Evidence map›Paper›PMID 31493001›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2019

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 and 1 more

Abstract read
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Article in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 43% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Blood biomarkers and breed genetics of aging in pet dogs.bioRxiv : the preprint server for biology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Pamela Xing Yi SohSchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia.
Juliana Maria Marin CelySchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia.
Sally-Anne MortlockSchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia.
Christopher James JaraSchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia.
Rachel BoothSchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia.
Siria NateraMetabolomics Australia, School of BioSciences, University of Melbourne, Parkville, Australia.
Ute RoessnerMetabolomics Australia, School of BioSciences, University of Melbourne, Parkville, Australia.
Ben CrossettSydney Mass Spectrometry, Charles Perkins Centre, University of Sydney, Sydney, Australia.
Stuart CordwellSchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia.
Mehar Singh KhatkarSydney School of Veterinary Science, Faculty of Science, University of Sydney, Sydney, Australia.
Peter WilliamsonSchool of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, Australia. p.williamson@sydney.edu.au.
University of Sydney · AUUniversity of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

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

methodsA 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.

resultsSummary 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.

conclusionDAO 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.

Indexed as

MetabolomePolymorphism, Single NucleotideAlcohol OxidoreductasesAnimalsD-Amino-Acid OxidaseDogsFemaleGas Chromatography-Mass SpectrometryGenome-Wide Association StudyHaplotypesHydroxyprolineMaleAlcohol OxidoreductasesD-Amino-Acid OxidaseHydroxyprolineL-threonine 3-dehydrogenaseCanineGeneticsGWASMetabolomicsPlasma

Identifiers

PMID31493001
OpenAlexW2972146262

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.