects
Neurobiology and Insect Physiology Research
Identification and Quantification in Food
Publication details
- Journal / source
- Genomics
- Publisher
- Elsevier BV
- Volume
- 118
- Issue
- 2
- Pages
- 111206–111206
- Publication date
- 20 January 2026
- Language
- EN