Genome Wide Association Study

Genome-wide association studies (GWAS) aim to identify genetic variants associated with specific traits or diseases by analyzing the entire genome of large populations. Current research emphasizes improving the efficiency and accuracy of GWAS through advanced computational methods, including machine learning models like neural networks, and refined statistical techniques such as mixed models and novel multiple testing procedures to handle high-dimensional data and account for confounding factors like population stratification and epistasis. These advancements are crucial for uncovering the complex genetic architecture of diseases, leading to improved diagnostics, therapeutics, and a deeper understanding of human biology.