Authors: Ryan A. Grant, MD; Michael Zaleski, BS; Yanfei Zhang, PhD; Vida Abedi, PhD; Manu Shivakumar, MS; Jonathan Slotkin; Ming Ta Lee, PhD (Lewisburg, PA)

The Geisinger MyCode initiative is one of the world’s largest biorepositories with integrated longitudinal electronic health record (EHR) linked to genetic data.  Currently, approximately 92,000 MyCode participants have high density single nucleotide polymorphism (SNP) array and whole exome sequence data. Leveraging this multimodal dataset, we can identify powerful associations and advance genomics-guided therapies. Spinal conditions account for the third largest United States healthcare expenditure, yet the genetics underlying degenerative spine conditions have not been significantly explored.

We identified all patients (n = 3,985) with an EHR diagnosis of spinal osteoarthritis or spondylosis who had genotype data available as part of our DiscovEHR collaboration with the Regeneron Genetics Center. We also identified 12,257 controls who did not have any diagnosis of osteoarthritis or degeneration. We sought to identify SNPs associated with spinal osteoarthritis or spondylosis using logistic regression in a genome-wide association analysis. 

Across the 3,985 cases and 12,257 controls, 39 SNPs demonstrated suggestive association with p-values < 5 * 10-5. One SNP (rs9379137) was below the threshold for genome-wide significance (p = 1.203 * 10-8). This SNP is located in the coding region of the bone morphogenetic protein-6 (BMP-6) gene. BMP-6 belongs to the transforming growth factor-beta family and has been shown to be involved in bone and cartilage growth.

We describe the use of a massive database of genotypes combined with phenotypic EHR data to identify an association between the BMP-6 gene and the development of spinal osteoarthritis.  We are expanding this analysis to the larger 150,000 participant MyCode database. Long-term, we will define precision medicine relationships between the genome and the development of degenerative spinal conditions, as well as identify those patients most likely to respond to surgery and other therapies.