Can AI reveal the next generation of high-impact bone genomics targets? - Casey S. Greene; Christopher R. Gignoux; Marc Subirana-Granés; Milton Pividori; Stephanie C. Hicks; Cheryl L. Ackert-Bicknell (Bone Reports)
Genetic studies have revealed hundreds of loci associated with bone-related phenotypes, including bone mineral density (BMD) and fracture risk. However, translating discovered loci into effective new therapies remains challenging. We review success stories including PCSK9-related drugs in cardiovascular disease and evidence supporting the use of human genetics to guide drug discovery, while highlighting advances in artificial intelligence and machine learning with the potential to improve target discovery in skeletal biology. These strategies are poised to improve how we integrate diverse data types, from genetic and electronic health records data to single-cell profiles and knowledge graphs. Such emerging computational methods can position bone genomics for a future of more precise, effective treatments, ultimately improving the outcomes for patients with common and rare skeletal disorders.