Machine learning strategies for discovering genetic patterns in complex and rare conditions

Clinically important genetic biomarkers associated with rare disorders are challenging to uncover due to often small sample sizes, high-dimensional variant data, and the presence of complex genetic interactions. Data-driven approaches such as machine learning (ML) methods with feature selection strategies are well-suited to overcome the challenges and can offer key biological insights. We developed GenoMLizer, a genome-wide machine learning (ML) tool for the prioritization of variants as genetic modifiers for the development of symptoms/comorbidities associated with rare disorders. To date, GenoMLizer results have provided novel biological insight and additional support to known genetic associations with symptoms/comorbidities of two diseases, COVID-19 and Turner syndrome.