, 15:123 | Cite as

Genome-wide association studies of 74 plasma metabolites of German shepherd dogs reveal two metabolites associated with genes encoding their enzymes

  • Pamela Xing Yi Soh
  • Juliana Maria Marin Cely
  • Sally-Anne Mortlock
  • Christopher James Jara
  • Rachel Booth
  • Siria Natera
  • Ute Roessner
  • Ben Crossett
  • Stuart Cordwell
  • Mehar Singh Khatkar
  • Peter WilliamsonEmail author
Original Article



German shepherd dogs (GSDs) are a popular breed affected by numerous disorders. Few studies have explored genetic variations that influence canine blood metabolite levels.


To investigate genetic variants affecting the natural metabolite variation in GSDs.


A total of 82 healthy GSDs were genotyped on the Illumina CanineHD Beadchip, assaying 173,650 markers. For each dog, 74 metabolites were measured through liquid and gas chromatography mass spectrometry (LC–MS and GC–MS) and were used as phenotypes for genome-wide association analyses (GWAS). Sliding window and homozygosity analyses were conducted to fine-map regions of interest, and to identify haplotypes and gene dosage effects.


Summary statistics for 74 metabolites in this population of GSDs are reported. Forty-one metabolites had significant associations at a false discovery rate of 0.05. Two associations were located around genes which encode for enzymes for the relevant metabolites: 4-hydroxyproline was significantly associated to D-amino acid oxidase (DAO), and threonine to l-threonine 3-dehydrogenase (LOC477365). Three of the top ten haplotypes associated to 4-hydroxyproline included at least one SNP on DAO. These haplotypes occurred only in dogs with the highest 15 measurements of 4-hydroxyproline, ranging in frequency from 16.67 to 20%. None of the dogs were homozygous for these haplotypes. The top two haplotypes associated to threonine included SNPs on LOC477365 and were also overrepresented in dogs with the highest 15 measurements of threonine. These haplotypes occurred at a frequency of 90%, with 80% of these dogs homozygous for the haplotypes. In dogs with the lowest 15 measurements of threonine, the haplotypes occurred at a frequency of 26.67% and 0% homozygosity.


DAO and LOC477365 were identified as candidate genes affecting the natural plasma concentration of 4-hydroxyproline and threonine, respectively. Further investigations are needed to validate the effects of the variants on these genes.


Canine Plasma Metabolomics Genetics GWAS 



This work was supported by the Canine Research Foundation. This research is supported by an Australian Government Research Training Program (RTP) Scholarship. Metabolites were extracted and analysed from plasma at Metabolomics Australia (School of BioSciences, University of Melbourne, Australia), a National Collaborative Research Infrastructure Strategy (NCRIS) initiative under Bioplatforms Australia, Pty Ltd. The authors would like to thank Himasha Mendis, Nirupama Jayasinghe and Alice Ng from Metabolomics Australia who extracted and analysed metabolites. The authors would also like to thank the owners and dogs that donated samples for this study.

Author Contributions

PXYS performed research, analysed the data, and wrote the manuscript. JMMC, CJJ, and SM contributed to performing research, analysis of data, and writing the manuscript. SM, RB, BC and SC collected the samples and data, and conceived the study. MSK contributed in the analysis, interpretations and writing. UR and SN advised on metabolomics analysis and contributed to writing. PW conceived the study, performed research, contributed to the analysis of the data and writing of the manuscript.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical Approval

All protocols in this study was conducted in accordance with the guidelines of the Animal Research Act, NSW, Australia, approved by the University of Sydney’s Animal Ethics Committee under protocols 444 and 4949.

Supplementary material

11306_2019_1586_MOESM1_ESM.docx (18 kb)
Supplementary material 1—ESM_1: Details on plasma preparation, machines used, and analysis methods for LC–MS and GC–MS for amines, sugars, organic acids, and fatty acids (DOCX 18 kb)
11306_2019_1586_MOESM2_ESM.pdf (206 kb)
Supplementary material 2—ESM_2: Summary statistics of amino acids. Outliers were defined as measurements beyond 4 standard deviations from the mean (PDF 205 kb)
11306_2019_1586_MOESM3_ESM.pdf (158 kb)
Supplementary material 3—ESM_3: Summary statistics of fatty acids. Outliers were defined as measurements beyond 4 standard deviations from the mean (PDF 158 kb)
11306_2019_1586_MOESM4_ESM.pdf (166 kb)
Supplementary material 4—ESM_4: Summary statistics of sugars. Outliers were defined as measurements beyond 4 standard deviations from the mean (PDF 166 kb)
11306_2019_1586_MOESM5_ESM.pdf (78 kb)
Supplementary material 5—ESM_5: Summary statistics of organic acids. Outliers were defined as measurements beyond 4 standard deviations from the mean (PDF 78 kb)
11306_2019_1586_MOESM6_ESM.pdf (266 kb)
Supplementary material 6—ESM_6: Correlation plots for all metabolites, and for each group of metabolites – amino acids, fatty acids, sugars and organic acids respectively. Complete observations and Spearman’s rank correlation were used from the dataset adjusted for normality and pruned for outliers (PDF 266 kb)
11306_2019_1586_MOESM7_ESM.pdf (8.5 mb)
Supplementary material 7—ESM_7: Genome-wide Manhattan plots of amino acids using output from the mixed linear model analysis. Red line indicates a genome-wide q-value cut-off of 0.05 (PDF 8702 kb)
11306_2019_1586_MOESM8_ESM.pdf (495 kb)
Supplementary material 8—ESM_8: Manhattan plots of significant chromosome associations for amino acids at a chromosome-wide q-value cut-off of 0.05. Where present, red line indicates a q-value cut-off of 0.05, blue line indicates a q-value cut-off of 0.01 (PDF 494 kb)
11306_2019_1586_MOESM9_ESM.pdf (4.4 mb)
Supplementary material 9—ESM_9: Genome-wide Manhattan plots of fatty acids using output from the mixed linear model analysis. Red line indicates a genome-wide q-value cut-off of 0.05 (PDF 4513 kb)
11306_2019_1586_MOESM10_ESM.pdf (254 kb)
Supplementary material 10—ESM_10: Manhattan plots of significant chromosome associations for fatty acids at a chromosome-wide q-value cut-off of 0.05. Where present, red line indicates a q-value cut-off of 0.05, blue line indicates a q-value cut-off of 0.01 (PDF 254 kb)
11306_2019_1586_MOESM11_ESM.pdf (5.9 mb)
Supplementary material 11—ESM_11: Genome-wide Manhattan plots of sugars using output from the mixed linear model analysis. Red line indicates a genome-wide q-value cut-off of 0.05 (PDF 6025 kb)
11306_2019_1586_MOESM12_ESM.pdf (136 kb)
Supplementary material 12—ESM_12: Manhattan plots of significant chromosome associations for sugars at a chromosome-wide q-value cut-off of 0.05. Where present, red line indicates a q-value cut-off of 0.05, blue line indicates a q-value cut-off of 0.01 (PDF 136 kb)
11306_2019_1586_MOESM13_ESM.pdf (2.9 mb)
Supplementary material 13—ESM_13: Genome-wide Manhattan plots of organic acids using output from the mixed linear model analysis. Red lines indicate a genome-wide q-value cut-off of 0.05 (PDF 3010 kb)
11306_2019_1586_MOESM14_ESM.pdf (247 kb)
Supplementary material 14—ESM_14: Manhattan plots of significant chromosome associations for organic acids at a chromosome-wide q-value cut-off of 0.05. Where present, red line indicates a q-value cut-off of 0.05, blue line indicates a q-value cut-off of 0.01 (PDF 246 kb)
11306_2019_1586_MOESM15_ESM.xlsx (216 kb)
Supplementary material 15—ESM_15: Significant SNPs at a chromosome-wide q-value cut off of 0.05 (XLSX 215 kb)
11306_2019_1586_MOESM16_ESM.xlsx (926 kb)
Supplementary material 16—ESM_16: Sliding window output of 2, 4 or 6 SNP haplotypes (p < 0.0001) for each significant region and the homozygosity of each haplotype in the top 15 and lowest 15 measurements for each metabolite (XLSX 925 kb)
11306_2019_1586_MOESM17_ESM.xlsx (30 kb)
Supplementary material 17—ESM_17: Summary table of significant associations and genes (XLSX 29 kb)
11306_2019_1586_MOESM18_ESM.xlsx (79 kb)
Supplementary material 18—ESM_18: GSD Metabolite concentrations (XLSX 79 kb)
11306_2019_1586_MOESM19_ESM.bed (3.5 mb)
Supplementary material 19—ESM_19 GSD genotype files (BED 3547 kb)
11306_2019_1586_MOESM20_ESM.bim (4.9 mb)
Supplementary material 20—ESM_20 GSD genotype files (BIM 5069 kb)
11306_2019_1586_MOESM21_ESM.fam (2 kb)
Supplementary material 21—ESM_21 GSD genotype files (FAM 2 kb)
11306_2019_1586_MOESM22_ESM.xlsx (11 kb)
Supplementary material 22—ESM_22: Metadata (age and sex) for GSDs in this study. Originally from Mortlock et al. 2016 (XLSX 10 kb)
11306_2019_1586_MOESM23_ESM.xlsx (21 kb)
Supplementary material 23—ESM_23: Summary statistics for all metabolites after adjusting for normality, pruning for outliers, and back-transforming the data. SD = Standard deviation; Q1 = first quartile; Q3 = third quartile; IQR = Interquartile range; CV = coefficient of variation(XLS X 21 kb)


  1. Adamski, J. (2012). Genome-wide association studies with metabolomics. Genome Medicine, 4(34), 1–7.Google Scholar
  2. Allaway, D., Kamlage, B., Gilham, M. S., Hewson-Hughes, A. K., Wiemer, J. C., Colyer, A., et al. (2013). Effects of dietary glucose supplementation on the fasted plasma metabolome in cats and dogs. Metabolomics, 9(5), 1096–1108. Scholar
  3. Asher, L., Diesel, G., Summers, J. F., McGreevy, P. D., & Collins, L. M. (2009). Inherited defects in pedigree dogs. Part 1: Disorders related to breed standards. Veterinary Journal, 182, 402–411. Scholar
  4. Bagheri, M., Farzadfar, F., Qi, L., Yekaninejad, M. S., Chamari, M., Zeleznik, O. A., et al. (2018). Obesity-related metabolomic profiles and discrimination of metabolically unhealthy obesity. Journal of Proteome Research, 17(4), 1452–1462. Scholar
  5. Ballevre, O., Cadenhead, A., Calder, A. G., Rees, W. D., Lobley, G. E., Fuller, M. F., et al. (1990). Quantitative partition of threonine oxidation in pigs: Effect of dietary threonine. American Journal of Physiology-Endocrinology and Metabolism, 259(4), E483–E491. Scholar
  6. Bauer, D., Hamacher, K., Bröer, S., Pauleit, D., Palm, C., Zilles, K., et al. (2005). Preferred stereoselective brain uptake of d-serine—A modulator of glutamatergic neurotransmission. Nuclear Medicine and Biology, 32(8), 793–797. Scholar
  7. Beckmann, M., Enot, D. P., Overy, D. P., Scott, I. M., Jones, P. G., Allaway, D., et al. (2010). Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs. British Journal of Nutrition, 103(8), 1127–1138. Scholar
  8. Benevides, G. P., Pimentel, E. R., Toyama, M. H., Novello, J. C., Marangoni, S., & Gomes, L. (2004). Biochemical and biomechanical analysis of tendons of caged and penned chickens. Connective Tissue Research, 45(4–5), 206–215. Scholar
  9. Biancalana, A., Veloso, L., & Gomes, L. (2010). Obesity affects collagen fibril diameter and mechanical properties of tendons in Zucker rats. Connective Tissue Research, 51(3), 171–178. Scholar
  10. Bird, M. I., & Nunn, P. B. (1983). Metabolic homoeostasis of l-threonine in the normally-fed rat. Importance of liver threonine dehydrogenase activity. Biochemical Journal, 214(3), 687–694. Scholar
  11. Boughton, B. A., Callahan, D. L., Silva, C., Bowne, J., Nahid, A., Rupasinghe, T., et al. (2011). Comprehensive profiling and quantitation of amine group containing metabolites. Analytical Chemistry, 83(19), 7523–7530. Scholar
  12. Breen, M., & Modiano, J. F. (2008). Evolutionarily conserved cytogenetic changes in hematological malignancies of dogs and humans—Man and his best friend share more than companionship. Chromosome Research, 16(1), 145–154. Scholar
  13. Burns, R. A., & Milner, J. A. (1982). Threonine, tryptophan and histidine requirements of immature Beagle dogs. Journal of Nutrition, 112(3), 447–452. Scholar
  14. Burns, R. A., Milner, J. A., & Corbin, J. E. (1981). Arginine: An indispensable amino acid for mature dogs. The Journal of Nutrition, 111(6), 1020–1024. Scholar
  15. Bushell, K. R., Kim, Y., Chan, F. C., Ben-Neriah, S., Jenks, A., Alcaide, M., et al. (2015). Genetic inactivation of TRAF3 in canine and human B-cell lymphoma. Blood, 125(6), 999–1005. Scholar
  16. Campbell, C. L., Bhérer, C., Morrow, B. E., Boyko, A. R., & Auton, A. (2016). A pedigree-based map of recombination in the domestic dog genome. G3: Genes, Genomes, Genetics, 6(11), 3517–3524. Scholar
  17. Chen, Y. P., Cheng, Y. F., Li, X. H., Yang, W. L., Wen, C., Zhuang, S., et al. (2017). Effects of threonine supplementation on the growth performance, immunity, oxidative status, intestinal integrity, and barrier function of broilers at the early age. Poultry Science, 96(2), 405–413. Scholar
  18. Cirulli, E. T., Guo, L., Leon Swisher, C., Shah, N., Huang, L., Napier, L. A., et al. (2019). Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metabolism, 29(2), 488–500. Scholar
  19. Colyer, A., Gilham, M. S., Kamlage, B., Rein, D., & Allaway, D. (2011). Identification of intra- and inter-individual metabolite variation in plasma metabolite profiles of cats and dogs. British Journal of Nutrition, 106(S1), S146–S149. Scholar
  20. Corfield, A. P., Myerscough, N., Longman, R., Sylvester, P., Arul, S., & Pignatelli, M. (2000). Mucins and mucosal protection in the gastrointestinal tract: New prospects for mucins in the pathology of gastrointestinal disease. Gut, 47(4), 589–594. Scholar
  21. D’Aniello, A. (2007). d-Aspartic acid: An endogenous amino acid with an important neuroendocrine role. Brain Research Reviews, 53(2), 215–234. Scholar
  22. D’Aniello, A., Vetere, A., & Petrucelli, L. (1993). Further study on the specificity of d-amino acid oxidase and of d-aspartate oxidase and time course for complete oxidation of d-amino acids. Comparative Biochemistry and Physiology, 105(3–4), 731–734. Scholar
  23. Defa, L., Changting, X., Shiyan, Q., Jinhui, Z., Johnson, E. W., & Thacker, P. A. (1999). Effects of dietary threonine on performance, plasma parameters and immune function of growing pigs. Animal Feed Science and Technology, 78(3–4), 179–188. Scholar
  24. Dias, D., Hill, C., Jayasinghe, N., Atieno, J., Sutton, T., & Roessner, U. (2015). Quantitative profiling of polar primary metabolites of two chickpea cultivars with contrasting responses to salinity. Journal of Chromatography, B: Analytical Technologies in the Biomedical and Life Sciences, 1000, 1–13.CrossRefPubMedGoogle Scholar
  25. Drost, H.-G. G., & Paszkowski, J. (2017). Biomartr: genomic data retrieval with R. Bioinformatics, 33(18), 1216–1217. Scholar
  26. Durinck, S., Moreau, Y., Kasprzyk, A., Davis, S., De Moor, B., Brazma, A., et al. (2005). BioMart and Bioconductor: A powerful link between biological databases and microarray data analysis. Bioinformatics. Scholar
  27. Durinck, S., Spellman, P. T., Birney, E., & Huber, W. (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols. Scholar
  28. Edgar, A. J. (2002). The human l-threonine 3-dehydrogenase gene is an expressed pseudogene. BMC Genetics, 3(1), 18. Scholar
  29. Endo, M., Ohashi, K., & Mizuno, K. (2007). LIM kinase and slingshot are critical for neurite extension. Journal of Biological Chemistry, 282(18), 13692–13702. Scholar
  30. Fiehn, O. (2002). Metabolomics—The link between genotypes and phenotypes. Plant Molecular Biology, 48(1–2), 155–171. Scholar
  31. Fleischer, S., Sharkey, M., Mealey, K., Ostrander, E. A., & Martinez, M. (2008). Pharmacogenetic and metabolic differences between dog breeds: Their impact on canine medicine and the use of the dog as a preclinical animal model. The AAPS Journal, 10(1), 110–119. Scholar
  32. Genchi, G. (2017). An overview on d-amino acids. Amino Acids, 49(9), 1521–1533. Scholar
  33. Gieger, C., Geistlinger, L., Altmaier, E., Hrabé de Angelis, M., Kronenberg, F., Meitinger, T., et al. (2008). Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genetics, 4(11), e1000282. Scholar
  34. Hammer, V. A., Rogers, Q. R., & Freedland, R. A. (1996). Threonine is catabolized by l-threonine 3-dehydrogenase and threonine dehydratase in hepatocytes from domestic cats (Felis domestica). The Journal of Nutrition, 126(9), 2218–2226. Scholar
  35. Hanna, V. S., & Hafez, E. A. A. (2018). Synopsis of arachidonic acid metabolism: A review. Journal of Advanced Research, 11, 23–32. Scholar
  36. Heilmann, R. M., McNiel, E. A., Grützner, N., Lanerie, D. J., Suchodolski, J. S., & Steiner, J. M. (2017). Diagnostic performance of the urinary canine calgranulins in dogs with lower urinary or urogenital tract carcinoma. BMC Veterinary Research, 13(1), 112. Scholar
  37. Kathrani, A., Werling, D., & Allenspach, K. (2011). Canine breeds at high risk of developing inflammatory bowel disease in the South-Eastern UK. Veterinary Record, 169(24), 635. Scholar
  38. Kawabe, M., Baba, Y., Tamai, R., Yamamoto, R., Komori, M., Mori, T., et al. (2015). Profiling of plasma metabolites in canine oral melanoma using gas chromatography-mass spectrometry. Journal of Veterinary Medical Science, 77(8), 1025–1028. Scholar
  39. Kettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., et al. (2016). Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nature Communications, 7, 1–9. Scholar
  40. Krejsgaard, T., Vetter-Kauczok, C. S., Woetmann, A., Kneitz, H., Eriksen, K. W., Lovato, P., et al. (2009). Ectopic expression of B-lymphoid kinase in cutaneous T-cell lymphoma. Blood, 113(23), 5896–5904. Scholar
  41. Lindblad-Toh, K., Wade, C. M., Mikkelsen, T. S., Karlsson, E. K., Jaffe, D. B., Kamal, M., et al. (2005). Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature, 438(7069), 803–819. Scholar
  42. Linder, D. E., Freeman, L. M., Holden, S. L., Biourge, V., & German, A. J. (2013). Status of selected nutrients in obese dogs undergoing caloric restriction. BMC Veterinary Research, 9(1), 219. Scholar
  43. Liu, Yun, Ke, X., Kang, H. Y., Wang, X. Q., Shen, Y., & Hong, S. L. (2016). Genetic risk of TNFSF4 and FAM167A-BLK polymorphisms in children with asthma and allergic rhinitis in a Han Chinese population. Journal of Asthma, 53(6), 567–575. Scholar
  44. Liu, Y., Wang, X., & Hu, C. A. A. (2017). Therapeutic potential of amino acids in inflammatory bowel disease. Nutrients. Scholar
  45. Lloyd, A. J., Beckmann, M., Tailliart, K., Brown, W. Y., Draper, J., & Allaway, D. (2016). Characterisation of the main drivers of intra- and inter- breed variability in the plasma metabolome of dogs. Metabolomics, 12(4), 1–12. Scholar
  46. Lloyd, A. J., Beckmann, M., Wilson, T., Tailliart, K., Allaway, D., & Draper, J. (2017). Ultra high performance liquid chromatography–high resolution mass spectrometry plasma lipidomics can distinguish between canine breeds despite uncontrolled environmental variability and non-standardized diets. Metabolomics, 13(2), 1–11. Scholar
  47. Machiela, M. J., & Chanock, S. J. (2014). GWAS is going to the dogs. Genome Biology, 15(3), 105. Scholar
  48. Madrigal-Ruíz, P.-M., Navarro-Hernández, R.-E., Petri, M.-H., Chavarría-Ávila, E., Ríos-Ibarra, C., Castro-Albarrán, J., et al. (2016). Inverse relationship of the CMKLR1 relative expression and chemerin serum levels in obesity with dysmetabolic phenotype and insulin resistance. Mediators of Inflammation, 2016, 1–9. Scholar
  49. Malinovsky, A. V. (2017). Reason for indispensability of threonine in humans and other mammals in comparative aspect. Biochemistry (Moscow), 82(9), 1055–1060. Scholar
  50. Mao, X., Zeng, X., Qiao, S., Wu, G., & Li, D. (2011). Specific roles of threonine in intestinal mucosal integrity and barrier function. Frontiers in Bioscience (Elite Edition), 3, 1192–1200.Google Scholar
  51. Milner, J. A. (2018). Assessment of the essentiality of methionine, threonine, tryptophan, histidine and isoleucine in immature dogs. The Journal of Nutrition, 109(8), 1351–1357. Scholar
  52. Mizuno, K., Niwa, R., Shuin, M., Ohashi, K., Kaji, N., & Uemura, T. (2003). Cell cycle-associated changes in slingshot phosphatase activity and roles in cytokinesis in animal cells. Journal of Biological Chemistry, 278(35), 33450–33455. Scholar
  53. Molina, H. (2002). The murine complement regulator Crry: New insights into the immunobiology of complement regulation. CMLS Cellular and Molecular Life Sciences, 59, 220–229.CrossRefPubMedGoogle Scholar
  54. Moncada, S., & Higgs, A. (1993). The l-arginine-nitric oxide pathway. New England Journal of Medicine, 329(27), 2002–2012. Scholar
  55. Mortlock, S. A., Booth, R., Mazrier, H., Khatkar, M. S., & Williamson, P. (2016). Visualization of genome diversity in German Shepherd dogs. Bioinformatics and Biology Insights, 9, 37–42. Scholar
  56. Moundras, C., Bercovici, D., Rémésy, C., & Demigné, C. (1992). Influence of glucogenic amino acids on the hepatic metabolism of threonine. Biochimica et Biophysica Acta (BBA)—General Subjects, 1115(3), 212–219. Scholar
  57. Nakamura, N., & Hirose, S. (2008). Regulation of mitochondrial morphology by USP30, a deubiquitinating enzyme present in the mitochondrial outer membrane. Molecular Biology of the Cell, 19(5), 1903–1911. Scholar
  58. O’Kell, A. L., Garrett, T. J., Wasserfall, C., & Atkinson, M. A. (2017). Untargeted metabolomic analysis in naturally occurring canine diabetes mellitus identifies similarities to human Type 1 diabetes. Scientific Reports, 7(1), 1–8. Scholar
  59. Ollier, W. E. R., Kennedy, L. J., Thomson, W., Barnes, A. N., Bell, S. C., Bennett, D., et al. (2001). Dog MHC alleles containing the human RA shared epitope confer susceptibility to canine rheumatoid arthritis. Immunogenetics, 53(8), 669–673. Scholar
  60. Olmstead, I. L. D., Hill, D. R. A., Dias, D. A., Jayasinghe, N. S., Callahan, D. L., Kentish, S. E., et al. (2013). A quantitative analysis of microalgal lipids for optimization of biodiesel and omega-3 production. Biotechnology and Bioengineering, 110(8), 2096–2104. Scholar
  61. Parker, H. G. (2012). Genomic analyses of modern dog breeds. Mammalian Genome, 23(1–2), 19–27. Scholar
  62. Peiravan, A., Bertolini, F., Rothschild, M. F., Simpson, K. W., Jergens, A. E., Allenspach, K., et al. (2018). Genome-wide association studies of inflammatory bowel disease in German Shepherd dogs. PLoS ONE, 13(7), e0200685. Scholar
  63. Petersen, D. L., Berthelsen, J., Willerslev-Olsen, A., Fredholm, S., Dabelsteen, S., Bonefeld, C. M., et al. (2017). A novel BLK-induced tumor model. Tumor Biology, 39(7), 101042831771419. Scholar
  64. Polley, M. J., Nachman, R. L., & Weksler, B. B. (1981). Human complement in the arachidonic acid transformation pathway in platelets. Journal of Experimental Medicine, 153(2), 257–268.CrossRefPubMedGoogle Scholar
  65. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., et al. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559–575. Scholar
  66. Puurunen, J., Sulkama, S., Tiira, K., Araujo, C., Lehtonen, M., Hanhineva, K., et al. (2016a). A non-targeted metabolite profiling pilot study suggests that tryptophan and lipid metabolisms are linked with ADHD-like behaviours in dogs. Behavioral and Brain Functions, 12(1), 27. Scholar
  67. Puurunen, J., Tiira, K., Lehtonen, M., Hanhineva, K., & Lohi, H. (2016b). Non-targeted metabolite profiling reveals changes in oxidative stress, tryptophan and lipid metabolisms in fearful dogs. Behavioral and Brain Functions. Scholar
  68. R Special Interest Group on Databases (R-SIG-DB), Wickham, H., & Müller, K. (2018). DBI: R Database Interface. R package. Retrieved from
  69. Rémond, D., Buffière, C., Godin, J.-P., Mirand, P. P., Obled, C., Papet, I., et al. (2009). Intestinal inflammation increases gastrointestinal threonine uptake and mucin synthesis in enterally fed minipigs. The Journal of Nutrition, 139(4), 720–726. Scholar
  70. Rhee, E. P., Ho, J. E., Chen, M. H., Shen, D., Cheng, S., Larson, M. G., et al. (2013). A genome-wide association study of the human metabolome in a community-based cohort. Cell Metabolism, 18(1), 130–143. Scholar
  71. Richards, K. L., & Suter, S. E. (2015). Man’s best friend: What can pet dogs teach us about non-Hodgkin’s lymphoma? Immunological Reviews, 263(1), 173–191. Scholar
  72. Richards, S. E., Wang, Y., Claus, S. P., Lawler, D., Kochhar, S., Holmes, E., et al. (2013). Metabolic phenotype modulation by caloric restriction in a lifelong dog study. Journal of Proteome Research, 12(7), 3117–3127. Scholar
  73. Rimbault, M., & Ostrander, E. A. (2012). So many doggone traits: Mapping genetics of multiple phenotypes in the domestic dog. Human Molecular Genetics, 21(1), 52–57. Scholar
  74. Rowell, J. L., McCarthy, D. O., & Alvarez, C. E. (2011). Dog models of naturally occurring cancer. Trends in Molecular Medicine, 17(7), 380–388. Scholar
  75. Ruth, M. R., & Field, C. J. (2013). The immune modifying effects of amino acids on gut-associated lymphoid tissue. Journal of Animal Science and Biotechnology, 4(1), 27. Scholar
  76. Samuelsson, B. (1991). Arachidonic acid metabolism: Role in inflammation. Zeitschrift für Rheumatologie, 50(Suppl 1), 3–6.PubMedGoogle Scholar
  77. Shen, K. Z., Cox, B. A., & Johnson, S. W. (1997). L-arginine potentiates GABA-mediated synaptic transmission by a nitric oxide-independent mechanism in rat dopamine neurons. Neuroscience, 79(3), 649–658. Scholar
  78. Shin, H. Y., Lee, D. C., Chu, S. H., Jeon, J. Y., Lee, M. K., Im, J. A., et al. (2012). Chemerin levels are positively correlated with abdominal visceral fat accumulation. Clinical Endocrinology, 77(1), 47–50. Scholar
  79. Simpfendorfer, K. R., Armstead, B. E., Shih, A., Li, W., Curran, M., Manjarrez-Orduño, N., et al. (2015). Autoimmune disease-associated haplotypes of BLK exhibit lowered thresholds for B cell activation and expansion of Ig class-switched B cells. Arthritis and Rheumatology, 67(11), 2866–2876. Scholar
  80. Soder, J., Hagman, R., Dicksved, J., Lindase, S., Malmlof, K., Agback, P., et al. (2017). The urine metabolome differs between lean and overweight Labrador Retriever dogs during a feed-challenge. PLoS ONE, 12(6), 1–17. Scholar
  81. Song, G. G., & Lee, Y. H. (2017). Association between BLK polymorphisms and susceptibility to SLE. Zeitschrift für Rheumatologie, 76(2), 176–182. Scholar
  82. Storey, J. (2015). qvalue: Q-value estimation for false discovery rate control. R package. Scholar
  83. Summers, J. F., Diesel, G., Asher, L., McGreevy, P. D., & Collins, L. M. (2010). Inherited defects in pedigree dogs. Part 2: Disorders that are not related to breed standards. Veterinary Journal, 183(1), 39–45. Scholar
  84. Takano, T., & Cybulsky, A. V. (2000). Complement C5b-9-mediated arachidonic acid metabolism in glomerular epithelial cells: Role of cyclooxygenase-1 and -2. American Journal of Pathology, 156(6), 2091–2101. Scholar
  85. Tamai, R., Furuya, M., Hatoya, S., Akiyoshi, H., Yamamoto, R., Komori, Y., et al. (2014). Profiling of serum metabolites in canine lymphoma using gas chromatography mass spectrometry. Journal of Veterinary Medical Science, 76(11), 1513–1518. Scholar
  86. Tang, L., Tong, Y., Cao, H., Xie, S., Yang, Q., Zhang, F., et al. (2014). The MTMR9 rs2293855 polymorphism is associated with glucose tolerance, insulin secretion, insulin sensitivity and increased risk of prediabetes. Gene, 546(2), 150–155. Scholar
  87. Trang, L. E., Fürst, P., Odeback, A. C., & Lövgren, O. (1985). Plasma amino acids in rheumatoid arthritis. Scandinavian Journal of Rheumatology, 14(4), 393–402. Scholar
  88. Tsai, K. L., Noorai, R. E., Starr-Moss, A. N., Quignon, P., Rinz, C. J., Ostrander, E. A., et al. (2012). Genome-wide association studies for multiple diseases of the German Shepherd Dog. Mammalian Genome, 23(1–2), 203–211. Scholar
  89. Turner, S. D. (2018). qqman: An R package for visualizing GWAS results using Q-Q and manhattan plots. Journal of Open Source Software. Scholar
  90. Vaysse, A., Ratnakumar, A., Derrien, T., Axelsson, E., Pielberg, G. R., Sigurdsson, S., et al. (2011). Identification of genomic regions associated with phenotypic variation between dog breeds using selection mapping. PLoS Genetics. Scholar
  91. Viant, M. R., Ludwig, C., Rhodes, S., Günther, U. L., & Allaway, D. (2007). Validation of a urine metabolome fingerprint in dog for phenotypic classification. Metabolomics, 3(4), 453–463. Scholar
  92. Vilson, Å., Bonnett, B., Hansson-Hamlin, H., & Hedhammar, Å. (2013). Disease patterns in 32,486 insured German Shepherd dogs in Sweden: 1995-2006. Veterinary Record, 173(5), 116. Scholar
  93. Wahl, J. M., Herbst, S. M., Clark, L. A., Tsai, K. L., & Murphy, K. E. (2008). A review of hereditary diseases of the German Shepherd dog. Journal of Veterinary Behavior: Clinical Applications and Research, 3(6), 255–265. Scholar
  94. Wang, X., Qiao, S. Y., Liu, M., & Ma, Y. X. (2006). Effects of graded levels of true ileal digestible threonine on performance, serum parameters and immune function of 10-25 kg pigs. Animal Feed Science and Technology, 129(3–4), 264–278. Scholar
  95. Warnes, G. R., Bolker, B., Bonebakker, L., Gentleman, R., Liaw, W. H. A., Lumley, T., et al. (2016). Package “gplots”: Various R programming tools for plotting data. R package. Scholar
  96. White, M. E., Hayward, J. J., Stokol, T., & Boyko, A. R. (2015). Genetic mapping of novel loci affecting canine blood phenotypes. PLoS ONE, 10(12), e0145199. Scholar
  97. Wickham, H. (2016). Package ‘ggplot2’: Elegant Graphics for Data Analysis. New York: Springer-Verlag. Scholar
  98. Wu, G., Bazer, F. W., Burghardt, R. C., Johnson, G. A., Woo, S., Darrell, K., et al. (2011). Proline and hydroxyproline metabolism: Implications for animal and human nutrition. Amino Acids, 40(4), 1053–1063. Scholar
  99. Wu, G., Bazer, F. W., Davis, T. A., Kim, S. W., Li, P., Marc Rhoads, J., et al. (2009). Arginine metabolism and nutrition in growth, health and disease. Amino Acids, 37(1), 153–168. Scholar
  100. Yanagiya, T., Tanabe, A., Iida, A., Saito, S., Sekine, A., Takahashi, A., et al. (2007). Association of single-nucleotide polymorphisms in MTMR9 gene with obesity. Human Molecular Genetics, 16(24), 3017–3026. Scholar
  101. Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 88(1), 76–82. Scholar
  102. Yao, J., de la Iglesia, H. O., & Bajjalieh, S. M. (2013). Loss of the SV2-like protein SVOP produces no apparent deficits in laboratory mice. PLoS ONE, 8(7), e68215. Scholar
  103. Zhang, H., Wang, L., Huang, Y., Zhuang, C., Zhao, G., Liu, R., et al. (2012a). Influence of BLK polymorphisms on the risk of rheumatoid arthritis. Molecular Biology Reports, 39(11), 9965–9970. Scholar
  104. Zhang, J., Wei, S., Liu, L., Nagana Gowda, G. A., Bonney, P., Stewart, J., et al. (2012b). NMR-based metabolomics study of canine bladder cancer. Biochimica et Biophysica Acta (BBA)—Molecular Basis of Disease, 1822(11), 1807–1814. Scholar
  105. Zhou, Y., Li, X., Wang, G., & Li, X. (2016). Association of FAM167A-BLK rs2736340 polymorphism with susceptibility to autoimmune diseases: A meta-analysis. Immunological Investigations, 45(4), 336–348. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Pamela Xing Yi Soh
    • 1
  • Juliana Maria Marin Cely
    • 1
  • Sally-Anne Mortlock
    • 1
  • Christopher James Jara
    • 1
  • Rachel Booth
    • 1
  • Siria Natera
    • 2
  • Ute Roessner
    • 2
  • Ben Crossett
    • 3
  • Stuart Cordwell
    • 1
    • 3
  • Mehar Singh Khatkar
    • 4
  • Peter Williamson
    • 1
    Email author
  1. 1.School of Life and Environmental Sciences, Faculty of ScienceUniversity of SydneySydneyAustralia
  2. 2.Metabolomics Australia, School of BioSciencesUniversity of MelbourneParkvilleAustralia
  3. 3.Sydney Mass Spectrometry, Charles Perkins CentreUniversity of SydneySydneyAustralia
  4. 4.Sydney School of Veterinary Science, Faculty of ScienceUniversity of SydneySydneyAustralia

Personalised recommendations