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

doi:10.1016/j.bonr.2025.101839

Can AI reveal the next generation of high-impact bone genomics targets?

Bone Rep. 2025 Mar 24:25:101839. doi: 10.1016/j.bonr.2025.101839. eCollection 2025 Jun.

Authors

Casey S Greene^{1

2}, Christopher R Gignoux^{1

2}, Marc Subirana-Granés¹, Milton Pividori^{1

2}, Stephanie C Hicks^{3

4

5

6}, Cheryl L Ackert-Bicknell^{1

2

7}

Affiliations

¹ Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
² Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
³ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁴ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
⁵ Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
⁶ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
⁷ Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado School of Medicine, Aurora, CO, USA.

Abstract

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.

Keywords: Artificial intelligence; Genetics; Knowledge graph; Machine learning; Systems biology; Target discovery.

Publication types

Review

Abstract

Publication types

Grants and funding