Osteoporosis is an age related disease in which a person's bone slowly becomes weaker with time. The bones may become so weak that they break easily such as a fall from standing height. The most commonly broke bones in osteoporosis are those of the hip, the spine or the wrist. Osteoporosis runs in families meaning that genetic differences explain why some people break bones in old age and other do not. Genetic studies have been done that show the the genes associated with spine (vertebral) fractures (broken bones) and hip fractures are different, suggesting that osteoporosis of the spine is not the exact same disease as osteoporosis of the hip. Genetic studies tell us what part of the genome (i.e. genes) are associated with a disease, but do not tell us how these genes act biologically to cause that disease. In this study, we seek to determine how the genes uniquely associated with spine osteoporosis behave in normal and aged bone, to determine how they interact with each other as a team to impact spine bone. In this study, we will measure gene activity (so called gene expression) in bone samples taken from people undergoing major spine deformity surgery. We will using genetic data from these patients to determine how gene activity is controlled in bone and how that relates to measures of bone health such as bone mineral density data. The results of this study will provide critical data regarding how osteoporosis of the spine happens, and these data will be used to find better and safer treatments to prevent bone fractures of the spine that happen with age.
Study Type
OBSERVATIONAL
Enrollment
550
This is a cross-sectional sample collection study. Vertebral bone tissue that would otherwise be discarded is collected from patients undergoing surgery to correct a spine deformity and gene expression is measure in these tissues.
Univeristy of Colorado Denver
Aurora, Colorado, United States
RECRUITINGGene and transcript quantification
The abundances (in transcripts per million, TPM) of all known transcripts will be quantified in each bone sample via next generation RNA-sequencing.
Time frame: Baseline
Genotypes
Low coverage whole genome sequence data will be obtained from all participants and the yielded outcome will be high quality genotypes for millions of single nucleotide polymorphism (SNPs) across the patient's genome. As this is low coverage genotyping, the coverage rate will be between 1 and 0.4X representation for each spot in the genome per patients, so the data will be imputed to ensure coverage to 1X for all patients. Each patient will be genotyped and therefore, data on a per participant level will be yielded.
Time frame: Baseline
Expression quantitative trait loci (eQTL)
Using the imputed genotyping data and the gene expression data, regions of the genomes where there is local control (so called cis-regulatory elements) of for each gene expressed in bone will be identified. The outcome deliverable will be a list of genes for which there is local genetic control of that gene's expression and the single nucleotide polymorphism(s) likely to be involved. Each entry in this list is an Expression quantitative trait loci (eQTL). This analysis uses all data from all participants in aggregate and the yielded results will be at the level of averages for the cohort, not at the individual participant level.
Time frame: Baseline
Co-localization
Expression quantitative trait loci that co-localize with previously published bone mineral density genome wide association study associations will be identified. Co-localizing genes will be prioritized for additional analyses. This analysis uses all data from all participants in aggregate and the yielded results will be at the level of averages for the cohort, not at the individual participant level.
Time frame: Baseline
Expression-phenotype correlation
For the genes for which a prioritized Expression quantitative trait loci from the co-localization analysis was identified, bone mineral density and other measures of bone mass and quality will be will be tested for correlation with the gene expression measures. This analysis uses all data from all participants in aggregate and the yielded results will be at the level of averages for the cohort, not at the individual participant level.
Time frame: Baseline
Co-expression Network
The participant level gene expression data, as collected from bone samples, will be used to construct networks of gene-gene interaction. This will yield modules of highly correlated gene expression. Bone mineral density data obtained from the patient electronic medical record will be used to identify modules that correlate with bone mineral density.This analysis uses all data from all participants in aggregate and the yielded results will be at the level of averages for the cohort, not at the individual participant level.
Time frame: Baseline
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.