Using Machine Learning to Link Genotype Data with Phenotypic Traits in Complex Genetic Disorders

Genetic disorders are among the most devastating diseases that affect humans. Yet, several genetic disorders remain incomprehensible to scientists and doctors. Correlating genotype data with phenotypic manifestations has the potential to help scientists better understand complex genetic disorders, such as Fibrodysplasia Ossificans Progressiva (FOP), Wilson’s disease, and Stiff Skin Syndrome. However, if such processes were done manually by humans, they might lead to severe errors or misconceptions and, quite simply, be a waste of time. Computer algorithms and machine learning techniques can be efficiently utilized to analyze genotype data along with phenotypic manifestations, providing scientists with the necessary data to reach accurate conclusions on misunderstood diseases.