Poster De Conférence Année : 2025

Evaluating low-pass genotyping strategies using PARSEC, a scalable and modular Nextflow pipeline

Résumé

Low-pass sequencing combined with genotype imputation offers a cost-effective alternative to SNP arrays for high-resolution genotyping in large-scale genomic studies. However, it requires specific algorithms and computational resources. We present PARSEC, a modular and scalable Nextflow pipeline for processing low-pass sequencing data and performing genotype imputation using three state-of-the-art tools: Stitch, Beagle, and Glimpse. We used PARSEC on a real-world dataset of 1,050 pig offsprings of 12 founders by subsampling their sequencing data to various depths. PARSEC scales efficiently with large datasets and supports different imputation strategies. Our results show that tools tailored for low-pass data (Stitch and Glimpse) significantly outperform Beagle, a general-purpose imputation tool. Furthermore, we demonstrate that joint imputation of closely related individuals using Stitch is more accurate than using a reference haplotype panel with Glimpse. We also monitored resource usage across all scenarios, confirming that PARSEC can process large cohorts using widely accessible computational infrastructure. PARSEC provides a user-friendly, reproducible, and portable solution for large-scale low-pass sequencing projects. Beyond its practical utility as a bioinformatics pipeline, it also offers methodological insights into low-pass sequencing and imputation strategies. As such, it is a valuable tool both for implementing large-scale studies and for guiding methodological decisions in population genomics and association research.

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Dates et versions

hal-05163312 , version 1 (15-07-2025)

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  • HAL Id : hal-05163312 , version 1

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Cervin Guyomar, Denis Milan, Christophe Klopp, Cécile Donnadieu, Bertrand Servin, et al.. Evaluating low-pass genotyping strategies using PARSEC, a scalable and modular Nextflow pipeline. Intelligent Systems for Molecular Biology/European Conference on Computational Biology, Jul 2025, Liverpool, United Kingdom. ⟨hal-05163312⟩
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