Molecular Epidemiological Survey for Degenerative Myelopathy in German Shepherd Dogs in Japan: Allele Frequency and Clinical Progression Rate.

Canine degenerative myelopathy (DM) is an adult-onset, chronic, progressive neurodegenerative disease reported in multiple canine breeds, including the German Shepherd Dog (GSD). Clinical signs include progressive motor neuron paralysis, which begins in the pelvic limbs and eventually leads to respiratory distress, which may necessitate euthanasia. A common DM-associated mutation is a single nucleotide substitution that causes an amino acid substitution (c.118G>A, p.E40K) in the canine SOD1 gene. This SOD1 mutation and the clinical progression rate of A/A risk genotype in the Japanese GSD population have not been analyzed before. Therefore, the aim of this study was to determine the frequency of the mutated allele and analyze the clinical progression rate in the Japanese GSD population. We studied 541 GSDs registered with the Japanese German Shepherd Dog Registration Society between 2000 and 2019. Genotyping was performed using real-time PCR with DNA extracted from the hair roots of each dog. The study revealed 330 G/G dogs (61%), 184 G/A dogs (34%), and 27 A/A dogs (5%), indicating a frequency of the mutant allele of 0.220, which are in Hardy−Weinberg equilibrium. We analyzed the clinical signs in A/A dogs with an age limit of 10 years based on information obtained from the dogs' owners. Of the seven A/A dogs older than 10 years, owners reported DM-related clinical signs, indicating a clinical progression rate of 100%. These results, further genotyping, and thorough clinical examinations of SOD1 A/A risk genotype will help control and prevent DM in the Japanese GSD population.

Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods.

BACKGROUND: A Bayesian approach based on a Dirichlet process (DP) prior is useful for inferring genetic population structures because it can infer the number of populations and the assignment of individuals simultaneously. However, the properties of the DP prior method are not well understood, and therefore, the use of this method is relatively uncommon. We characterized the DP prior method to increase its practical use. RESULTS: First, we evaluated the usefulness of the sequentially-allocated merge-split (SAMS) sampler, which is a technique for improving the mixing of Markov chain Monte Carlo algorithms. Although this sampler has been implemented in a preceding program, HWLER, its effectiveness has not been investigated. We showed that this sampler was effective for population structure analysis. Implementation of this sampler was useful with regard to the accuracy of inference and computational time. Second, we examined the effect of a hyperparameter for the prior distribution of allele frequencies and showed that the specification of this parameter was important and could be resolved by considering the parameter as a variable. Third, we compared the DP prior method with other Bayesian clustering methods and showed that the DP prior method was suitable for data sets with unbalanced sample sizes among populations. In contrast, although current popular algorithms for population structure analysis, such as those implemented in STRUCTURE, were suitable for data sets with uniform sample sizes, inferences with these algorithms for unbalanced sample sizes tended to be less accurate than those with the DP prior method. CONCLUSIONS: The clustering method based on the DP prior was found to be useful because it can infer the number of populations and simultaneously assign individuals into populations, and it is suitable for data sets with unbalanced sample sizes among populations. Here we presented a novel program, DPART, that implements the SAMS sampler and can consider the hyperparameter for the prior distribution of allele frequencies to be a variable.

Unusual death of a baby: a dog attack and confirmation using human and canine STRs.

We performed an autopsy on a 3-month-old baby boy who had only one area severe and extensive wound to his head and face. Three unrelated miniature dachshunds were in the house. After our investigation, we were able to confirm that the wound had in fact been caused by a dog attack, and we were able to identify the offending dog among the three dogs using both human and canine short tandem repeat obtained from samples taken from the suspected dog and from the scene of the attack.