Bayesian mixed model analysis uncovered 21 risk loci for chronic kidney disease in boxer dogs

PLoS Genet. 2023 Jan 24;19(1):e1010599. doi: 10.1371/journal.pgen.1010599. eCollection 2023 Jan.

Abstract

Chronic kidney disease (CKD) affects 10% of the human population, with only a small fraction genetically defined. CKD is also common in dogs and has been diagnosed in nearly all breeds, but its genetic basis remains unclear. Here, we performed a Bayesian mixed model genome-wide association analysis for canine CKD in a boxer population of 117 canine cases and 137 controls, and identified 21 genetic regions associated with the disease. At the top markers from each CKD region, the cases carried an average of 20.2 risk alleles, significantly higher than controls (15.6 risk alleles). An ANOVA test showed that the 21 CKD regions together explained 57% of CKD phenotypic variation in the population. Based on whole genome sequencing data of 20 boxers, we identified 5,206 variants in LD with the top 50 BayesR markers. Following comparative analysis with human regulatory data, 17 putative regulatory variants were identified and tested with electrophoretic mobility shift assays. In total four variants, three intronic variants from the MAGI2 and GALNT18 genes, and one variant in an intergenic region on chr28, showed alternative binding ability for the risk and protective alleles in kidney cell lines. Many genes from the 21 CKD regions, RELN, MAGI2, FGFR2 and others, have been implicated in human kidney development or disease. The results from this study provide new information that may enlighten the etiology of CKD in both dogs and humans.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles
  • Animals
  • Bayes Theorem
  • Dogs
  • Genome-Wide Association Study*
  • Humans
  • Kidney
  • Polymorphism, Single Nucleotide
  • Renal Insufficiency, Chronic* / epidemiology
  • Renal Insufficiency, Chronic* / genetics
  • Renal Insufficiency, Chronic* / veterinary

Grants and funding

UPPMAX is partially funded by the Swedish Research Council through grant agreement no. 2018-05973. The project received financial support from SKK/Agria Pet Insurance (project no N2014-0043), the Norwegian Kennel Club, the Norwegian boxer club, the Jane and Aatos Erkko Foundation, and HiLife. Genome sequencing of Dog10K project was supported by National Science and Technology Innovation 2030 Major Project of China (2021ZD0203900) and the National Key R&D Program of China (2019YFA0707101). KL-T was funded by a Distinguished Professorship from the Swedish Research Council. MK is financially supported by the Knut and Alice Wallenberg Foundation as part of the National Bioinformatics Infrastructure Sweden at SciLifeLab. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.