Performing highly parallelized and reproducible GWAS analysis on biobank-scale data

• Published in: NAR genomics and bioinformatics Vol. 6; no. 1; p. lqae015
• Main Authors: Schönherr, Sebastian, Schachtl-Riess, Johanna F, Di Maio, Silvia, Filosi, Michele, Mark, Marvin, Lamina, Claudia, Fuchsberger, Christian, Kronenberg, Florian, Forer, Lukas
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.03.2024
• Subjects: Biobanks; Genome-wide association studies; Genomic analysis; Genotypes; Phenotypes
• ISSN: 2631-9268; 2631-9268
• DOI: 10.1093/nargab/lqae015

Genome-wide association studies (GWAS) are transforming genetic research and enable the detection of novel genotype-phenotype relationships. In the last two decades, over 60 000 genetic associations across thousands of traits have been discovered using a GWAS approach. Due to increasing sample sizes, researchers are increasingly faced with computational challenges. A reproducible, modular and extensible pipeline with a focus on parallelization is essential to simplify data analysis and to allow researchers to devote their time to other essential tasks. Here we present nf-gwas, a Nextflow pipeline to run biobank-scale GWAS analysis. The pipeline automatically performs numerous pre- and post-processing steps, integrates regression modeling from the REGENIE package and supports single-variant, gene-based and interaction testing. It includes an extensive reporting functionality that allows to inspect thousands of phenotypes and navigate interactive Manhattan plots directly in the web browser. The pipeline is tested using the unit-style testing framework nf-test, a crucial requirement in clinical and pharmaceutical settings. Furthermore, we validated the pipeline against published GWAS datasets and benchmarked the pipeline on high-performance computing and cloud infrastructures to provide cost estimations to end users. nf-gwas is a highly parallelized, scalable and well-tested Nextflow pipeline to perform GWAS analysis in a reproducible manner.