抄録
Genome-wide association studies (GWAS) serve as essential tools for elucidating the genetic architecture of complex traits through systematic analysis of genome-wide genotype-phenotype relationships. The exponential growth of high-throughput sequencing technologies has generated unprecedented volumes of genetic variation data, including single-nucleotide polymorphisms, creating substantial computational challenges for data processing and analysis. This review examines the application of big data technologies in GWAS, encompassing four critical domains: (1) scalable data storage and management systems, (2) high-performance computing frameworks, (3) dimensionality reduction and feature extraction methodologies for large-scale datasets, and (4) machine learning and artificial intelligence algorithms for identifying and predicting genetic variation patterns. Through comprehensive analysis of current big data approaches in GWAS, we address the computational complexity inherent in massive genomic datasets, focusing on methods for detecting epistatic interactions and strategies for multi-dimensional data integration. Furthermore, we explore the transformative potential of AI technologies in precision breeding applications, particularly their implications for crop improvement and personalized agriculture. The primary objective of this work is to advance complex trait genetics research by systematically evaluating big data processing methods and AI technologies in GWAS contexts, ultimately providing novel technical frameworks and methodologies for efficient functional gene identification.
