Identification of Loci Associated with Over-Eating and Obesity in Golden Retriever Dogs

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Introduction

In 2016, the Association for Pet Obesity Prevention conducted a clinical survey that found that an estimated 53.9% of dogs and 58.9% of cats were determined to be clinically overweight or obese by their veterinarian (APOP, 2016). Obesity is defined as an excess accumulation of body fat that results in impairment of health of bodily functions. Overweight and obese dogs can suffer from other health conditions that are the result of their increased adiposity, such as type 2 diabetes mellitus, hyperlipidemia, hypertension and cardiovascular diseases (Osto and Lutz, 2015). An increased amount of adipose tissue causes stress on the joints and produces hormones and other cell-signaling molecules, which may accelerate the development of these obesity-related diseases (Laflamme et. al, 2012). Obesity continues to be an issue today due to the lack of nutritional awareness among pet owners and genetic variants that puts pets in a predisposition for obesity.

If restricted diets and regular exercise are insufficient at keeping dogs at a healthy body weight, clients may turn to pharmaceutical managements. Drugs such as mitratapide and dirlotapide can be used to reduce food intake by causing enzymes in the intestines to inhibit fat absorption and induce the release of a cascade of factors that constrain food intake (Laflamme et. al, 2012). Some breeds may be more susceptible to accruing detrimental amounts of adipose tissue due to genetics, and yet a balanced nutritional diet and physical exercise should be enough to counter their predisposition to become overweight (Mankowska et. al, 2017).

The Golden retriever has a genetic predisposition to obesity due to a deletion in the pro-opiomelanocortin (POMC) gene (Raffan et. al, 2016). The POMC gene codes a protein called pro-opiomelanocortin, produced mainly by the pituitary gland, and it undergoes a wide range of post‐translational cleavage and modification events in specific tissues (Navarro et al 2015). A frameshift mutation in this gene causes a 14-base pair deletion at position 17:19431807-19431821 in exon 3, disrupts the coding sequence, and disrupts the production of β-lipocortin, β-melanocyte stimulating hormone (β-MSH) and β-endorphin (Raffan et. al, 2014, Davison et al 2017). A frameshift mutation is a mutational event that is caused by an insertion or deletion of one or more nucleotides of DNA in a gene, shifting the reading frame of the codon and all the codons that follow the mutation site. β-MSH is a peptide produced by POMC and it binds to melanocortin 4 receptors (MC4R), which sends a signal to the brain that is crucial for energy homeostasis (Abbott et. al, 2000). β-endorphin attaches to the opioid receptors in the brain and stimulates a signal for pain relief, and it may play a role in feeding behavior and weight gain through pleasurable food properties (Mendez et. al, 2015).

Although Labrador retrievers have a higher frequency of mutation for POMC, this experiment will be conducted in Golden retrievers because they are within the same clade. A clade is a group of organisms that include a common ancestor and all the descendants of that ancestor. A clade may include many thousands of species or just a few and it is a method of classification according to the proportion of measurable characteristics that they have in common. It is assumed that the higher the proportion of characteristics that two organisms share, the more recently they diverged from a common ancestor. The Golden retriever was separated from the Flat-coated retriever, a shared ancestor between the Labrador and Golden retriever (Parker et al. 2017). Dogs are unique because the genetic homogeneity within individual breeds is much greater than within distinct human populations. For some breeds, genetic variation has been reduced by bottlenecks and this conservation means that there are few DNA differences between the Labrador and Golden retriever. This provides a great opportunity to study complex traits that would not be possible in human populations because some genome-wide association studies require a much larger sample size to achieve an adequate statistical power (Hong and Park). This conservation within dog DNA allows for smaller sample sizes while maintaining statistical power. (Ostrander and Wayne).

Genome-wide association analyses can be used to study these complex traits. A genome – wide association analysis is a study in which a dense array of genetic markers with a considerable proportion of common variation in genome sequence is analyzed in a set of DNA samples that are informative for a trait of interest, in this case over-eating. The aim is to map susceptibility effects through the detection of associations between genotype frequency and trait status (McCarthy e.t al, 2008). s. GWAS methods are chiefly concerned with determining alleles associated with various SNPs in each study subject, and making statistical comparisons to identify SNPs or genes associated with a particular trait.

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Methodology

The owners of each dog will complete a survey on the behaviors of their dog and that will be used to assess the eating phenotype of the dog. With each question, participants will rate their dog on a scale of always true (1) and never true (5). The numerical score will represent the quantitative trait of appetite and that will be used to identify loci associated with obesity in the genome-wide association analysis. There will be a total of 15 food related behavior questions and a copy of the survey questions can be found in the appendix. The twelve unrelated Golden retrievers will have their gums swabbed using two oral bristle brushes to collect DNA. The oral bristles from each dog will be shipped to Neogen (Lincoln, NE) where DNA will be extracted, and genotyping conducted.

A genome-wide association analysis will be completed with the Illumina 170,000 SNP Canine BeadChip (San Diego, CA) to identify loci that are associated with over-eating in Golden retrievers. This analysis will be continued in a program called SNP and Variation Suite (SVS). Sample statistics in SVS will produce sample call rates over the entire genome and over autosomes only. Low call rates may indicate a discrepancy in the quality of DNA or errors that may have occurred during lab handling. A quality control analysis will be conducted to confirm the accuracy of the genotype data SNPs. The quality control consists of filtering based on SNP call rate, minor allele frequency and Hardy Weinberg equilibrium. Following quality control, a sex check analysis will determine if the individuals’ anatomical and genotypic sex designations matched and an Identity By Descent (IBD) matrix will be conducted to check the data for duplicates or any other relatedness. These quality control methods allow researchers to choose the thresholds and filter out SNPs failing to meet respective quality control measures. The data will also be tested for potential covariates and population stratification through a principal components analysis (PCA). This will identify if allele frequencies differ between two groups due to ancestry differences. The SNPs will then be run through an Efficient Mixed-Model Association eXpedited (EMMAX), and the resulting spreadsheet will be used to create a Manhattan plot and Q-Q plot to better visualize the results. Any markers that reach the threshold of significance in the Manhattan plot will be further investigated. If there is no relationship between the phenotype and the genetic model, the Q-Q plot will display a uniformed line.

A limitation to this research project could be relatedness of the participants. Ideally, the dogs in the study would be related so that a representation of the true population of Golden retrievers can be evaluated. Related individuals can skew the results because alleles can be shared due to their relatedness rather than the phenotype. This can be accounted for in the analysis by a genome relationship matrix.

Another limitation could be that twelve dogs is insufficient to achieve the statistical power to identify an association. The DNA in dogs has been conserved within related breeds or phylogenetic clades. This conservation means that there are few DNA difference among related breeds and it can be exploited in genomic studies to reduce sample size while maintaining statistical power. Genomic associations have been identified with less than twelve dogs, however, these genomic regions had large effects on the phenotype. It is possible that loci with small effects on obesity may not be identified because of the small study population.

A third limitation could stem from the pet owners inaccurately answering the questions on the survey. However, these questions, when answered by dog owners accurately, have been utilized by other researchers to identify genomic associations with behaviors.

Expected Results

The genome-wide association study will identify loci that are associated with over-eating and obesity in Golden retrievers and that one of this loci will include POMC. The identification of these loci will allow owners to identify before their dog is over-weight if they are predisposed to obesity. This can help owners be careful to moderate the amount of food eaten and the amount of exercise given to dogs that have a predisposition to obesity. Moreover, these analyses may provide additional insight into the mechanisms of appetite in dogs.

Bibliography

  1. Abbott, Caroline R., et al. “Investigation of the Melanocyte Stimulating Hormones on Food Intake: Lack of Evidence to Support a Role for the Melanocortin-3-Receptor.” Brain Research, vol. 869, no. 1-2, Elsevier B.V, 2000, pp. 203–10, doi:10.1016/S0006-8993(00)02386-6.
  2. Davison, L. J., et al. “The Canine POMC Gene, Obesity in Labrador Retrievers and Susceptibility to Diabetes Mellitus.” Journal of Veterinary Internal Medicine, vol. 31, no. 2, 2017, pp. 343–48. PubMed Central, doi:10.1111/jvim.14636.
  3. “Home.” Association for Pet Obesity Prevention, https://petobesityprevention.org/. Accessed 1 Mar. 2018.
  4. Laflamme, D. P. “COMPANION ANIMALS SYMPOSIUM: Obesity in Dogs and Cats: What Is Wrong with Being Fat?” Journal of Animal Science, vol. 90, no. 5, May 2012, pp. 1653–62. ntserver1.wsulibs.wsu.edu:2830, doi:10.2527/jas.2011-4571.
  5. Mankowska, M., et al. “Confirmation That a Deletion in the POMC Gene Is Associated with Body Weight of Labrador Retriever Dogs.” Research in Veterinary Science, vol. 112, June 2017, pp. 116–18. ntserver1.wsulibs.wsu.edu:2120, doi: 10.1016/j.rvsc.2017.02.014.
  6. McCarthy, Mark I., et al. “Genome-Wide Association Studies for Complex Traits: Consensus, Uncertainty and Challenges.” Nature Reviews Genetics, vol. 9, no. 5, May 2008, pp. 356–69. EBSCOhost, doi:10.1038/nrg2344.
  7. Mendez, Ian A., et al. “Involvement of Endogenous Enkephalins and β -Endorphin in Feeding and Diet-Induced Obesity.” Neuropsychopharmacology, vol. 40, no. 9, Aug. 2015, pp. 2103–12. www.nature.com, doi:10.1038/npp.2015.67.
  8. Navarro, Sandra, et al. “60 YEARS OF POMC: POMC: An Evolutionary Perspective.” Journal of Molecular Endocrinology, vol. 56, no. 4, 2016, pp. T113-118. PubMed, doi:10.1530/JME-15-0288.
  9. Osto, Melania, and Thomas A. Lutz. “Translational Value of Animal Models of Obesity-Focus on Dogs and Cats.” European Journal of Pharmacology, vol. 759, July 2015, pp. 240–52. PubMed, doi:10.1016/j.ejphar.2015.03.036.
  10. Parker, Heidi G., et al. “Genomic Analyses Reveal the Influence of Geographic Origin, Migration and Hybridization on Modern Dog Breed Development.” Cell Reports, vol. 19, no. 4, Apr. 2017, pp. 697–708. PubMed Central, doi:10.1016/j.celrep.2017.03.079.
  11. Raffan, Eleanor, et al. “A Deletion in the Canine POMC Gene Is Associated with Weight and Appetite in Obesity-Prone Labrador Retriever Dogs.” Cell Metabolism, vol. 23, no. 5, May 2016, pp. 893–900. PubMed Central, doi: 10.1016/j.cmet.2016.04.012.
  12. Raffan, Eleanor, et al. “The Coding Sequence of POMC and Obesity and Appetite in Labrador Retriever Dogs.” The Lancet, vol. 383, no. S1, Elsevier Ltd, 2014, pp. S86–S86, doi:10.1016/S0140-6736(14)60349-4.
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Identification of Loci Associated with Over-Eating and Obesity in Golden Retriever Dogs. (2022, Jun 29). Edubirdie. Retrieved May 21, 2024, from https://edubirdie.com/examples/identification-of-loci-associated-with-over-eating-and-obesity-in-golden-retriever-dogs/
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