Application of an in silico approach identifies a genetic locus within ITGB2, and its interactions with HSPG2 and FGF9, to be associated with anterior cruciate ligament rupture risk.

ABSTRACT We developed a Biomedical Knowledge Graph model that is phenotype and biological function-aware through integrating knowledge from multiple domains in a Neo4j, graph database. All known human genes were assessed through the model to identify potential new risk genes for anterior cruciate ligament (ACL) ruptures and Achilles tendinopathy (AT). Genes were prioritised and explored in a case–control study comparing participants with ACL ruptures (ACL-R), including a sub-group with non-contact mechanism injuries (ACL-NON), to uninjured control individuals (CON). After gene filtering, 3376 genes, including 411 genes identified through previous whole exome sequencing, were found to be potentially linked to AT and ACL ruptures. Four variants were prioritised: HSPG2:rs2291826A/G, HSPG2:rs2291827G/A, ITGB2:rs2230528C/T and FGF9:rs2274296C/T. The rs2230528 CC genotype was over-represented in the CON group compared to ACL-R (p < 0.001) and ACL-NON (p < 0.001) and the TT genotype and T allele were over-represented in the ACL-R group and ACL-NON compared to CON (p < 0.001) group. Several significant differences in distributions were noted for the gene-gene interactions: (HSPG2:rs2291826, rs2291827 and ITGB2:rs2230528) and (ITGB2:rs2230528 and FGF9:rs2297429). This study substantiates the efficiency of using a prior knowledge-driven in silico approach to identify candidate genes linked to tendon and ACL injuries. Our biomedical knowledge graph identified and, with further testing, highlighted novel associations of the ITGB2 gene which has not been explored in a genetic case control association study, with ACL rupture risk. We thus recommend a multistep approach including bioinformatics in conjunction with next generation sequencing technology to improve the discovery potential of genomics technologies in musculoskeletal soft tissue injuries. Highlights A biomedical knowledge graph was modelled for musculoskeletal soft tissue injuries to efficiently identify candidate genes for genetic susceptibility analyses. The biomedical knowledge graph and sequencing data identified potential biologically relevant variants to explore susceptibility to common tendon and ligament injuries. Specifically genetic variants within the ITGB2 and FGF9 genes were associated with ACL risk. Novel allele combinations (HSPG2-ITGB2 and ITGB2-FGF9) showcase the potential effect of ITGB2 in influencing risk of ACL rupture.