AI-Enhanced GWAS Data Analysis by Leverage AI and machine learning models

An attempt was made to develop artificial intelligence (AI), machine learning (ML) integration within the genome-wide association studies (GWAS) to enhance the detection and understanding of genetic variations associated with complex disorders.Although sophisticated machine learning models, such as neural networks and AutoML frameworks are used, these application shows enhancement on prediction accuracy, variant prioritisation and disease risk assessment.Key findings are the discovery of new intronic and non-coding variants surpassing standard GWAS methods and that these regulatory variants are of major importance.AI-enhanced genomics is also our research's contribution to the promise of precision medicine and has the full computational framework for variation identification, functional interpretation, and translation.