Abstract
The study of genetic variants in human populations provides crucial insights into the molecular basis of human diseases, evolution, and adaptation. Computational approaches, including genome-wide association studies (GWAS), next-generation sequencing (NGS), and statistical modeling, are essential tools in identifying and interpreting genetic variants. This article explores the key computational methods used to study genetic variants in human populations, focusing on variant discovery, functional annotation, and population genetic analysis. We also discuss the challenges involved in analyzing large-scale genomic data and the potential of computational tools to advance personalized medicine and disease understanding.
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