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

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) have identified over 60,000 genetic associations across numerous traits in the past two decades.
• Increasing sample sizes in GWAS present significant computational challenges for researchers.
• Reproducible, modular, and parallelized analysis pipelines are crucial for efficient GWAS data processing.

Purpose of the Study:

• To introduce nf-gwas, a Nextflow pipeline for biobank-scale genome-wide association studies.
• To provide a robust and scalable solution for complex GWAS analysis.
• To facilitate the discovery of novel genotype-phenotype relationships.

Main Methods:

• nf-gwas is a Nextflow pipeline automating pre- and post-processing steps for GWAS.
• Integrates REGENIE for regression modeling, supporting single-variant, gene-based, and interaction testing.
• Features automated reporting with interactive Manhattan plots and is tested with nf-test.

Main Results:

• The pipeline automates extensive pre- and post-processing for GWAS.
• Supports diverse testing methods including single-variant, gene-based, and interaction analyses.
• Offers interactive reporting for thousands of phenotypes and validated performance on HPC and cloud platforms.

Conclusions:

• nf-gwas provides a highly parallelized, scalable, and reproducible solution for biobank-scale GWAS.
• The pipeline simplifies complex genetic data analysis, allowing researchers to focus on biological interpretation.
• Automated testing and validation ensure reliability for clinical and pharmaceutical applications.