Osteochondrosis (OC), simply defined as a failure of endochondral ossification, is a complex disease with both genetic and environmental risk factors that is commonly diagnosed in young horses, as well as other domestic species. Although up to 50 % of the risk for developing OC is reportedly inherited, specific genes and alleles underlying risk are thus far completely unknown. Regions of the genome identified as associated with OC vary across studies in different populations of horses. In this study, we used a cohort of Standardbred horses from the U.S. (n = 182) specifically selected for a shared early environment (to reduce confounding factors) to identify regions of the genome associated with tarsal OC. Subsequently, putative risk variants within these regions were evaluated in both the discovery population and an independently sampled validation population of Norwegian Standardbreds (n = 139) with tarsal OC. After genome-wide association analysis of imputed data with information from >200,000 single nucleotide polymorphisms, two regions on equine chromosome 14 were associated with OC in the discovery cohort. Variant discovery in these and 30 additional regions of interest (including 11 from other published studies) was performed via whole-genome sequencing. 240 putative risk variants from 10 chromosomes were subsequently genotyped in both the discovery and validation cohorts. After correction for population structure, gait (trot or pace) and sex, the variants most highly associated with OC status in both populations were located within the chromosome 14 regions of association. The association of putative risk alleles from within the same regions with disease status in two independent populations of Standardbreds suggest that these are true risk loci in this breed, although population-specific risk factors may still exist. Evaluation of these loci in other populations will help determine if they are specific to the Standardbred breed, or to tarsal OC or are universal risk loci for OC. Further work is needed to identify the specific variants underlying OC risk within these loci. This is the first step towards the long-term goal of constructing a genetic risk model for OC that allows for genetic testing and quantification of risk in individuals.

Background: There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe⁺ signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic changes associated with disease, specifically inflammatory conditions, often related to serious diseases such as cancer or COVID-19 infection. Relatively complicated, yet very efficient experimental methods have been introduced recently (DIRE, JEDI) to suppress the rest of the spectrum, thus allowing measurement of these integrals of interest. Methods: In this study, we introduce a simple alternative processing method using CRAFT (Complete Reduction to Amplitude-Frequency Table), a time-domain (FID) analysis tool which can highlight selected subsets of the spectrum by choice for quantitative analysis. The output of this approach is a direct, spreadsheet-based representation of the required peak amplitude (integral) values, ready for comparative analysis, completely avoiding all the convectional data processing and manipulation steps. The significant advantage of this alternative method is that it only needs a simple water-suppressed 1D spectrum with no further experimental manipulation whatsoever. In addition, there are no pre/post processing steps (such as baseline and/or phase), further minimizing potential dependency on subjective decisions by the user and providing an opportunity to automate the entire process. Results: We applied this methodology to horse serum samples to follow the presence of inflammation for cohorts with or without OCD (Osteochondritis Dissecans) conditions and find diagnostic separation of the of the cohorts through statistical methods. Conclusions: The powerful and simple CRAFT-based approach is suitable to extract selected biomarker information from complex NMR spectra and can be similarly applied to any other biofluid from any source or sample, also retrospectively. There is a potential to extend such a simple analysis to other, previously identified relevant markers as well.