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Bayesian mixed model analysis uncovered 21 risk loci for chronic kidney disease in boxer dogs [1]

['Frode Lingaas', 'Faculty Of Veterinary Medicine', 'Department Of Preclinical Sciences', 'Pathology', 'Norwegian University Of Life Sciences', 'Ås', 'Katarina Tengvall', 'Science For Life Laboratory', 'Department Of Medical Biochemistry', 'Microbiology']

Date: 2023-02

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.

Author summary Chronic kidney disease (CKD) is described as a set of heterogeneous disorders affecting kidney structure and function. CKD is common in dogs and has been diagnosed in nearly all breeds. In this study, we identified 21 genetic regions associated with CKD in a boxer population and investigated the relevant genes and putative regulatory variants in these regions. Studies of canine CKD may help to better understand the pathology of kidney disease in both dogs and humans, and shows an important potential for early identification of high-risk individuals.

Citation: Lingaas F, Tengvall K, Jansen JH, Pelander L, Hurst MH, Meuwissen T, et al. (2023) Bayesian mixed model analysis uncovered 21 risk loci for chronic kidney disease in boxer dogs. PLoS Genet 19(1): e1010599. https://doi.org/10.1371/journal.pgen.1010599 Editor: Leigh Anne Clark, Clemson University, UNITED STATES Received: September 3, 2022; Accepted: January 4, 2023; Published: January 24, 2023 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability: The GWAS genotypes have been uploaded in SciLifeLab Data Repository (https://doi.org/10.17044/scilifelab.20014820). The sequencing data of 20 Norwegian boxers were deposited to ENA with accession number of ERR6182485- ERR6182504. 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. Competing interests: The authors have declared that no competing interests exist.

Introduction Chronic kidney disease (CKD) in humans is comprised of heterogeneous disease pathways that result in structural damage or decreased function presented as reduced glomerular filtration rate (GFR) or other markers of kidney disease for a period of more than three months [1]. In humans, CKD consists of clinically distinct disorders, also including congenital anomalies of both the kidney and urinary tract (CAKUT) and kidney disease secondary to hypertension and diabetes. This group of diseases represents a heavy and increasing disease burden in humans with prevalence estimates >10% and with substantial variation between human populations [2,3]. CKD also commonly occurs in dogs and cats [4,5]. The incidence of CKD in dogs is most likely between 0.5–1.5% and estimates from clinical data indicate that 10% of dogs over the age of 15 years are diagnosed with CKD [4]. Epidemiological analysis based on insurance data has shown an average incidence of kidney disease in general (without distinction between acute and chronic forms) of around 1.6% and significant differences in risk between Swedish dog breeds, with the highest incidence in the Bernese mountain dog, miniature schnauzer and boxer [5]. Although CKD may be initiated by environmental factors, a clear and significant genetic contribution has been shown in humans, strongly supported by heritability estimates of both disease and markers of kidney dysfunction, familial clustering, as well as linkage and genome-wide association studies (GWAS) [6–9]. It has become obvious that hundreds of genes contribute to complex forms of kidney disease as well as to the variation in markers associated with healthy kidneys [9,10]. Estimated glomerular filtration rate (eGFR), which is used as a marker for CKD in humans, was found to have high heritability (~29%) in a large biobank-study and more than 300 loci associated with eGFR-values were identified [11]. To date, more than 600 genes and loci have been implicated in monogenic and complex kidney diseases [12]. Causal variants associated with CKD may include rare monogenic or private variants, or common alleles with smaller effects. Additional variants are associated with kidney function markers, like eGFR and serum creatinine level [13]. A number of reports describe inherited renal disease in different dog breeds: Autosomal recessive hereditary nephropathy is noted in shih tzu [14], cocker spaniel [15], English springer spaniel [16], and Bernese mountain dog [17]; an autosomal dominant hereditary nephropathy in the bull terrier dogs [18]; and an X-linked hereditary nephropathy in Navasota dogs [19]. For decades, there have been concerns about a relatively high incidence of CKD in boxers. Based on pedigree studies breeders have suspected that CKD in young dogs commonly referred to as juvenile kidney disease (JKD), might have a genetic component. In a retrospective juvenile nephropathy study of 37 boxers less than five years old, Chandler[20] reported morphological findings of interstitial fibrosis, cell infiltration, dilated tubules and sclerotic glomeruli, Hoppe and Karlstam [21] described fetal, immature glomeruli and foci with small dysplastic structures surrounded by immature mesenchymal tissue in three CKD boxer puppies. Kolbjørnsen et al [22] studied morphological characteristics in seven related boxer dogs, three males and four females, between two months and five years. All cases had bilaterally small kidneys with segmental scarring and pronounced interstitial fibrosis. Across different studies of boxers, several dogs share these morphologic features of immature glomeruli, atypical tubules, proliferative arterioles and adenomatoid change [21,23,24]. Such features were less prominent in the study by Chandler [20], who also found a high frequency of incontinence and urinary tract infections in the studied boxers. Kolbjørnsen et al [22] classified the morphological features as reflux nephropathy or segmental hypoplasia. One case of juvenile nephropathy/nephronophthisis in boxers has also been reported [25]. The different studies show variation in morphology and time at onset, indicating that there may be a significant phenotypic and genetic heterogeneity also within the boxer breed. In this study, we aimed to uncover the genetic loci that contribute to canine CKD in a boxer population and explore candidate genes and functional variants within the associated regions. We hope this study will further improve our understanding of the genetic mechanism of CKD in dogs, which may in turn allow us to establish a canine model to facilitate the relevant studies in humans.

Discussions In this study, we used a Bayesian approach and identified 21 genetic regions associated with CKD in boxer dogs. These loci together explain 57% of the phenotypic variation. Meanwhile, we screened variants in LD with the top 50 BayesR markers, and identified 17 putative regulatory SNPs in evolutionary constrained positions. EMSAs confirmed that four SNPs, from the introns of MAGI2 and GALNT18, and an intergenic region of CKD on chr28, exhibited allele-specific binding in kidney cell lines, implying their potential function on CKD in boxers. The process of domestication may have led to an increase in number and frequency of deleterious genetic variants, due to the artificial selection and population drift [45]. In this study, five CKD regions were identified around domestication selection signals. Additionally, dramatic changes in allele frequency between wolves and purebred dogs were found in two putative regulatory SNPs (C8 and C12). This suggests that selection or drift may be partially responsible for an increased prevalence of CKD in some dog breeds, and may also provide an insight into the origin of the genetic basis of disease. Estimated LD extends ~50-fold greater distances within dog breeds than in humans [46]. In this study, the large region of 5.2 Mb on chr18 showed the strongest association with CKD. Although CKD candidate genes, like RELN and MAGI2, were easily recognized for their relevance to kidney function, it remains unclear if other genes from the locus or the interaction between them participate in CKD pathogenesis. To address the importance of these genes, it would be helpful to examine the CKD region in different breeds. Within the same TAD as the CKD region on chr28, the FGFR2 is an interesting gene due to its function in the development of early embryos [47]. Two major splice variants of FGFR2, FGFR2IIIb, and FGFR2IIIc, were predominantly expressed in distinct tissues with differential ligand affinity [48]. Fgfr2IIIb null mice showed reduction in both kidney size and number of presumptive nephrons [49]. In our study, screening of human-based annotated elements did not reveal any potential regulatory variant in this gene. Thus, investigation of the dog-specific regulatory elements in the region may be helpful. In addition to the top CKD regions, genes from other CKD regions may also be essential to kidney function. For example, near the CKD region on chr35 (chr35:10.8–11.4 Mb), transcription factor AP-2 alpha (TFAP2A) acts as a gatekeeper of differentiation during kidney development, by activating the terminal differentiation program of distal segments in the pronephros [50]. DNA (cytosine-5)-methyltransferase 3A (DNMT3A) from the CKD region on chr17 (chr17:18.5–19.8 Mb) is responsible for the methylation of gene regulatory regions that act as enhancers during kidney development [51]. Located in the CKD region on chr5 (chr5:65.9–66.0 Mb), Junctophilin 3 (JPH3) was identified in an association study of human CKD in 4,829 Japanese individuals [52]. YTH N6-Methyladenosine RNA Binding Protein 1 (YTHDF1) near the CKD region on chr24 (chr24:47.2–47.7 Mb) was highly expressed in the human fibrotic kidneys as a key contributor for renal fibrosis [53]. Folliculin interacting proteins-1 (FNIP1) was located in the CKD region on chr11 (chr11:19.3–20.2Mb). Disruption of FNIP1 resulted in the enlarged kidney size and significantly increased renal cyst formation [54]. At the 21 identified CKD loci, the load of risk alleles was significantly different between cases and controls (Fig 1C). Meanwhile, we investigated the risk alleles load of 21 CKD loci in 75 other breeds (S5 Table). It showed some breeds with high prevalence of CKD have a high-risk allele load, e.g., Cavalier King Charles Spaniel (22 alleles) [55], Collie (22 alleles), flat coated retriever (22 alleles) and Shih tzu (20 alleles) [5]. But other high-prevalence breeds were observed with a relatively low load, e.g. miniature schnauzer (18 alleles), boxer (17 alleles) [5] and Labrador retriever (17 alleles)[56]. This finding may indicate genetic heterogeneity, and that different sets of risk loci may play roles in CKD in other breeds. Therefore, the association of 21 risk loci and CKD in other breeds needs to be verified. To fully understand the genetics of CKD in canine, accurate diagnosis and sample collection from a wide range of breeds is required. This will aid cross-breed investigation, but will also increase the power to detect the loci with low effect through meta-analysis, and allow for fine mapping of shared CKD regions across breeds [46]. This analysis also has a potential value in risk estimation. In humans, risk scores based on the high number of identified CKD loci, such as polygenic risk scores (PRS)[57] and genomic risk score (GRS)[58], have been established for early identification of individuals at risk. A similar score system could be developed for canine CKD. We tentatively used the SNP effect from the BayesR analysis to estimate polygenic risk scores on 107 additional dogs, which were excluded from our original material due to the quality control. For this additional cohort predictions of polygenic risk scores yielded an odds ratio of 13.5 for being cases versus controls using risk scores below or above 0 as the decision threshold, where 0 was the average risk score. This finding will open important possibilities for early intervention and preventive measures for young dogs, and additionally provides a unique opportunity for selection of the breeding dogs at the lowest risk to reduce disease incidence. In conclusion, studies of canine CKD may help us understand the pathology of kidney disease in both dogs and human patients, and show an important potential of early identification of patients in predictive medicine.

Materials and methods Ethics statement All examination and sample collection of involved dogs were performed as part of the necessary diagnostic work-out by certified veterinarians according to ethical guidelines of the Norwegian University of Life Sciences or Swedish University of Agricultural Sciences. Sample collection in Finland was ethically approved by the Animal Ethics Committee of State Provincial Office of Southern Finland (ESAVI/343/04.10.07/2016). Written or verbal consents were obtained from the owners. CKD diagnosis There is significant age variation in boxers affected by CKD. Still, because of the high prevalence of CKD in younger dogs, only cases below 6 years of age (average 2.6 years) were included in this study. The support for the diagnosis varied between samples and countries for both cases and controls and was based on a combination of available information and age. The diagnostic support was based on either i) clinical characteristics only. Evaluation of samples includes general clinical evaluation, with urine analysis, clinical chemistry with elevated creatinine, urea and/or SMDA ++; ii) clinics with clinical chemistry. Same as above, and with additional clinic chemistry test of serum sample at Norwegian University of Life Science, or iii) morphology evaluation with clinical data or clinical pathology data (S11 Table). Most samples with kidney tissues available were evaluated pathomorphologically in Norway or Sweden. Controls were collected from healthy elderly dogs (>8 years; average 9,8 years), with no known history of renal failure or urinary tract infections, and often supported with serum biochemistry analyses within reference intervals. For some control dogs, we were able to confirm a healthy kidney by morphology in older dogs euthanized for other reasons than kidney disease. Macroscopic renal lesions The typical cases showed bilaterally small, firm, and pale mottled kidneys with irregular surfaces. The cortical surfaces revealed coarse nodular irregularities with numerous segmental fibrotic depressions surrounded by nodular hypertrophic cortical tissue. The renal capsules were frequently adherent to the renal cortical surfaces. On the cut surfaces, the cortices were irregularly thinned with multifocal to coalescing pale radial scars causing depressions of the cortical surfaces (Fig 5A and 5B). PPT PowerPoint slide

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TIFF original image Download: Fig 5. Pictures of boxer kidney with chronic kidney disease (CKD). (A) A non-decapsulated kidney from an 8-month-old female boxer dog with CKD revealing a coarse nodular irregular cortical surface; (B) sagittal section of the kidney revealing an irregularly pale and thinned cortex; (C) LM revealing cortical interstitial fibrosis with tubular atrophy and multifocal infiltration of mononuclear inflammatory cells, (D) glomerulocystic atrophy (HE x10) and (E) hyperplasia of medullary collecting duct epithelium consistent with so-called “atypical tubuli” or “adenomatoid change” (HE x20). https://doi.org/10.1371/journal.pgen.1010599.g005 Histological renal lesions The fibrotic segments of the renal cortex were characterized by variable and often multifocal infiltration of mononuclear inflammatory cells (lymphocytes and plasma cells) and tubular atrophy and paucity of glomeruli combined with enlargement of the interstitial connective tissue (Fig 5C). Tubular microcysts and glomerulocystic atrophy were observed within the fibrotic segments (Fig 5D). Medullary fibrosis and variable multifocal lymphoplasmacytic infiltration were common findings. Both in the juxtamedullary cortex and in the medulla epithelial hyperplasia of collecting ducts was frequently giving the impression of “atypical tubules” also called “adenomatoid tubular change” (Fig 5E). Glomeruli in the surrounding hypertrophic cortex often revealed varying segmental or global sclerosis. Fibrotic thickening around and along the Bowman capsules was a frequent finding. DNA extraction and genotyping Blood samples were collected for 362 boxers from Australia, Denmark, Finland, Germany, Norway, Sweden, UK and US (S1 and S12 Tables). DNA mini kit (QIAGEN, Germany) was used for DNA extraction from the blood sample with standard protocol. DNA was quantified by Nanodrop 2000 and stored at -20°C. The genotyping was performed at the SNP & SEQ Technology Platform in Uppsala or GeneSeek (Neogen, US) using Illumina CanineHD BeadChip. Quality control Of the 362 genotyped samples, we excluded one duplicate sample, and dogs with missing phenotype record (one dog), failed to meet the diagnosis criteria (14 dogs), or age criteria (three dogs). Six samples were identified and removed as outliers in inbreeding analysis with plink (v 1.90b4.9) [59], and 22 dogs were filtered out due to an overall call rate < 90%. The relatedness was tested by the plink2 with KING [60] kingship cutoff of 0.15, and 61 dogs were removed at this step. UU_Cfam_GSD_1.0 (CanFam4; NCBI assembly: GCA_011100685.1) was used reference assembly in this study [61]. Markers with genotyping rate < 95% (55,597 markers) and minor allele frequency < 1% (47,668 markers) were excluded. A final dataset consisting of 254 boxers with 101,664 autosomal markers were used for the analysis. Population structure We performed principal components analysis (PCA) with autosomal markers to evaluate population structure with plink (v 1.90b4.9) [59]. The first two components explained 15.5% and 6.5% genetic variation respectively. The PCA plot illustrated that boxers with different country of origin were mixed in one cluster (S4 Fig). Notably, the majority of boxers from the US (25 of 27) were slightly distanced from the others. However, since the controls and cases were evenly distributed across the population with no obvious stratification, all boxers were considered as one single group. We conducted a variance component analysis using WOMBAT (v 17/04/2014) [60], which showed a genomic relationship matrix (GRM)-based heritability of CKD was 0.61 ± 0.09 in this population. Bayesian association analysis We used the BayesR (v 01/04/2021) algorithm [26] to perform a genome-wide association analysis of CKD in the boxer population. BayesR assumes that the SNP effects are a priori derived from a mixture of four normal distributions: N(0, 0), N(0, 0.0001 ), N(0, 0.001 ) or N(0, 0.01 ). SNP effects from the four distributions were estimated using the Markov Chain Monte Carlo (MCMC) sampling. BayesR was run with the first principal component as the covariate for a total of 300,000 iterations with a burn-in step of 100,000. The model convergence was assessed from ten BayesR repeats runs, and top 50 markers with the highest absolute effect size were considered as the candidates, according to a previous study [62]. The frequency of these top 50 markers were investigated in other 75 breeds (sample size > = 10) from a previous study [27]. LD block analysis of candidate BayesR markers was performed and visualized with Haploview (v 4.2) [63]. Genome sequencing and variant calling To discover common variants from the candidate regions, we generated WGS data for 20 Norwegian boxers (12 cases and eight controls, S4 Table). Illumina short reads libraries preparation and sequencing were performed by the Norwegian Sequencing Centre at University of Oslo in Norway. The paired-end reads were mapped to dog UU_Cfam_GSD_1.0 reference [61] using BWA-mem2 (v 2.1) [64]. The alignment was sorted and indexed by SAMtools (v 1.14) [65]. Duplicate reads were detected and marked by the MarkDuplicates module of Picard Toolkit (v 2.27.5; Broad Institute). The SNPs and INDELs were called using the HaplotypeCaller from GATK (v 4.2.0.0) [66]. Afterward, a joint genotyping analysis was performed using CombineGVCFs and GenotypeGVCFs, to merge variants for samples in one cohort. Only biallelic SNPs and indels were selected and filtered using SelectVariants, with “hard-filtering” parameters “QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0” and “QD < 2.0 || FS > 200.0 || ReadPosRankSum < −20.0”, respectively. Genotype imputation and validation The WGS genotypes from 20 boxers were phased using SHAPEIT2 (v 2.r904) [67], to generate a reference pool of haplotypes. SNP chip data were pre-phased with SHAPEIT2. Genotype imputation was performed using IMPUTE2 (v 2.3.2) [68] by comparing the chip data haplotype with the reference pool. Imputed genotypes with probability > 0.9 were kept, otherwise they were set as missing. Specifically, a ~500 Kb region on chr18 was masked during the imputation, which is known as a genomic duplication caused by the orthologous segments on chr9 [61]. The imputed variants were filtered by requiring a minor allele frequency > 0.01, and call rate > 0.95. Internal cross-validation from IMPUTE2 indicated an imputation concordance of 97.7%. Meanwhile, we designed the primers and validated two imputed variants using sanger sequencing: 11bp indel (chr18: 16811242; forward: TCCCAAGCCAAACTCTGTTC; reverse: CCTGAAATGGCCTCTTTCTC) and one T/C SNP (chr18: 16914812; forward: ATTTGTCCCTGGCATTCTTG; reverse: GGGTCTCATTAGGCCCTGTT), which showed concordances of 100% (135/135) and 98.8% (168/170) respectively. We further cross validated the imputation with seven boxers from previous studies [69–71]. The WGS data of seven samples were downloaded and mapped to the canfam4 with coverages ranging from 17-28x (S13 Table). For these boxers, the genotypes were called and filtered (GQ > 30) at 7,264,546 variants (5,601,532 SNPs and 1,663,014 Indels) that were identified in the reference panel of 20 Norwegian boxers (HaplotypeCaller in GATK). The imputation was performed based on a subset of genotypes at 104,839 markers from the illumina chip. Afterward, we validated the imputed genotypes by comparing to the genotypes called from WGS data, with–sample-diff function in plink2 (v alpha-2.3)[59], which revealed an average of imputation rate (successfully imputed; genotype probability > 0.9 in IMPUTE2) of 95%, and imputation accuracy of 96% (correctly imputed, S13 Table). CKD candidate regions Imputed variants with high LD (r2>0.9) to any of the top 50 BayesR markers were detected with plink (-r2 -ld-window -r2 0.9 -ld-window-kb 1000 -ld-window 5000). LD blocks within 500 Kb were merged, and 21 candidate CKD regions were defined by variant position at the ends of LD block. The pairwise interaction of 21 BayesR markers were screened using “–fast-epistasis” function with BOOST method [72] implemented in plink, and no significant interaction was found. BayesR markers with the highest effect size were selected from each of 21 CKD regions. To assess the phenotypic variation explained by the identified CKD regions, we determined the association of phenotype and 21 BayesR markers with analysis of variance (ANOVA) in R (v 4.0.1; aov function) with model “phenotype ~ SNP1+SNP2+…+SNP21”. The means of risk alleles load between control and case groups were counted and compared with t-test in R (ttest function). Additionally, we counted the risk allele load individually, then calculated the average load for the 75 breeds described in the Bayesian association analysis. Gene annotation and gene ontology analysis Protein-coding genes within the CKD regions and the closest gene on each side were identified based on UU_Cfam_GSD_1.0 annotation from NCBI (GCF_011100685.1). Genes were assigned to the human orthologues for the gene ontology (GO) analysis with Metascape [73]. Overlap of candidate variants with genomic features To investigate if dog domestication contributes to CKD, we compared CKD regions with selection signals from four studies [34,35,74,75]. Imputed variants in LD with BayesR markers were evaluated by comparing different annotated genomic features: 1) variant effect was annotated using SNPeff (v 4.3t)[76]; 2) location to topologically associating domains (TAD) in CKD regions were extracted from a previous study in dog liver tissue[77]; 3) risk allele frequency of imputed variants was extracted from Dog10K dataset (1,591 purebred dogs, 281 village dogs and 57 wolves; https://kiddlabshare.med.umich.edu/dog10K/) [69]; 4) The phyloP scores from 241 mammals were extracted from the Zoonomia project[78]. To obtain an extended functional annotation, imputed SNPs were lifted to the human hg38 genome using LiftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver); 5) the lifted SNPs were intersected with the human regulatory elements from cCREs and HS databases from ENCODE[29,79], and with the promoters and enhancers from GeneHancer [30]. Electrophoretic mobility shift assay (EMSA) Putative regulatory SNPs were selected and tested with EMSA in Madin-Darby Canine Kidney (MDCK) and human embryonic kidney (HEK293) cell lines. All cell lines were cultured in DMEM (Gibco), supplemented with 10% heat inactivated foetal bovine serum (Gibco), 1% penicillin, streptomycin and glutamine (Gibco) and maintained at 37°C (5% CO 2 ). For EMSAs, nuclear extracts from each cell line were prepared according to the manufacturer’s specification (NucBuster Protein Extraction Kit, Merck) and assayed using the appropriate oligo set (S14 Table) and with the Lightshift Electrophoretic Mobility-Shift Assay kit (Thermo Fisher Scientific). The following alterations were made to the manufacturer’s protocol: 12–15 μg of appropriate nuclear extract was pre-incubated on ice for 40 minutes in binding buffer (binding buffer supplemented with: 7.5% Glycerol, 0.063% NP-40, 30.1 mM KCl, 2 mM MgCl2, 0.1 mM EDTA, 50 ng/ul Poly (dI·dC)). Biotin labelled ds-oligonucleotides were added at 200 fmol and competed where appropriate with matched 20 pmol unlabelled ds-oligonucleotides. Reactions were incubated on ice for 40 minutes prior resolution on a 5% polyacrylamide gel (BioRad) run in 0.5 × TBE at 100 V for one and half hours. For four variants which showed allele-specific binding in the EMSA, a technical replicate was performed to validate the result.

Acknowledgments The computations and data handling were enabled by resources in projects SNIC 2021/5-579, SNIC 2021/5-580 and SNIC 2021/2-11 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX. The sequencing service was provided by the Norwegian Sequencing Centre, hosted by the University of Oslo and supported by the Functional Genomics and Infrastructure programs of the Research Council of Norway and the Southeastern Regional Health Authorities. The project also received data management/infrastructure support from ELIXIR Norway, supported by the Research Council of Norway. Genomic variation data was produced by the Dog10K Project, an international collaboration to advance canine genetics. The authors would like to thank the dog owners, breed club and veterinary colleagues in all participating countries who provided information, and DNA samples for the study. We acknowledge Bruce Cattanach (deceased) for his contribution to the sample collection from the UK.

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