  Motivation: Quality control of genomic data is an essential but complicated
multi-step procedure, often requiring separate installation and expert
familiarity with a combination of disparate bioinformatics tools. Results: To
provide an automated solution that retains comprehensive quality checks and
flexible workflow architecture, we have developed snpQT, a scalable,
stand-alone software pipeline, offering some 36 discrete quality filters or
correction steps, with plots before-and-after user-modifiable thresholding.
This includes build conversion, population stratification against 1,000 Genomes
data, population outlier removal, and built-in imputation with its own pre- and
post- quality controls. Common input formats are used and users need not be
superusers nor have any prior coding experience. 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. Availability: snpQT is open source and freely available at
https://github.com/nebfield/snpQT Contact<EMAIL_ADDRESS><EMAIL_ADDRESS>