snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data

Christina Vasilopoulou; Benjamin Wingfield; Andrew P Morris; William Duddy

doi:10.12688/f1000research.53821.2

snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data

F1000Res. 2021 Jul 14:10:567. doi: 10.12688/f1000research.53821.2. eCollection 2021.

Authors

Christina Vasilopoulou¹, Benjamin Wingfield², Andrew P Morris³, William Duddy¹

Affiliations

¹ Northern Ireland Centre for Stratified Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK.
² Centre for Personalised Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK.
³ Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK.

Abstract

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

Keywords: Anaconda; BioContainers; GWAS; GWAS pipeline; Genomic Variants; Imputation; Nextflow; Population Stratification; QC; Quality Control; SNPs.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genome*
Genomics*
Humans
Quality Control
Reproducibility of Results
Software

Grants and funding

This work was financed by the EU Regional Development Fund (ERDF) EU Sustainable Competitiveness Programme for Northern Ireland, Northern Ireland Public Health Agency (HSC R&D) & Ulster University. C.V. was the recipient of a DfE international scholarship from Ulster University.