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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million

Alex Rodriguez¹, Youngdae Kim², Tarak Nath Nandi¹

¹Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA.

Biorxiv : the Preprint Server for Biology

|June 3, 2024

Summary

This summary is machine-generated.

Optimized the Scalable and Accurate Implementation of Generalized Mixed Model (SAIGE) algorithm using GPU-based distributed computing for large-scale genomic analysis. This approach accelerated genome-wide association studies (GWAS) by 20-fold, enabling efficient analysis of massive biobank datasets.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Biobanks have accelerated genomic discoveries but present computational challenges for large-scale data analysis.
Existing statistical frameworks struggle with the extensive matrix operations required for massive genomic datasets.

Purpose of the Study:

To introduce computational optimizations for the SAIGE algorithm.
To enable efficient large-scale genome-wide association studies (GWAS) using GPU-based distributed computing.

Main Methods:

Implemented GPU-based distributed computing optimizations for the SAIGE algorithm.
Applied optimized SAIGE to conduct GWAS on 2,068 phenotypes from the Veterans Affairs Million Veteran Program (MVP) dataset (635,969 participants).
Utilized the Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC) for scaling the analysis.

Main Results:

Achieved a 20-fold acceleration in GWAS analysis compared to the baseline SAIGE model.
Successfully scaled the analysis to over 6,000 nodes on the Summit HPC.
Demonstrated significant time and cost benefits using a Docker container of the optimizations on UK Biobank and All of Us datasets.

Conclusions:

Computational optimizations, particularly GPU-based distributed computing, significantly enhance the scalability and efficiency of the SAIGE algorithm for large-scale genomic analyses.
The optimized SAIGE approach provides substantial computational advantages for biobank-scale GWAS, facilitating deeper genomic discoveries.
The provided Docker container ensures broad applicability and cost-effectiveness across different cloud infrastructures and datasets.