Harnessing Artificial Intelligence in Genomics for the Prevention of Recessive Disorders: An Overview

Recessive genetic disorders, frequently concealed within heterozygous carriers, reveal a substantial challenge in context of clinical genetics owing to their asymptomatic characteristics in carriers and the profound consequences when transmitted in a bi-allelic manner. These disorders contribute significantly to the global burden of inherited diseases, with prevalence influenced by ethnicity, population genetics, and consanguinity patterns. The emergence of next-generation sequencing has facilitated the accessibility of extensive genomic data; nonetheless, the intricacies of interpretation continue to present a significant impediment. The fields of artificial intelligence (AI) and machine learning are now transforming the genomic landscape by facilitating comprehensive analyses of genomic variants, amalgamating phenotype data, and forecasting disease risks with enhanced speed and precision. This review examines the contemporary AI-driven methodologies employed in the prevention of recessive disorders through carrier screening, embryo selection, and extensive population analyses. We reference recent advancements, including AI systems such as PhenIX, X rare, Deep Variant, and prioritization frameworks based on GPT-4. Additionally, we address ethical considerations, challenges pertaining to clinical translation, and the potential of generative artificial intelligence in the context of genetic counseling. By scrutinizing both the technical evolution and translational significance, this review positions artificial intelligence as an indispensable instrument in predictive and preventive genomic medicine. However, the integration of artificial intelligence in genomics is not without limitations, including algorithmic bias, data privacy concerns, and underrepresentation of diverse populations in training datasets. These challenges may affect diagnostic accuracy and equitable clinical implementation, underscoring the need for careful validation and ethical oversight.