Abstract
Genome-wide association studies (GWAS) have become a cornerstone of modern genetics, enabling researchers to identify genetic variants associated with complex traits and diseases. Recent advances in GWAS methodologies, such as the use of large-scale datasets and advanced statistical models, have enhanced the ability to uncover novel genetic loci and provide insights into the genetic architecture of human diseases. This article reviews the current trends in GWAS, focusing on the role of big data, population diversity, and integration with other omics data. The article also explores future directions in GWAS, including the application of machine learning, multi-omics integration, and the study of rare variants in understanding complex traits and diseases
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