Abstract
Introduction
German shepherd dogs (GSDs) are a popular breed affected by numerous disorders. Few studies have explored genetic variations that influence canine blood metabolite levels.
Objectives
To investigate genetic variants affecting the natural metabolite variation in GSDs.
Methods
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.
Results
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.
Conclusion
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.
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Acknowledgements
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.
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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.
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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.
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11306_2019_1586_MOESM1_ESM.docx
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)
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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)
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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)
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Supplementary material 4—ESM_4: Summary statistics of sugars. Outliers were defined as measurements beyond 4 standard deviations from the mean (PDF 166 kb)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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
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_MOESM16_ESM.xlsx
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)
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Supplementary material 22—ESM_22: Metadata (age and sex) for GSDs in this study. Originally from Mortlock et al. 2016 (XLSX 10 kb)
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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)
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Soh, P.X.Y., Marin Cely, J.M., Mortlock, SA. et al. Genome-wide association studies of 74 plasma metabolites of German shepherd dogs reveal two metabolites associated with genes encoding their enzymes. Metabolomics 15, 123 (2019). https://doi.org/10.1007/s11306-019-1586-2
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DOI: https://doi.org/10.1007/s11306-019-1586-2