ArticleGigaScience2021
BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of the functionality and usability of open-source rare variant analysis pipelines.Briefings in bioinformatics · 2025Pooled it
- T cell receptor clonotypes predict human leukocyte antigen allele carriage and antigen exposure history.Communications biology · 2026Article
- GWAIS-Web: a free and secure web service for ultra-fast and large-scale genome-wide association interaction studies.Bioinformatics advances · 2026Article
- Industrialization drives convergent microbial and physiological shifts in the human metaorganism.bioRxiv : the preprint server for biology · 2025Article
- Associations of ACE I/D and AGTR1 rs5182 polymorphisms with diabetes and their effects on lipids in an elderly Chinese population.Lipids in health and disease · 2024Article
- Performing highly parallelized and reproducible GWAS analysis on biobank-scale data.NAR genomics and bioinformatics · 2024Article
- The combination of autism and exceptional cognitive ability is associated with suicidal ideation.Neurobiology of learning and memory · 2023Article
- H3AGWAS: a portable workflow for genome wide association studies.BMC bioinformatics · 2022Article
- Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population.Translational psychiatry · 2022Article
- BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.GigaScience · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples.
resultsHere we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs.
conclusionsResearchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.
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