WxS-QC - a quality control pipeline for human germline short-variant Whole-Genome and Whole-Exome cohorts for population-scale analyses.

Authors

Gennadii Zakharov, Ruth Eberhardt, Alina Frolova, Dmytro Horyslavets, Eric Hidari, Iaroslav Popov, Joanna Dennehy, Mykhailo Koreshkov, Pavlos Antoniou, Valeriia Kantsypa, Vladimir Ovchinnikov, Vivek Iyer

Published in

Bioinformatics (Oxford, England). Jul 20, 2026. Epub Jul 20, 2026.

Abstract

Whole-exome (WES) and whole-genome (WGS) sequencing are rapidly becoming preferred methods for population-scale analysis of the human genetic landscape. However, there are currently no standardized quality control (QC) pipelines for human WES and WGS datasets.In this paper, we present WxS-QC, a powerful, scalable, and convenient pipeline for the QC of human germline short-variant WGS and WES cohorts for population-scale analyses. Our pipeline is suitable for both rare-variant discovery and common-variant association studies. It is based on deeply refactored gnomAD v3 and v4 quality control pipelines, contains several methods we have developed de novo, and is aligned with current best practices in WGS/WES germline cohort QC.We provide all methods in a single codebase, aligned to work together and controlled via a single YAML config, with automatic export of resulting graphs and summary tables, excellent performance and scalability, and comprehensive documentation. The pipeline can run in any UNIX-like environment and can efficiently process cohorts of up to 200,000 whole-exome samples, with the potential to handle bigger datasets.
The pipeline code is written in Python using the Hail library and is freely available under the BSD-3 license here: https://github.com/wtsi-hgi/wxs-qc. The detailed description of the pipeline is available in the pipeline documentation: https://github.com/wtsi-hgi/wxs-qc/blob/main/README.md. We also provide an open dataset with all required metadata, which is available at https://wxs-qc-data.cog.sanger.ac.uk/wxs-qc_public_dataset_v3.tar. An example of test dataset analysis is available in the supplementary materials.
Supplementary data are available at Bioinformatics online.

PMID:
42475502
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.

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