Sequencing & Pangenomes

Pool-Seq Framework for Robust SNP Panel Design

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Abstract

Pool sequencing (Pool-Seq) has emerged as a cost-effective and efficient approach in population genetics, particularly when balancing sequencing cost with population coverage and when the objective is to select markers based on allele-frequency patterns. However, translating these data into reliable genotyping resources remains challenging. To address this limitation, a streamlined framework was developed for SNP panel design directly from Pool-Seq datasets by integrating stringent variant filtering, accurate allele-frequency estimation, innovative marker-selection strategies, and validation using individual-based genotypes. Empirical study one, focused on marker discovery for a local fish strain, utilized four equimass-standardized pools (~60 individuals per pool) sequenced to a realized coverage of 166à—. Raw reads were processed through the PoolParty workflow for trimming, alignment, duplicate handling, and mapping-quality filtering prior to allele-frequency estimation. Using Poolfstat and BayPass, 103 informative markers (average Fst = 0.049) were identified as capable of distinguishing local strains despite an overall homogenized genetic structure (pairwise Fst = -0.0141). Validation using individual genotypes demonstrated the feasibility of Pool-Seq-based marker design, with 97 of 103 spike-in markers surpassing call-rate QC thresholds. Application of a machine-learning classifier to these markers produced 61-73% accuracy depending on population composition. Empirical study two, addressing hybrid detection and parentage analysis, is ongoing; Pool-Seq data from 16 pools representing parental strains and hybrid offspring are currently being processed through the PoolParty workflow as the initial step toward the discovery of diagnostic markers. Together, these results indicate that Pool-Seq, when combined with rigorous filtering and targeted validation, provides a robust foundation for developing accurate and transferable SNP panels. This framework offers a practical approach for programs seeking cost-effective genomic tools to support selective breeding, stock management, and conservation in aquatic species.

Keywords: 2026

How to Cite:

Zhao, H., Allen, M., Mashruwala, D., Stannard, J. & Verbyla, K., (2026) “Pool-Seq Framework for Robust SNP Panel Design”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286811. doi: https://doi.org/10.31274/wcgalp.24130

Rights: 1