Computational Biology Approaches to Studying Neurogenetics

Abstract

Neurogenetics is a rapidly growing field that investigates the genetic factors underlying neurological and psychiatric disorders. Computational biology approaches have revolutionized neurogenetic research by providing tools to analyze large-scale genomic, transcriptomic, and proteomic data. These approaches help identify genetic variations, gene expression patterns, and molecular networks that contribute to diseases such as Alzheimer's, Parkinson's, and autism. This article explores computational biology techniques, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and machine learning, in the study of neurogenetics. We discuss the integration of multi-omics data, challenges in data interpretation, and the potential of computational biology to advance our understanding of neurogenetic disorders.