Efficient GPU Processing Method to Analyze Large GWAS Data Sets

Genome-wide association studies (GWAS) have ex-panded rapidly, generating large genetic data sets that require advanced computational strategies for efficient analysis. This study introduces a novel method that uses Graphics Processing Units (GPUs) to analyze large scale GWAS data sets, enhancing computational efficiency and reducing processing time. Our approach leverages GPUs' inherent parallel processing capabilities to significantly accelerate computation-intensive tasks commonly encountered in GWAS, such a Genetic Risk Score (GRS). We explain how our GPU-GRS computational approach out-performs traditional CPU (Central Processing Unit) methods, outlining the architectural optimizations that enable parallel processing to handle large genomic data sets more effectively. We also demonstrate the scalability of our approach by ana-lyzing increasingly large synthetic G WAS data sets, showcasing its ability to manage the growing size and complexity of genetic data efficiently. Our findings suggest that adopting GPU-based methods in G WAS analysis can play a pivotal role in the era of big genomic data, offering a path towards more time-efficient and scalable solutions.