Machine learning-assisted genome-wide association study

Machine learning-assisted GWAS integrates classical genome-wide association testing with machine learning models to improve the detection of genetic variants associated with complex traits. Where traditional GWAS tests each single nucleotide polymorphism (SNP) independently using linear or logistic regression, ML-GWAS captures non-linear interactions and epistasis, ranks candidate loci more accurately, and reduces the false discovery burden in large biobank datasets. The approach has become increasingly prominent as sample sizes and genomic complexity outpace the assumptions of conventional single-SNP tests.