A whole genome scan for the identification of chromosomal locations of quantitative trait loci (QTL) for equine osteochondrosis (OC) and osteochondrosis dissecans (OCD) was performed in order to identify positional candidate genes (Dierks et al.
2007). The high number of QTL on different chromosomes found for OC and OCD in this study suggests that several genes are possibly involved in the development of OC and OCD but there is no hint on the type of gene action and in which way the different genes may interact with each other.
Potential candidate genes have to code for hormones, enzymes, metabolic factors and/or their receptors involved in the complex of cartilage differentiation and maturation during enchondral ossification, in growth processes, or vascularisation.
Genes causing osteoarthritis in other species can also be used as candidate genes for the molecular genetic analysis of OC in horses. Studies on the variation in gene expression of key chondrogenic genes and genes associated with cartilage pathology between normal and OC chondrocytes may also help to identify candidate genes and their potential role in the pathogenesis of osteochondrosis.
Furthermore, the Equine Articular Cartilage cDNA Library can be helpful to select genes which are at least expressed in cartilage. At the moment, 13,964 equine articular ESTs can be found in the NCBI nucleotide database (http://www.ncbi.nlm.nih.gov/sites/entrez).
Mandatory requirements for evaluating responsible genes for OC were further refinement of the QTL with a higher marker density. The lack of published microsatellites in the era before the horse genome assembly only enabled linkage studies with marker distances of about 20 to 40 cM so that it was not possible to determine exact QTL positions, let alone to detect QTL in regions with a low marker density or insufficient informative markers. A proper knowledge of the QTL helps clarifying how many positional candidate genes have to be tested for linkage disequilibrium with OC and which of these candidate genes may be responsible for OC.
Genome-wide significant QTL on equine chromosome (ECA) 5 and 16 and additionally the QTL on ECA21 were chosen for fine mapping. The release of the horse genome assembly EquCab2 made it possible to identify new microsatellites so
OC and fetlock OCD could be delimited to a region between 76.69 Mb and 92.77 Mb by the use of 49 microsatellites in total.
A QTL for hock OCD on ECA16 extended from 17.60 to 45.18 Mb and a QTL for fetlock OC could be narrowed down to an interval between 6.55 and 24.26 Mb by genotyping 56 microsatellites and 15 single nucleotide polymorphisms (SNPs).
On ECA21, 22 microsatellites were used to refine the QTL for hock OC and hock OCD to a region between 5.45 and 17.14 Mb.
In South German coldblood horses a linkage study for osteochondrosis revealed a QTL on ECA18 (Wittwer et al. 2007). This gave reason for further investigation of ECA18 in Hanoverian warmblood horses. Due to a much more evenly and densely distributed marker set of 27 microsatellites, it was possible to identify a new QTL for OC in fetlock and/or hock joints between 74.94 and 82.25 Mb.
On all mentioned chromosomes a partial consistence of the QTL for hock OC and hock OCD respectively fetlock OC and fetlock OCD leads to the assumption that the same genes may play a role in the development of this disease and that OCD is an aggravated form of OC. However, the genetic influences on the development of fetlock OC and hock OC seem to be due to different loci as the hock QTL do not map at the fetlock QTL. This seems most likely as the genetic correlations between fetlock OC and hock OC were close to zero in trotter horses (Grøndahl and Dolvik 1993), or even negative in Hanoverian warmblood horses (Stock et al. 2005).
Due to their locations in the vicinity of the identified QTL and additionally their indicated role, an amount of putative candidate genes could be identified. The QTL region on ECA5 includes collagen type XXIV, alpha 1 (COL24A1) at 78.1 Mb.
COL24A1 is a marker for embryonic bone formation and may play a role in regulation of type I collagen fibrillogenesis (Koch et al. 2003). Furthermore, Matsuo et al. (2008) found out that COL24A1 is not only expressed in the forming skeleton of the mouse embryo, but also transcribed in the trabecular bone and periosteum of the newborn mouse. Due to its function in bone formation COL24A1 seems to be a suitable functional candidate gene for osteochondrosis.
In the hock QTL on ECA16 candidate genes could be several hyaluronoglucosaminidase genes, HYAL1, HYAL2 and HYAL3 at 36.9 Mb.
Hyaluronidases intracellularly degrade hyaluronan, one of the major
joints.
The QTL region on ECA18 includes a gene which encodes the parathyroid hormone 2 receptor (PTH2R). The exact function of PTH2R is unknown but as parathyroid hormone is a key regulator of calcium metabolism, this gene possibly plays a role in the development of osteochondrosis.
On ECA21 at 5.67 Mb, the PIK3R1 gene encodes for the phosphoinositide-3-kinase regulatory subunit 1. PIK3R1 is a candidate gene for osteoporosis (Huang et al.
2008) and involved in osteoblast differentiation (Zhang et al. 2007) and in the osteoblastic responses to stress (Hamamura et al. 2008). Previous studies demonstrate an involvement of PIK3R1 on molecular mechanisms of bone repair (Li et al. 2007). This gene also seems to be one of the functional candidate genes for osteochondrosis. Further studies are necessary to evaluate their actual influence on the development of osteochondrosis, and if so, to detect functional causative mutations in this genes.
In the course of horse genome sequencing, a SNP collection was compiled which built the basis of a SNP microarray containing ~57,000 SNPs. This array is commercially available since 2008 and offers the opportunity to genotype very fast a large number of SNPs capturing a large variation of the horse genome. A whole genome association analysis was performed using the Illumina Equine SNP50 Beadchip in order to confirm and further refine the QTL and to identify new genomic regions harbouring genes responsible for osteochondrosis in Hanoverian warmblood horses.
The analysis revealed a large number of associated SNPs distributed over the already known QTL regions. Most of the SNPs were located in intergenic regions;
therefore, the detection of the causal mutations for OC could not be expected based on SNP analysis. Further statistical calculations were necessary to refine the QTL regions, so we performed haplotype association and variance analyses. It was possible to show haplotypes in the QTL regions significantly associated with the different phenotypic traits of OC which could highly delimit the genomic regions harbouring QTL for osteochondrosis. Furthermore, SNPs which were included in the haplotypes explained best the proportion of phenotypic variance for the respective trait with values of about 15 to 18%. The hypothesis that the development of OC is
phenotypic variance for the respective traits.
Besides the already known QTL, we could identify further significant regions for the different phenotypic traits on ten different chromosomes, which can possibly increase the explained phenotypic variance for the respective traits. The differences in QTL discoveries among the two methods are likely related to the sample composition and the insufficient marker density in the former whole genome scan so that the power to detect QTL strongly diminishes. It seems that the previously identified QTL are family dependent, as many OC predisposing alleles segregate in different families and possibly only a few OC-alleles are distributed generally in Hanoverian warmblood horses. Using the SNP microarray, common variants will be detected and family specific alleles are at lower chance to be identified.
The present study was able to narrow down QTL regions in the horse genome.
Further studies including more horses are necessary to validate the results, i.e. to verify the significant associated SNPs, also in other breeds than the Hanoverian warmblood horse. To make use of the genotypic information of highly associated SNPs without knowing the causal mutations, marker assisted selection (MAS) can be beneficial. The main advantage of the use of genotypic information is that it is available very early in life. Identification of parents carrying OC predisposing alleles can substantially improve selection for progeny with a reduced risk for OC. This is not only desirable from an economic point of view; it is also desirable from an animal welfare point of view. Selection of horses with regard to OC is currently based on radiological examination. The success of this selection mode was found to be very limited. Summing up, integrating MAS into breeding programs can be a valuable tool to lower the prevalence of OC in horses.
References
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Grøndahl AM and Dolvik NI 1993. Heritability estimations of osteochondrosis in the tibiotarsal joint and of bony fragments in the palmar/plantar portion of the metacarpo- and metatarsophalangeal joints of horses. Journal of American
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Huang QY, Li GHY, Cheung WMW, et al. 2008. Prediction of osteoporosis candidate genes by computational disease-gene identification strategy. J Hum Genet 53, 644-655.
Koch M, Laub F, Zhou P, Hahn RA, Tanaka S, Burgeson RE, Gerecke DR, Ramirez F, Gordon MK 2003. Collagen XXIV, a vertebrate fibrillar collagen with structural features of invertebrate collagens: selective expression in developing cornea and bone. Journal of Biological Chemistry 278, 43236-43244.
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Matsuo N, Tanaka S, Yoshioka H, Koch M, Gordon MK, Ramirez F 2008. Collagen XXIV (Col24a1) gene expression is a specific marker of osteoblast differentiation and bone formation. Connective Tissue Research 49, 68-75.
Stock KF, Hamann H, Distl O 2005. Estimation of genetic parameters for the prevalence of osseous fragments in limb joints of Hanoverian Warmblood horses. Journal of Animal Breeding and Genetics 122, 271-280.
Wittwer C, Löhring K, Drögemüller C, Hamann H, Rosenberger E, Distl O 2007.
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Zhang AX, Yu WH, Ma BF, et al. 2007. Proteomic identification of differently expressed proteins responsible for osteoblast differentiation from human mesenchymal stem cells. Mol Cell Biochem 304, 167-179.
CHAPTER 10
Summary