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bioRxiv · 10.1101/2023.08.08.552417

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

Schoenherr, S.·Schachtl-Riess, J.·Di Maio, S.·Filosi, M.·Mark, M.·Lamina, C.·Fuchsberger, C.·Kronenberg, F.·Forer, L.
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Abstract

MotivationGenome-wide association studies (GWAS) in large biobanks 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 human diseases and traits have been discovered using a GWAS approach. Due to denser genotyping and increasing sample sizes, researchers are increasingly faced with computational challenges when executing GWAS analysis. 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 such as result interpretation and downstream analysis. ResultsHere 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 currently supports single-variant, gene-based and interaction testing. nf-gwas also 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 extensively tested using the unit-style testing framework nf-test to ensure code maintainability, 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. Availabilitynf-gwas is free available at https://github.com/genepi/nf-gwas. Contactlukas.forer@i-med.ac.at

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Schoenherr, S., Schachtl-Riess, J., Di Maio, S., Filosi, M., Mark, M., Lamina, C., Fuchsberger, C., Kronenberg, F., Forer, L.. 2023-08-08. Performing highly parallelized and reproducible GWAS analysis on biobank-scale data. https://doi.org/10.1101/2023.08.08.552417

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