Osteochondrosis (OC) is a developmental orthopedic disorder in young, growing horses, where irregular bone formation in the joints leads to necrotic areas, resulting in chronic or recurrent lameness. Incidence of OC varies considerably among breeds, and displays a multifactorial mode of inheritance. The incidence of \(\mathrm{OC}\) is rising in the population of race horses. Discuss the reasons why the incidence of OC might be rising, and describe what can be done to detect OC susceptibility in horses with the help of QTL analysis.

There could be various reasons for OC incidence rising in racehorses, including genetic factors, breeding practices, and environmental factors. Some possible reasons might include: 1. Selective breeding: Breeding practices that focus on certain traits, like speed and endurance, may inadvertently select for genes associated with OC. This could lead to an increase in the incidence of OC in the racehorse population over time. 2. Increased stress on joints: Racehorses are often subjected to intense physical training and competition, which puts pressure on their joints. This additional stress could contribute to the development of OC. 3. Nutritional factors: Changes in feeding practices or diets could impact the development of OC in horses, especially considering the crucial role of nutrition in the growth of bone and cartilage.

QTL analysis is a statistical method that can be used to identify the genomic regions associated with a quantitative trait, like the susceptibility to OC in horses. Here is a step-by-step description of how to detect OC susceptibility with the help of QTL analysis: 1. Phenotypic data collection: Collect phenotypic data on a sample of horses, including information on the presence or absence of OC, as well as other quantitative traits that may be related to OC susceptibility. 2. Genotyping: Obtain genotypic data for the same sample of horses using high-density SNP (Single Nucleotide Polymorphism) genotyping arrays. This will provide information on each horse's genetics, which can be used in conjunction with the phenotypic data to detect potential associations. 3. Mapping: Perform linkage analysis to identify regions of the genome that may be associated with OC susceptibility. This involves examining the co-segregation of SNP markers with the OC trait to identify regions that are likely to harbor genes responsible for susceptibility. 4. Statistical analysis: Use statistical methods, such as linear regression or mixed models, to test the association between individual SNP markers and OC susceptibility, while accounting for the relatedness among individuals and population structure. 5. Validation: Validate the identified QTL regions by performing functional studies or by examining their role in independent horse populations. This can help to confirm the associations and narrow down the candidate genes for further investigation. 6. Identification of candidate genes: Analyze the QTL regions to identify candidate genes that might be responsible for OC susceptibility. This can be done through bioinformatics analyses, gene expression studies, or functional studies in model organisms. Ultimately, QTL analysis can contribute to a better understanding of the genetic factors underlying OC susceptibility in horses, and ultimately help in the development of breeding and management strategies to reduce the prevalence of this debilitating condition in racehorses.