Next Article in Journal
A Multicentre Epidemiologic Study of Sudden and Unexpected Death in Adult Cats and Dogs in Australia
Previous Article in Journal
Successful Treatment of Captive Common Marmosets (Callithrix jacchus) Infested with Common Cat Fleas (Ctenocephalides felis) by Using Topical Imidacloprid and Environmental Control Measures
Previous Article in Special Issue
Flow Cytometric Features of B- and T-Lmphocytes in Reactive Lymph Nodes Compared to Their Neoplastic Counterparts in Dogs
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis

by
Pamela Xing Yi Soh
1,2,
Mehar Singh Khatkar
3,4 and
Peter Williamson
1,3,*
1
School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
2
School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
3
Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
4
School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA 5371, Australia
*
Author to whom correspondence should be addressed.
Vet. Sci. 2023, 10(9), 581; https://doi.org/10.3390/vetsci10090581
Submission received: 2 August 2023 / Revised: 7 September 2023 / Accepted: 11 September 2023 / Published: 19 September 2023
(This article belongs to the Special Issue Advances in Canine and Feline Lymphoma)

Abstract

:

Simple Summary

Lymphoma is a common cancer in dogs, with large variation between breeds in both the incidence and frequency of immunophenotypes (B-cell or T-cell lymphoma). Very few studies have explored the genetic underpinnings of lymphoma in dogs, and it is not known whether various breeds share common risk genes considering the disparities in disease occurrence and immunophenotype distribution. In this study, our aim was to investigate lymphoma in a population of Border Collies, a breed at increased risk of lymphoma (often B-cell type). To our knowledge, this is the first genetic investigation of lymphoma risk in Border Collies. We examined pedigree data for possible inheritance patterns, and conducted a genetic investigation that incorporated the pedigree information to uncover possible genetic predispositions for lymphoma. We identified regions on chromosomes 18 and 27 that harbour cancer-related genes as prime candidates for lymphoma susceptibility, warranting further investigation.

Abstract

There has been considerable interest in studying cancer in dogs and its potential as a model system for humans. One area of research has been the search for genetic risk variants in canine lymphoma, which is amongst the most common canine cancers. Previous studies have focused on a limited number of breeds, but none have included Border Collies. The aims of this study were to identify relationships between Border Collie lymphoma cases through an extensive pedigree investigation and to utilise relationship information to conduct genome-wide association study (GWAS) analyses to identify risk regions associated with lymphoma. The expanded pedigree analysis included 83,000 Border Collies, with 71 identified lymphoma cases. The analysis identified affected close relatives, and a common ancestor was identified for 54 cases. For the genomic study, a GWAS was designed to incorporate lymphoma cases, putative “carriers”, and controls. A case-control GWAS was also conducted as a comparison. Both analyses showed significant SNPs in regions on chromosomes 18 and 27. Putative top candidate genes from these regions included DLA-79, WNT10B, LMBR1L, KMT2D, and CCNT1.

1. Introduction

Lymphoma is the most common haematological malignancy in dogs, with an incidence of about 13 to 114 per 100,000 dogs annually, and has varying incidence between countries and breeds [1,2,3,4]. The distribution of immunophenotypes is also highly varied across breeds, such that the T-cell type occurs in the majority of cases in some breeds (e.g., Siberian Husky) while the B-cell type occurs in the majority of cases in others (e.g., Basset Hound and Rottweiler) [5,6].
Canine lymphoma has numerous parallels to human non-Hodgkin’s lymphoma, potentially implicating the shared environment on the aetiology of the disease [7,8]. The aetiology of canine lymphoma is not fully understood and is likely a result of complex interactions between genetics and the environment. Previous studies have explored chromosomal aberrations [9], epigenetic silencing [10] epigenetic deregulation [11], long non-coding RNAs [12], microRNAs [13], DNA methylation [14], somatic mutations [15], and germline mutations [16] as mechanisms for canine lymphoma.
Several environmental risk factors have been identified, such as exposure to herbicides [17], tobacco smoke [18], or household chemicals [19] or proximity to industrial areas [20] and waste management [6,21]. Geographic associations with the incidence of canine lymphoma have also been explored and show similarities to human lymphoma incidence [4,17].
Several studies have exploited genome-wide association studies (GWASs) to identify risk genotypes for canine lymphoma. One study reported two shared risk loci on chromosome 5 for B-cell lymphoma and hemangiosarcoma but did not find any single nucleotide polymorphisms (SNPs) significantly associated with B-cell lymphoma alone [22]. Another study detected a significant signal on chromosome 4 and identified the candidate genes MCC, MXD3, and FGFR4 [23]. The authors were unable to find any SNPs significantly associated with lymphoma when a GWAS was conducted across multiple breeds [23]. A study of T-zone lymphoma found genome-wide significance on chromosome 8, near thyroid hormone regulation genes DIO2 and TSHR, and on chromosome 14, near hyaluronidase genes SPAM1, HYAL4, and HYALP1 [24]. Given the phenotypic and genetic diversity across dog breeds [25,26,27,28,29,30,31,32,33,34] and differences in the incidence and immunophenotype of lymphoma [1,2,3,4,5,6], it is not yet clear whether genetic risk factors for lymphoma in one breed will be the same in another. A more recent study of multiple breeds with a range of haematopoietic malignancies showed some overlap in associated regions and the potential for pleiotropic effects [35].
Border Collies are a popular breed worldwide and are at increased risk of lymphoma compared to the general population [36]. Lymphomas in Border Collies are most often of the B-cell immunophenotype (84 to 91% of cases) [5,37]. We previously reported the results of a survey of lymphoma of Border Collies in Australia and identified 28 cases with a common female ancestor [37], suggesting a potential heritable risk. In this study, the case number and extent of pedigree analysis was expanded and used to inform SNP-based GWAS analyses.

2. Methods

2.1. Animals

This study was conducted in accordance with guidelines from the Animal Research Act, NSW, Australia, approved by the Animal Ethics Committee of the University of Sydney under ethics numbers 37/634/6013. A total of 289 blood samples (38 cases, 251 controls) from Border Collies were collected in EDTA-coated vacutainers by licensed veterinarians and were voluntarily submitted from owners with written informed consent. Dogs were considered ‘purebred’ if they had been registered by the Australian National Kennel Club (ANKC) or following visual inspection by veterinarians. Diagnosis of lymphoma was confirmed through immunohistochemistry, with T- or B-cell subtype reported for 22 of the 38 lymphoma cases. Controls were from samples in an in-house biobank that were reported to be lymphoma-free at the time of collection, and 11 were confirmed by owners to not have lymphoma at the time of a health survey [37]. Further details of dogs are included in Supplementary Data S1.

2.2. Pedigree Construction

Pedigree data were available through an extensive database with records from over 83,000 Border Collies [30]. Clinical details of 71 lymphoma cases and pedigree information for these dogs were provided with consent, either through survey [37] or via direct reporting from owners. Pedigrees were constructed using a combination of the R packages ‘kinship2’, ‘pedigree’, and ‘FamAgg’ [38,39,40].

2.3. Genotyping and Imputation

DNeasy Blood and Tissue Kit (Qiagen, Melbourne, Victoria, Australia) was used to isolate genomicDNA following the manufacturer’s protocol. Genotyping was conducted by Geneseek (Lincoln, NE, USA) using the CanineHD BeadChip (Illumina, San Diego, CA, USA). Sixty-one DNA samples were genotyped on an older array covering over 170,000 evenly spaced, genome-wide SNPs (170 k dataset), while 230 samples were genotyped on a more extensive array covering over 220,000 SNPs (220 k dataset). Genotype data were imputed, using the same methods as those described in [30] to match all samples to the higher-density SNP chip using BEAGLE v5.1 [41]. Both datasets were first filtered separately using PLINK v1.9 [42] for individual missingness of 0.2 (--mind 0.2), and the 220 k dataset was filtered for a minor allele frequency of 0.02 (--maf 0.02). The datasets were then merged and filtered to remove SNPs with over 25% missing call rate (--geno 0.25), then imputed on BEAGLE v5.1 [41] with default settings except that the cluster was set to 0.05 and effective population was set to 100. An effective population size of 100 was selected as pedigree estimates of diversity suggest an effective population size of 99 to 129 for Border Collies [30,32,43].

2.4. Genome-Wide Association Study (GWAS)

Two cases were excluded from the GWAS due to uncertainties with their diagnosis; whether these dogs had stage V lymphoma (includes any stage I-IV signs and bone marrow involvement; secondary leukaemia) or acute lymphocytic leukaemia (ALL) (neoplastic lymphocytes or lymphoid progenitors originating in the bone marrow) was never elucidated. Stage V lymphoma and ALL can be difficult to differentiate and the prognosis and response to treatment for each disease is different [44,45], which may suggest different molecular causes. Since 84 to 90% of lymphoma cases in Border Collies are B-cell type [5,37], and T-cell non-Hodgkin’s lymphoma (NHL) in humans has different genetic causes from B-cell NHL [46], another 4 dogs diagnosed with T-cell lymphomas were excluded. Of the remaining 32 lymphoma cases, 18 were B-cell lymphomas, while the rest were untyped.
As there were many relatives of lymphoma cases in the control population and to improve power, a quantitative phenotype was chosen, based on the assumption of Mendelian inheritance for a recessive risk genotype for lymphoma, such that lymphoma cases were coded as 2, carriers were coded as 1, and controls were coded as 0. Carriers included dogs that were offspring of lymphoma cases, parents of lymphoma cases, or grandparents/great-grandparents of at least two cases. PIHAT values (proportion of identity-by-descent (IBD)) were calculated in PLINK v1.9 using --genome [42]. Controls with PIHAT > 0.25 (i.e., first- and second-degree relatives) to cases were also removed. There was a total of 27 carriers included in the analysis, including 10 offspring of lymphoma cases, 12 parents of one case each (6 of which were also grandparents or great-grandparents of one or more cases), 1 parent of two cases, and 4 dogs that were grandparents or great-grandparents of at least two cases. Out of the 221 controls that remained after genotype quality control filtering, 22 dogs were removed due to their relationship (known through pedigree) with lymphoma cases as it was unclear whether they could be carriers for a risk genotype. This included one great-grandparent, eight grandparents, five siblings, six half-siblings, one grandchild, and one aunt of lymphoma cases. A separate GWAS was also run using just the cases and controls (excluding those with PIHAT > 0.25 to cases) with a binary phenotype to compare these results to the results from the quantitative phenotype GWAS.
Genome-wide Complex Trait Analysis (GCTA) [47] was used to filter the imputed dataset for a minor allele frequency of 0.02 and create genetic relationship matrices (GRM), which were incorporated into the mixed linear model analysis (MLMA). All plots were generated using R v3.6.1 (R Development Core Team 2011). False discovery rates (FDR) were estimated by genome-wide q-values and chromosome-wise q-values through the R package “qvalue” using the MLMA output of p-values [48]. A q-value threshold of 0.1 was selected based on sample size and power estimate. Manhattan plots were created in R using the R package “qqman” [49]. A linear discriminant analysis (LDA) for the top 100 SNPs (ranked by p-value) for each GWAS was used to find the combination of SNPs that best explained the phenotype and discriminated between cases and controls through a backwards stepwise regression. Carriers were removed from the quantitative phenotype GWAS dataset. This was conducted using a linear model for phenotype (1 for controls and 2 for cases) as the outcome variable and using the genotypes of cases and controls at the significant SNPs as the predictor variables. The output was then used in the inbuilt R package ‘stats’ function step(), with direction set to ‘backward’.
Regional association plots were generated using modified code from R packages “RACER” and “IntAssocPlot” [50,51]. Linkage disequilibrium (LD) values (r2) for the regional association plots were calculated using PLINK v1.9 [42] and gene annotations were obtained from the National Center for Biotechnology Information (NCBI) (GCF_000002285.3 CanFam3.1, retrieved on 8 January 2020). Genes in all cancer pathways and viral infection pathways for Canis familiaris were fetched from KEGG (http://www.kegg.jp/, accessed on 8 January 2020) using the R package “KEGGREST” [52]. Viral infection pathway genes were included based on studies of lymphoma and viruses in humans [53,54,55]. Ensembl (https://www.ensembl.org, accessed on 8 January 2020) Release 106 dog genome CanFam3.1 (GCA_000002285.2) [56] was used to investigate genes of interest and genetic variants that are predicted to affect protein function using Sorting Intolerant From Tolerant (SIFT) values [57]. SIFT values less than 0.05 indicate that the variant in question likely affects protein function. Variants were also investigated in the dog population from [27], integrated in Ensembl as PRJEB24066.

2.5. Haplotype Analysis

Haplotype association analyses were conducted by creating haplotype blocks with PLINK v1.9 [42] for significant chromosomes in the GWAS and then using the haplotype blocks in PLINK v1.07 [58] as input for --hap. The quantitative phenotype was used for a linear regression (--hap-linear) while the binary phenotype was used for a logistic regression (--hap-logistic) for cases and controls. Haplotype frequencies for each group and each highly significant block (p < 0.0001) were calculated using a custom R script.

2.6. Relationship Networks (NetView)

The R packages ‘netview’ and ‘GGally’ were used to construct relationship networks based on pedigree or genotype data and clustered based on a k-value of 10 [59]. Interactive networks were created using the R package ‘network D3’ [60]. To visualise the relative position of each lymphoma case in the population and to investigate case clustering, usage of pedigree data was maximised by creating a kinship matrix based on 15 generations of ancestry for dogs with available health status from our previous survey, as well as all genotyped dogs that had pedigrees available. This included 54 lymphoma cases (including 22 genotyped), 114 normal controls (12 genotyped), and 40 other genotyped dogs of unknown health status. An additional relationship network was created based on genotype data using a distance matrix created in PLINK v1.9 [42] for only the dogs included in the GWAS analysis, including 31 lymphoma cases, 27 carriers, and 119 controls.

2.7. Restricted Maximum Likelihood (REML) Analysis

A REML analysis was conducted for the cases (31 dogs) and controls (119 dogs) in GCTA [47] to estimate the phenotypic variance (Vp) explained by the genetic variance (V(G)) for (1) all the autosomes together, (2) each autosome separately, and (3) significant regions from the GWAS output. The analysis uses a log likelihood ratio test to calculate p-values. The variance explained (V(G)/Vp, heritability) is transformed to the underlying liability scale (V(G)/Vp_L) using disease prevalence such that if heritability on the observed scale is very large, this value becomes relatively small when transformed to the liability scale if there is ascertainment bias (i.e., proportion of cases to controls is much larger than prevalence), which is typical in case-control studies [61]. Previous reports from South Africa estimated a lymphoma prevalence of 0.02 in Border Collies [62]. Since the prevalence of lymphoma in Border Collies in Australia has not been reported, prevalence values of 0.1, 0.05, and 0.025 were tested for each analysis.

3. Results

3.1. Pedigree Investigations

Along with the 35 genotyped lymphoma cases, an additional 30 lymphoma cases that had pedigree information were known from our previous health survey study [37], and another six deceased dogs that were relatives of genotyped or surveyed lymphoma cases that had also been diagnosed with lymphoma were reported privately to us from owners. Of these 71 dogs, 25 had B-cell lymphomas (35%), three had T-cell lymphomas (4%), and 43 had unknown type lymphomas (61%). The mean age at first diagnosis was 8.7 years (SD 3.3). The distribution of age at first diagnosis of lymphoma is presented in Figure 1. The peak age at diagnosis was 9 years, while the age at diagnosis ranged from as young as 1 year and 11 months to 15 years and 3 months.
Of these 71 cases, 54 had pedigrees available (22 genotyped). All 54 pedigree cases were traced to a common ancestor born in 1968, dog_811 (Figure 2). The common ancestor for 28 cases identified in our previous study [37], dog_67066, was an offspring of dog_811, and was identified as an ancestor to 52 of these cases. In this pedigree, there were two pairs of full-sibling littermates diagnosed with lymphoma (dog_13422 and dog_13511; dog_2445 and dog_2446). One of the affected littermates, dog_13422, was also a parent of another case, dog_13339, and was one of five affected parent–offspring duos. One of the other affected parent–offspring duos (dog_33176 and dog_30475) descended from dog_50125, which sired two other cases to separate dams and was a grandparent of two cases and a great-grandparent of one case. One parent (dog_2625) in another affected parent–offspring duo (dam dog_2625 and offspring dog_2626) had an affected aunt (dog_2615) that was a full sibling of dog_2625′s dam. The sire of dog_2615 and dog_2625′s dam was also a grandparent of another case (dog_2629). Dog_50681, which we previously identified as a common ancestor for 12 cases [37], was identified as a common ancestor for an additional seven cases. These 19 cases primarily descended from two full siblings, dog_44286 (four cases) and dog_44277 (12 cases), while three dogs descended from dog_50681. Within these 19 cases, there were three pairs of parent–offspring cases and one aunt–niece occurrence of lymphoma.
A NetView plot was created using a pedigree kinship matrix with the 54 known lymphoma cases, normal pedigree dogs identified through our previous health survey, and genotyped dogs that had pedigrees available (Figure 3). The plot showed that lymphoma cases were widespread in the network, and a good distribution of animals was captured in the genotyping. There was one cluster of dogs that separated from the main network (top left of figure), which included four lymphoma cases, none of which were available for genotyping.

3.2. GWAS with Quantitative Phenotype

Through pedigree mapping, we were able to identify the relationships of genotyped dogs with all known lymphoma cases and the familial occurrence of lymphoma that suggested a potential heritable risk. This information was used to design the GWAS through the assumption of Mendelian inheritance of a recessive genotypic risk for lymphoma. Putative “carriers” were identified as dogs that were either (i) parents of lymphoma cases; (ii) offspring of lymphoma cases; or (iii) grandparents or great-grandparents of at least two cases. Dogs that could potentially be “carriers”, such as siblings or half-siblings of lymphoma cases or grandparents and/or great-grandparents of only one case, were removed from the GWAS analysis due to the uncertainty of their status as “carrier” or control. After the imputation and filtering of the genotype data, as well as the exclusion of cases based on uncertain or T-cell diagnoses and the exclusion of controls due to relationships with lymphoma cases (PIHAT > 0.25), 177 dogs (31 lymphoma cases, 27 carriers, and 119 controls) and 153,900 SNPs were left for genomic analyses.
There were no genome-wide significant SNPs (q < 0.1) from the mixed linear model analysis (Figure 4); however, chromosome-wise calculated q-values showed significance (q < 0.1) for chromosomes 18 and 27 (Figure 5). Chromosome 18 was significant for 28 SNPs, with 27 of these SNPs being located between 37.2 Mb and 41.9 Mb and one SNP at 55.4 Mb (Figure 4A, Table 1). Three of these SNPs were within ZDHHC5, and there was one SNP each in LOC100688997, LOC100684610, CTNND1, OR10C10, OR5B21, TMEM109, LOC483451, and SSRP1. To account for SNPs in linkage, the significant region of interest was expanded 500 kb upstream and downstream to search for relevant genes. The 36.7 to 42.4 Mb region spanned a total of 238 protein-coding genes, including five genes that were listed in cancer or viral infection pathways from KEGG. These genes were DLA-79, PSMC3, DDB2, NR1H3, and SPI1. The genes PSMC3, DDB2, NR1H3, and SPI1 were downstream of the significant region (42.2–42.4 Mb). The top associated SNP, 18:38704682, was in a non-coding region but had low to moderate linkage (0.2 < r2 < 0.5) with nine SNPs (Figure 6, Table 1). Eight lymphoma cases carried homozygous AA at this SNP. Of these eight, six had confirmed B-cell lymphoma diagnosis. Four cases were in complete remission (disappearance of tumours and symptoms) following a chemotherapy protocol, two were in partial remission and were still undergoing treatment, one survived a month after initial diagnosis and treatment with chemotherapy, and one had palliative care and was euthanised a month after initial diagnosis. The ages at first diagnosis for these eight dogs ranged from 3 years to 11 years and 5 months. The nine SNPs linked to this top SNP spanned the genes SLC43A1, SLC43A3, SSRP1, and PRG3. Two of these SNPs were in non-coding regions. Two other SNPs were in SLC43A1 (18:38659203 and 18:38674512), one of which was within exon 15 and is a 3′ UTR variant (18:38674512). SLC43A1 has two missense variants that are not tolerated (SIFT = 0), G > A at 18:38665640, causing G191S, and C > T at 18:38663900, causing P217S. Three SNPs (18:38732149, 18:38737451, 18:38723578) were within SLC43A3, one of which was within exon 1 and is a 5′ UTR variant (18:38723578). SLC43A3 has one missense variant, a T > C mutation at 18:38728209, causing C56R (SIFT = 0.03). One SNP (18:38805092) was in exon 5 of SSRP1 and is a splice region variant. There was another SNP in SSRP1 (18:38807806) that was significant in the analysis (Table 1), and homozygosity (AA) at this SNP occurred in two cases and not in carriers or controls. SSRP1 has three missense variants that are tolerated (SIFT > 0.05). The remaining linked SNP (18:38766110) was in exon 1 of PRG3 and is annotated as a missense variant (C > T), causing L6F with a SIFT value of 0.07. PRG3 has another missense variant at 18:38766791 but is also tolerated (SIFT = 0.09).
Another significant SNP, 18:41229735, had low to strong linkage (0.25 < r2 < 0.65) to nine SNPs (Figure 5, Table 1). This SNP had a higher frequency of homozygosity for the minor allele in lymphoma cases compared to carriers and controls (0.45, 0.22, and 0.13, respectively), while the frequency of heterozygosity was slightly greater in carriers and controls (0.56 and 0.49) compared to lymphoma cases (0.42) (Table 1). The 14 lymphoma cases that were homozygous for the minor allele included nine confirmed B-cell cases, and the age of first diagnosis averaged 9.7 years (range: 3 to 15.25 years). Only six of these cases had available staging for their lymphoma, with four cases diagnosed at stage III lymphoma and two diagnosed at stage I. Four cases were in complete remission, five cases were still undergoing chemotherapy treatment, one had chemotherapy and had had a recurrence of lymphoma, two had elected palliative care, and treatment and response were unknown for two dogs. This SNP is in exon 2 of the olfactory receptor gene LOC100684610 (OR4C5) and is annotated as a missense variant (A > G), causing K249E with a SIFT value of 1 (low confidence), suggesting that the variant is tolerated (Figure 7). One of the linked SNPs, 18:41144389 (r2 = 0.25), is in exon 3 of DLA-79, and is annotated as a missense variant (C > A), causing P366T with a SIFT value of 0.01. Of the 217 sample genotypes from [27], the SNP 18:41144389 had a minor allele frequency of 0.1, and two out of seven Border Collies in their dataset carried the heterozygous genotype CA. In our population, this SNP was homozygous AA in two cases, one carrier, and two controls and heterozygous AC in 12 cases, seven carriers, and 22 controls, with an overall minor allele frequency that was also 0.1.
The DLA-79 gene is an MHC class Ib gene and is listed in the KEGG viral infection pathways for the human papillomavirus (HPV), Epstein–Barr virus (EBV), Kaposi sarcoma-associated herpesvirus (KSHV), human cytomegalovirus (HCMV), human immunodeficiency virus 1 (HIV-1), and human T-cell leukaemia virus 1 (HTLV-1) and in the viral carcinogenesis pathway. Chromosome 27 was significant for 29 SNPs, spanning 1.5 to 8.9 Mb (Figure 4, Table 2). This included seven SNPs within 3.1 to 3.6 Mb, nine SNPs within 5.1 to 5.8 Mb, and seven SNPs within 8.3 to 8.9 Mb. Three SNPs were within ANO6, two SNPs each were in SCN8A and LOC111092890 (lncRNA), and there was one SNP each in LOC486504 (pseudogene), TARBP2, LOC111092799 (lncRNA), 2632 ANKRD33, FIGNL2, BIN2, SPATS2, LOC111092735, LMBR1L, RHEBL1, WNT10B, and CCNT1.
The expanded 1 to 9.4 Mb region consisted of 190 protein-coding genes, including 12 genes that were listed in cancer pathways from KEGG. These were: SP1, ESPL1, ITGB7, and EIF4B at 1.8 to 2.2 Mb (Supplementary Figure S1); ATF1 at 3.9 Mb (Supplementary Figure S2); WNT1, WNT10B, ADCY6, and CCNT1 at 5.6 to 5.8 Mb (Figure 8); and PFKM, COL2A1, and HDAC7 at 6.6 to 7 Mb (Supplementary Figure S3). The top SNP on chromosome 27, 27:8892980, was in ANO6 and had low to moderate linkage to four other SNPs on ANO6 (0.2 < r2 < 0.5), which included the two other SNPs that were significant in this gene (27:8883501 and 27:8884575) (Figure 9). A total of 16% (five dogs) of lymphoma cases were homozygous for the minor allele at this top SNP, while 4% of carriers and 4% of controls were homozygous (Table 2). ANO6 has two missense mutations annotated, both of which are predicted to not be tolerated (27:8995542 C > T, causing E14K; 27:8995524 C > T, causing E20K; SIFT = 0). There are also two in-frame deletions at 27:8995479–8995496 (causing F29_G35delinsC) and 27:89954802645–8995491 (causing F31_F34del), and there are deletions at 27:8995478–8995479 (causing G35X), 27:8995478 (causing G35X), 27:8995479–8995497 (causing F29_G35delinsX), and 27:8995480–8995492 (causing F31_F34delinsX). Another significant SNP, 27:5603116, was in an intronic region of the WNT10B gene, which is involved in the pathways ‘human papillomavirus infection’, ‘proteoglycans in cancer’, and ‘pathways in cancer’. This SNP was strongly linked (r2 > 0.8) to one SNP in a non-coding region and had low to moderate linkage (0.2 < r2 < 0.5) to three other SNPs, each in a different gene: LMBR1L, KMT2D, and CACNB3 (Figure 9). This SNP was homozygous in 42% of lymphoma cases, 26% of carriers, and 5% of controls (Table 2). Carriers and controls had similar frequencies of heterozygosity at this SNP at 48% and 52%, respectively, whereas lymphoma cases had a heterozygosity frequency of 35% at this SNP. The 13 lymphoma cases that were homozygous for the minor allele included nine confirmed B-cell cases and an affected dam–offspring duo (PBC101 and PBC102) while the age of first diagnosis averaged 8.6 years (range: 1.9 to 13.3 years). Among these dogs, four went into complete remission following chemotherapy, two had partial remission and survived less than a year following the commencement of treatment, three were still undergoing treatment at the time of sampling, three underwent palliative treatment, and one was euthanised due to an advanced stage of lymphoma. WNT10B only has one known missense variant at 27:5604484 (C > G, causing R303G) but is predicted to be tolerated (SIFT = 0.28). LMBR1L has a missense variant at 27:5487906 (C > T, causing R484W in transcript ENSCAFT00000013794.4 and R458W in transcript ENSCAFT00000068147.1) that is not tolerated (SIFT = 0.01). There was also a SNP in this gene that was significant (27:5478927), and the homozygosity of the minor allele at this SNP was similar between cases (23%) and carriers (22%), but greater in these than in controls (3%). KMT2D has many known missense variants, in-frame deletions and insertions, frameshift mutations, and premature stop codon mutations (SIFT scores unavailable).
A backwards stepwise regression of the top 100 SNPs was conducted to find the best combinations of SNPs to discriminate between cases and controls. A total of 74 SNPs were retained across 20 chromosomes (residual standard error (RSE) = 0.1013 on 16 degrees of freedom (DF), adjusted R2 = 0.9379, p = 3.79 × 10−8) (Supplementary Table S2). This included 19 SNPs from chromosome 18 and 16 SNPs from chromosome 27. Haplotype analyses were conducted for the significant regions on chromosomes 18 and 27. There were 18 haplotype blocks that were highly significant (p < 0.0001) on chromosome 18, ranging from 2 to 14 SNPs in size (Table 3). The top haplotype was a 12 SNP block from 18:38038090 to 18:38129762, which spanned seven olfactory genes, and was homozygous in two cases (dogs PBC098 and PBC158) and was not homozygous in any carriers or controls. A majority of the other significant haplotypes also covered olfactory genes: LOC106559997, OR5B21, OR10C10, LOC483503, OR04E05, LOC483541, and LOC100685982. Two haplotypes (3 SNPs and 5 SNPs in size) each had one SNP within CTNND1, which was homozygous only in two cases (PBC098 and PBC158) and not in any carriers or controls. Both dogs had confirmed B-cell lymphoma and entered complete remission following chemotherapy. PBC098 was diagnosed at 3 years with unknown staging, grading, or subtype, while PBC158 was diagnosed at 11 years and 5 months with stage III, high-grade, multicentric lymphoma. There were three significant SNPs and a 5 SNP haplotype spanning ZDHHC5 that was homozygous in three cases (PBC098, PBC099, and PBC158) and not homozygous in any carriers or controls. PBC099 had also had B-cell lymphoma diagnosed at 9 years and entered complete remission following chemotherapy.
On chromosome 27, there were 29 highly significant (p < 0.0001) haplotype blocks, ranging from 2 to 19 SNPs in size (Table 4). The top haplotype was 9 SNPs long (from 27:5104085 to 27:5241391) and spanned the SPATS2 gene. This haplotype was homozygous in 29% of cases (nine dogs), 7% of carriers (two dogs), and 2% of controls (two dogs). The nine dogs that were homozygous for this haplotype included four dogs diagnosed at 4 years old or younger, three of which entered complete remission following therapy while one was euthanised. Five cases were diagnosed at 9 years or older; three of these were still undergoing chemotherapy treatment at the time of sampling, one was undergoing palliative treatment, and one was euthanised.
A NetView plot was also created based on a distance matrix using the genotype data to visualise the relationships between cases, carriers, and control dogs (Figure 10). There were two small groups of controls and two individual control dogs that did not map to the main network. Three cases were part of a subcluster of the main network (Figure 10, top left) which lacked carrier dogs. These three cases had no pedigree information. An inspection of the control dogs in this cluster in the in-house pedigree database showed that this cluster primarily consisted of working Border Collies. There were eight carriers that clustered closely with three cases, which included an affected dam–daughter duo (PBC101 and PBC102) (Figure 10, top right). The other lymphoma cases were mostly spread out through the lower half and right side of the main network.

3.3. GWAS with Binary Phenotype

A separate GWAS was run with just 31 cases and 119 controls using a binary phenotype (1 for affected, 0 for control). The top SNPs did not reach genome-wide significance (Figure 11), but chromosome-wise calculated q-values showed that there were four significant SNPs on chromosome 13 (0.05 < q < 0.1), three significant SNPs on chromosome 14 (0.05 < q < 0.1), 24 significant SNPs on chromosome 18 (0.05 < q < 0.1), and 25 significant SNPs on chromosome 27 (19 SNPs with q < 0.05, six SNPs with 0.05 < q < 0.1) (Figure 12, Table 5). The significant regions on chromosomes 18 (37.7 Mb to 42.3 Mb) and 27 (3.1 Mb to 11.7 Mb) overlapped with the regions identified in the quantitative phenotype GWAS such that 21 SNPs on chromosome 18 and 23 SNPs on chromosome 27 were significant in both analyses. There were three SNPs that were significant on chromosome 18 at 41.7 Mb to 42.3 Mb that were not significant in the quantitative phenotype GWAS, none of which were located within genes, while there were two SNPs that were significant on chromosome 27 and were not significant in the quantitative phenotype GWAS at 4,561,667 bp (within LIMA1) and 11,672,173 bp (within the lncRNA LOC106557903).
The significant SNPs on chromosome 13 spanned 17.4 to 20.7 Mb and included two SNPs within DEPTOR, one SNP within EXT1, and one SNP within the lncRNA LOC111098540 (Table 5). There were no KEGG pathway viral infection or cancer pathway genes within 500 kb upstream and downstream of this region. The significant SNPs on chromosome 14 spanned two SNPs at 3.9 to 4.1 Mb, which included one SNP, within EXOC4, and one SNP at 54,260,890 bp that was not within a gene (Table 5). The expanded region 500 kb upstream and downstream of 3.9 to 4.1 Mb did not contain any KEGG viral infection or cancer pathway genes, and nor did a 500 kb region around the 14:54260890 SNP.
A backwards stepwise regression of the top 100 SNPs was also conducted as per the quantitative GWAS to discriminate between cases and controls (Supplementary Table S3). A total of 79 SNPs remained across 21 chromosomes (RSE = 0.1186 on 3 DF, adjusted R2 = 0.9379, p = 3.79 × 10−8) (Supplementary Table S2). This included 19 SNPs from chromosome 18 and 19 SNPs from chromosome 27. Among these 79 SNPs, 54 were in common with the 74 SNPs that remained after the backwards stepwise regression of the quantitative phenotype GWAS.
Haplotype block association analysis for the significant chromosomes, as detected in the above analysis, found chromosome 18 to have five highly significant blocks (p < 0.0001), ranging from 2 to 14 SNPs in size (Table 6). The 14 SNP haplotype from 18:38233567 to 18:38383741 was the most significant (p = 7.66 × 10−5 ) for this chromosome, occurring only in two cases and no controls, and spanned several olfactory genes. Chromosome 27 had seven highly significant haplotypes ranging from 2 to 12 SNPs in size (Table 7). The most significant (p = 2.82 × 10−6) on this chromosome was an 8 SNP haplotype from 27:5112245 to 27:5241391, overlapping with the most significant haplotype block in chromosome 27 in the quantitative phenotype GWAS, and occurred in nine cases (29% of cases) and two controls (2% of controls) and spanned the gene SPATS2. Notably, there was a 2 SNP haplotype (CA at 27:5592820 to 27:5603116) spanning the WNT10B gene that was homozygous in 42% of cases (13 dogs) and 5% of controls (6 dogs). There were no highly significant haplotype blocks for chromosomes 13 and 14 (p > 0.0001).

3.4. Phenotypic Variance Explained by Genetic Variance (Heritability)

REML analyses were conducted to estimate the phenotypic variance explained by genetic variance. A total of 12 chromosomes were significant (p < 0.05), and the analysis with all autosomes was also highly significant (p = 1.43 × 10−6) (Table 8). Even at the lowest prevalence tested (0.025), all autosomes explained 106.09% (standard error (SE) ± 21.77%) of the disease liability. Chromosome 27 was the most significant (p = 1.34 × 10−6) and explained at least 43.19% (±11.94%) of the disease liability at the lowest prevalence and up to 65.3% (±18.06%) at the highest prevalence (0.1). The significant region on chromosome 27 (1 to 9 Mb) explained less liability, at a minimum of 27.71% (±10.18%). Chromosome 18 was also highly significant (p = 1.21 × 10−3), explaining a minimum of 33.17% (±13.54%) at the lowest prevalence. The significant region on chromosome 18 (37 to 56 Mb) explained more disease liability than all SNPs on chromosome 18, from a minimum of 43.4% (±12.83%) up to 65.63% (±19.4%). Unexpectedly, chromosome 21 explained the most variance (V(G)/Vp = 47.43% (±13.44%), V(G)/Vp_L = 76.08% (±21.57%), p = 0.002).

4. Discussion

Lymphoma is a common cause of mortality in dogs and has been underinvestigated compared to human lymphoma. In this study, pedigree investigations were expanded from our previous study [37] and showed further evidence that there is likely an underlying heritable genetic component to lymphoma as 54 cases were traced to a common ancestor. In the genetic investigations, a quantitative phenotype GWAS was designed that maximised the use of dogs that had known relationships to lymphoma cases by including a “carrier” group based on the assumption that a genetic risk for lymphoma is inherited as a recessive trait. Here, we identify five candidate genes that warrant further investigation: DLA-79, WNT10B, LMBR1L, KMT2D, and CCNT1. We also tested a binary-phenotype GWAS using cases and controls only and found a large overlap in significant regions with the quantitative phenotype GWAS (21 SNPs on chromosome 18 and 23 SNPs on chromosome 27 were significant in both analyses).
Previous studies have investigated the pedigrees of lymphoma-affected dogs in small family groups with between three and nine cases [63,64]. In the present study, we mapped a much larger pedigree for 54 cases and were able to identify several occurrences of first- or second-degree relatives of lymphoma cases that were also diagnosed with lymphoma, including two pairs of affected full siblings, one affected aunt–niece pair, one affected uncle–niece pair, and five affected parent–offspring pairs, suggesting a likely heritable risk. There was further evidence that ancestors were possible carriers of a risk allele, such as the two full-sibling males (dog_44286 and dog_44277) that gave rise to four and twelve cases, respectively, which included three pairs of parent–offspring cases and one aunt–niece occurrence of lymphoma. The common ancestor for all 54 cases, dog_811, was also identified as the common ancestor for many dogs carrying the trapped neutrophil syndrome (TNS) mutation and the neuronal ceroid lipofuscinosis (NCL) mutation in our previous study on Border Collie diversity [30]. Dog_811 was identified as the fourth top-contributing ancestor to the population of dogs born between 2005 and 2015 [30], and so it may appear as a common ancestor for lymphoma simply because its descendants were frequently bred and made substantial contributions to the present generation of Border Collies. However, the familial occurrence of the disease still suggests a genetic risk is being transmitted.
It should be noted that retrospective studies are often limited by the available health information on individuals in the study. It is likely that healthy, lymphoma-unaffected dogs in our pedigree were not captured in our health survey, and with no information on their ages or causes of death, the accuracy of heritability or prevalence estimations of the disease are affected. For example, the NetView plot of the genotyped dogs showed that there were three cases that had no pedigree information in a subcluster of the main network that had no carriers. This may mean that there is another population of Border Collies carrying a risk genotype for lymphoma that was not captured in our pedigree data, but it will require the identification and genotyping of more carrier dogs for this to be elucidated.
The quantitative phenotype GWAS had the advantage of utilising known relatives of lymphoma cases as possible carriers of a genetic risk for lymphoma and identified potential candidate genes with known or predicted associations with lymphoma. The top SNP on chromosome 18 was in a non-coding region but was in low to moderate linkage (0.2 < r2 < 0.5) to SNPs in genes that have shown involvement in other cancers but not lymphoma, including SLC43A1 in prostate cancer [65] and leukaemia [66]; SLC43A3 in angiosarcoma [67]; and SSRP1 (which also had a significant SNP) in colorectal cancer and glioma [68,69]. SSRP1 is a component of the Facilitates Chromatin Transcription (FACT) complex, which plays a role in transcriptional regulation and DNA damage repair and can also accelerate tumour transformation [70]. There are also microRNAs that regulate SSRP1 and have been shown to promote cancer progression or malignancy [71]. Given its broad cancer biology role, it may be considered a candidate in canine lymphoma; however, only two cases were homozygous for the minor allele at the significant SNP and were homozygous for a 3 SNP haplotype spanning this gene.
There was a significant SNP (18:41229735) within chromosome 18 within the olfactory receptor OR4C5, where the homozygous minor allele genotype (AA) was more common in lymphoma cases (45%) compared to carriers (22%) and controls (13%). Dogs have approximately three times as many olfactory genes as humans, but none of the genes have been studied for any involvement in cancers. This significant SNP was linked (r2 = 0.25) to a SNP in exon 3 of DLA-79 that is annotated as a missense variant and predicted to not be tolerated. DLA-79 is a Major Histocompatibility Complex (MHC) class Ib gene and is thought to play a special role in the immune response, likely binding a distinct set of peptides or ligands [72]. DLA-79 has 64% amino acid identity with the consensus sequence of HLA-A, -B, and -C. Risk haplotypes have been identified for human non-Hodgkin’s lymphoma at HLA-A, -B, and -C, and lower expression levels of HLA-G have been associated with aggressive lymphoma and poor prognosis [73,74,75].
There were also significant SNPs on chromosome 18 in the genes ZDHHC5, CTNND1, and TMEM109. ZDHHC5 has been implicated in other cancers [76], while TMEM109 is involved in regulating apoptosis [77]. CTNND1 is a member of the catenin family and is involved with cell adhesion and Rho GTPase activity. It has been implicated in the SRC-family kinase-mediated transformation of cells [78]. Reduced expression has been shown in activated B-cell diffuse large B-cell lymphoma (ABC DLBCL), and variation in sequence or expression is associated with other cancers [79,80]. CTNND1 may have a genetic variant in Border Collies that has influenced the expression of the gene or increases the risk for lymphoma; however, only two cases shared homozygous haplotypes across the gene.
On chromosome 27, three significant SNPs (including the top SNP) were within ANO6 (anoctamin 6). There has yet to be evidence that the gene is involved in lymphoma, but it has shown a role in cellular apoptosis through pyroptosis and ferroptosis [81,82]. The associated region on this chromosome was large (7–8Mb in size) and included 12 genes listed in cancer pathways: SP1, ESPL1, ITGB7, EIF4B, ATF1, WNT1, WNT10B, ADCY6, CCNT1, PFKM, COL2A1, and HDAC7. Among these genes, only WNT10B and CCNT1 harboured a significant SNP. There were also significant haplotypes that had SNPs spanning CCNT1 (5 SNP haplotype) and PFKM (4 SNP haplotype), both of which were more common in lymphoma cases (13–19%) compared to controls (13%).
For the significant SNP in WNT10B, there was a higher frequency of lymphoma cases (42%) that were homozygous for the minor allele than carriers (26%) and controls (5%). WNT10B is directly downstream of the cancer pathway gene WNT1 (~8 kb away). WNT10B and WNT1 are members of the Wnt signalling pathway, which plays a crucial role in development, the regulation of stem cells, and tissue homeostasis [83,84]. The Wnt pathway is implicated in multiple cancers and has been shown to be activated in DLBCL [83,85,86]. Other Wnt genes have been implicated in haematopoietic malignancies, such as WNT5A, which has been identified as a tumour suppressor that inhibits B-cell proliferation and is a motility factor in Hodgkin’s lymphoma cells [87,88]. WNT10B is associated with multiple cancers and an intronless variant has been found in acute myeloid leukaemia [89,90], but the gene has not yet shown involvement in lymphoma. This region could be explored for possible functional variants associated with canine lymphoma risk given the involvement of the Wnt pathway in numerous cancers including haematopoietic malignancies.
The significant WNT10B SNP was also linked (0.2 < r2 < 0.5) to one SNP each in CACNB3, LMBR1L, and KMT2D. CACNB3 encodes a calcium voltage-gated channel and has no known role in cancer. LMBR1L (limb development membrane protein 1 like) has an essential role in lymphopoiesis and lymphoid activation [91]. There was one significant SNP in LMBR1L and there is one known missense variant (27:5487906) in this gene that is not tolerated. LMBR1L should therefore be explored further since mutations in this gene have the potential to affect normal lymphocyte development.
KMT2D (lysine methyltransferase 2D) is a widely studied gene in the cancer context. Multiple mutations and abnormal expression of this tumour suppressor gene and epigenetic modifier have been associated with DLBCL, follicular lymphoma, T-cell lymphomas, and several other cancers [92,93,94,95,96,97,98]. Notably, one study found that the treatment of T-lymphoma cells with the histone deacetylase inhibitor chidamide and the hypomethylating agent decitabine induced apoptosis and stopped tumour growth by increasing the interaction between KMT2D and SPI1 (PU.1) [99], which was in the associated region on chromosome 18 in the present study. The fact that it was within a significant region, and has numerous known mutations, would suggest that KMT2D may harbour a risk genotype for canine lymphoma.
There was one significant SNP in CCNT1 that had a greater frequency of carriers homozygous for the minor allele (22%) compared to cases (13%) and controls (1%). The 5 SNP haplotype that included SNPs in CCNT1 was also homozygous in the same proportion for each group. CCNT1 forms a complex with the CDK9 serine/threonine kinase, and together, CDK9/CCNT1 is required for the differentiation of several cell types, including the differentiation and activation of lymphoid cells [100,101]. An imbalance of expression levels between CDK9 and CCNT1 has been found in several lymphoma types, suggesting a common mechanism of deregulation of transcription in the neoplastic transformation of these cells [100]. If this gene harbours a risk genotype for lymphoma, it is not clear why carriers have a higher proportion of homozygotes compared to cases. It is possible that the “carriers” identified in this study may develop lymphoma later in their life or that this is one factor among a combination of genetic factors necessary to cause lymphoma. The involvement of this gene in lymphoid cells and its expression in several lymphomas make it a good candidate for further investigation for a risk genotype for canine lymphoma.
A significant 4 SNP haplotype spanned the cancer pathway gene PFKM (phosphofructokinase, muscle), and homozygosity for this haplotype was more common in cases (19%) compared to carriers (7%) and controls (3%). PFKM is part of the glycolysis pathway, and posttranslational modifications of the enzyme and silencing by miRNA can promote cancer cell proliferation or adaptation to metabolic stress [102,103]. However, there has yet to be evidence of direct involvement in lymphoma.
The results from the binary-phenotype GWAS almost completely overlapped with the results from the quantitative phenotype, with 21 SNPs on chromosome 18 and 23 SNPs on chromosome 27 being significant in both analyses. There were additional chromosome-wise significant associations on chromosomes 13 and 14, which included SNPs within the genes EXT1, DEPTOR, and EXOC4. EXT1 (exostosin-1) encodes a glycosyltransferase that is required in heparan sulphate biosynthesis. Heparan sulphate is a linear polysaccharide that is found ubiquitously as a proteoglycan and is involved in a wide range of biological processes such as embryonic development, metabolism, and cell signalling, which consequently means that defects in its synthesis causes many different diseases [104]. EXT1 is a tumour suppressor gene that has downregulated expression in acute lymphoblastic leukaemia and is frequently hypermethylated in leukaemia, causing the loss of heparan sulphate synthesis [105]. Given the broad range of effects of EXT1 through heparan sulphate biosynthesis and its role as a tumour suppressor, it is possible this gene could be involved in lymphoma, but this has yet to be reported.
DEPTOR (DEP domain-containing mTOR interacting protein) is another tumour suppressor gene and is an important regulator of mTOR [106]. Mechanistic target of rapamycin (mTOR) is a protein kinase that regulates a range of processes important in cancer, such as cell proliferation and autophagy, and many inhibitors of mTOR have been developed for cancer treatment [107]. Downregulation of DEPTOR has been shown to induce apoptosis and increase sensitivity to doxorubicin in human multiple myeloma cells [108]. Doxorubicin is commonly used in chemotherapy protocols for the treatment of canine lymphoma [109], but whether DEPTOR affects sensitivity to doxorubicin in lymphoma cells is not known. DEPTOR has also been found to regulate migration and cytokine expression in DLBCL cells through its interaction with microRNA-155 (MiR-155) [110]. MiR-155 is one of the most widely studied microRNAs, particularly in lymphoma. It is considered a biomarker for B-cell malignancies, it can increase lymphoma cell motility, and its expression levels have been correlated with prognosis in B-cell lymphoma [111,112,113]. MiR155 can also inhibit the transcription factor SPI1, which was within the associated region in chromosome 18 in the present study, and the upregulation of MiR-155 and downregulation of SPI1 have been observed in numerous lymphoma types [114]. Genetic variants in DEPTOR may affect the interaction with MiR-155 and suggest an avenue for further investigation.
EXOC4 (exocyst complex component 4; also known as SEC8) encodes a component of the exocyst complex that has roles in cell migration [115]. One study examined malignant peripheral sheath tumour cells and found reduced levels of EXOC4 after treatment with doxorubicin and sorafenib, reporting that EXOC4 was involved in the regulation of Bcl-2 [116]. Bcl-2 is one of the most widely studied genes in haematological malignancies, especially in diffuse large B-cell lymphoma (DLBCL) [117,118]. EXOC4 does not currently have a clear role in lymphoma, but it may be involved by sensitising lymphoma cells to doxorubicin or affecting Bcl-2 transcription.
Chromosome 21 explained the greatest variance in disease liability when all SNPs were included in the analyses, while significant regions across chromosomes 18 (37–56 Mb) and 27 (1–9 Mb) explained a considerable proportion of disease liability, even at the lowest prevalence levels tested—43.4% (±12.83%) and 27.71% (±10.18%), respectively. These estimates are higher than those from a previous GWAS on hemangiosarcoma and B-cell lymphoma in Golden Retrievers, which found a region on chromosome 5 (25–40 Mb) that harboured two shared risk loci for both cancers, explaining 22.4% (±10.7%) of variance [22]. The study also estimated the variance explained by the region for B-cell lymphoma only and found that it explained 60% (±25%) at a prevalence of 0.0625 [22]. At the closest prevalence in our study (0.05), the chromosome 18 region explained a similar amount of variance, at 52.93% (±15.65%), albeit across a slightly larger region (19 Mb compared to 15 Mb). The chromosome 27 region in our study explained 33.79% (±12.42%) at a prevalence of 0.05 although this region was only 8 Mb in size.
The GWAS findings in the current study differ from those of previous GWAS on canine lymphoma [22,23,24,35]. Including the present study, each GWAS on canine lymphoma has now identified associated regions on different chromosomes (Chr 2, 4, 5, 8, 11, 14, 18, 20, and 27). This suggests that there are distinct genetic mechanisms underlying the risk of developing lymphoma in certain breeds or breed clades or that common oncogenic pathways are influenced by variants with different frequencies in different breeds. This is supported by a multi-breed multi-cancer study that identified non-coding sequence variants associated with haematopoietic malignancies [35].
The main limitations to this study were the lack of reported details on each lymphoma case. Nearly half of the cases had no immunotype diagnosis, which is common for older cases where palliative care is elected. Most of the genotyped cases were diagnosed as older dogs, and so, parents were mostly deceased or were not available for genotyping. Only four of the youngest cases (≤5 years) had pedigrees available, and only two sets of trios were available for genotyping. Prospective studies including more relatives could explore family-based association testing and, preferably, sequence data, which would increase the chance of sampling rare variants. It could also allow the use of alternate methods, such as the genome-wide association study by proxy (GWAX) method, which utilises first-degree relatives [119]. Lastly, due to our limited sample size, a relaxed q-value cut-off of 0.1 was used to detect more associations that could be biologically relevant; however, this would have increased the risk of false positive associations.
In conclusion, despite limitations in sample size due to opportunistic sampling, pedigree analyses showed evidence of a potential heritable risk of lymphoma, with nine pairs involving an affected parent—offspring, littermates, and an uncle/aunt—niece identified, and 54 cases could be traced to a common ancestor. The pedigree relationships were used to design a GWAS that identified genomic regions and associated genes that may be considered candidates for lymphoma risk in Border Collies. The results from this approach support the case-control GWAS, indicating that the inclusion of putative carriers will be useful for future canine studies.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/vetsci10090581/s1, Supplementary Data S1, sample information; Supplementary Data S2, REML results; Supplementary Data S3, PLINK genotype files (.bed, .bim, .fam) for dogs genotyped on the 170 k array; Supplementary Data S4, PLINK genotype files (.bed, .bim, .fam) for dogs genotyped on the 220 k array; Supplementary Figures S1–S3, regional association plots for chromosome 27; Supplementary Table S1, linear discriminant analysis results for the top 100 SNPs using a quantitative phenotype; Supplementary Table S2, linear discriminant analysis results for the top 100 SNPs using a binary phenotype.

Author Contributions

Conceptualisation, P.X.Y.S., M.S.K. and P.W.; methodology, M.S.K.; formal analysis, P.X.Y.S.; investigation, P.X.Y.S. and P.W.; resources, P.W.; data curation, P.X.Y.S.; writing—original draft preparation, P.X.Y.S.; writing—review and editing, P.X.Y.S. and M.S.K.; supervision, M.S.K. and P.W.; funding acquisition, P.X.Y.S. and PW; visualisation, P.X.Y.S.; interpretation of results, P.X.Y.S., M.S.K. and P.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by funds from the Australian Canine Research Foundation (PW) (University of Sydney code RA399) and donations from the Border Collie Club of New South Wales (University of Sydney code D1350) and the Mac and Jetty lymphoma research fund (University of Sydney code D1312). PXYS was also a recipient of the Australian postgraduate research training program scholarship.

Institutional Review Board Statement

The animal study protocol was approved by the Animal Ethics Committee of the University of Sydney under ethics numbers 37/634/6013.

Informed Consent Statement

Written informed consent has been obtained from the owners of the dogs that participated in this study to publish this paper.

Data Availability Statement

The data presented in this study are available in the supplementary files (Supplementary Data S3 and S4).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Dobson, J.M.; Samuel, S.; Milstein, H.; Rogers, K.; Wood, J.L. Canine neoplasia in the UK: Estimates of incidence rates from a population of insured dogs. J. Small Anim. Pract. 2002, 43, 240–246. [Google Scholar] [CrossRef] [PubMed]
  2. Edwards, D.S.; Henley, W.E.; Harding, E.F.; Dobson, J.M.; Wood, J.L. Breed incidence of lymphoma in a UK population of insured dogs. Vet. Comp. Oncol. 2003, 1, 200–206. [Google Scholar] [CrossRef] [PubMed]
  3. Comazzi, S.; Marelli, S.; Cozzi, M.; Rizzi, R.; Finotello, R.; Henriques, J.; Pastor, J.; Ponce, F.; Rohrer-Bley, C.; Rutgen, B.C.; et al. Breed-associated risks for developing canine lymphoma differ among countries: An European canine lymphoma network study. BMC Vet. Res. 2018, 14, 232. [Google Scholar] [CrossRef] [PubMed]
  4. Pinello, K.C.; Niza-Ribeiro, J.; Fonseca, L.; de Matos, A.J. Incidence, characteristics and geographical distributions of canine and human non-Hodgkin’s lymphoma in the Porto region (North West Portugal). Vet. J. 2019, 245, 70–76. [Google Scholar] [CrossRef]
  5. Modiano, J.F.; Breen, M.; Burnett, R.C.; Parker, H.G.; Inusah, S.; Thomas, R.; Avery, P.R.; Lindblad-Toh, K.; Ostrander, E.A.; Cutter, G.C.; et al. Distinct B-cell and T-cell lymphoproliferative disease prevalence among dog breeds indicates heritable risk. Cancer Res. 2005, 65, 5654–5661. [Google Scholar] [CrossRef]
  6. Pastor, M.; Chalvet-Monfray, K.; Marchal, T.; Keck, G.; Magnol, J.P.; Fournel-Fleury, C.; Ponce, F. Genetic and environmental risk indicators in canine non-Hodgkin’s lymphomas: Breed associations and geographic distribution of 608 cases diagnosed throughout France over 1 year. J. Vet. Intern. Med. 2009, 23, 301–310. [Google Scholar] [CrossRef]
  7. Ito, D.; Frantz, A.M.; Modiano, J.F. Canine lymphoma as a comparative model for human non-Hodgkin lymphoma: Recent progress and applications. Vet. Immunol. Immunopathol. 2014, 159, 192–201. [Google Scholar] [CrossRef]
  8. Richards, K.L.; Suter, S.E. Man’s best friend: What can pet dogs teach us about non-Hodgkin’s lymphoma? Immunol. Rev. 2015, 263, 173–191. [Google Scholar] [CrossRef]
  9. Thomas, R.; Smith, K.C.; Ostrander, E.A.; Galibert, F.; Breen, M. Chromosome aberrations in canine multicentric lymphomas detected with comparative genomic hybridisation and a panel of single locus probes. Br. J. Cancer 2003, 89, 1530–1537. [Google Scholar] [CrossRef]
  10. Ferraresso, S.; Bresolin, S.; Arico, A.; Comazzi, S.; Gelain, M.E.; Riondato, F.; Bargelloni, L.; Marconato, L.; Kronnie, G.; Aresu, L. Epigenetic silencing of TFPI-2 in canine diffuse large B-cell lymphoma. PLoS ONE 2014, 9, e92707. [Google Scholar] [CrossRef]
  11. Ferraresso, S.; Arico, A.; Sanavia, T.; Da Ros, S.; Milan, M.; Cascione, L.; Comazzi, S.; Martini, V.; Giantin, M.; Di Camillo, B.; et al. DNA methylation profiling reveals common signatures of tumorigenesis and defines epigenetic prognostic subtypes of canine Diffuse Large B-cell Lymphoma. Sci. Rep. 2017, 7, 11591. [Google Scholar] [CrossRef] [PubMed]
  12. Cascione, L.; Giudice, L.; Ferraresso, S.; Marconato, L.; Giannuzzi, D.; Napoli, S.; Bertoni, F.; Giugno, R.; Aresu, L. Long Non-Coding RNAs as Molecular Signatures for Canine B-Cell Lymphoma Characterization. Noncoding RNA 2019, 5, 47. [Google Scholar] [CrossRef] [PubMed]
  13. Craig, K.K.L.; Wood, G.A.; Keller, S.M.; Mutsaers, A.J.; Wood, R.D. MicroRNA profiling in canine multicentric lymphoma. PLoS ONE 2019, 14, e0226357. [Google Scholar] [CrossRef]
  14. Epiphanio, T.M.F.; Fernandes, N.; de Oliveira, T.F.; Lopes, P.A.; Ressio, R.A.; Goncalves, S.; Scattone, N.V.; Tedardi, M.V.; Kulikowski, L.D.; Damasceno, J.; et al. Global DNA methylation of peripheral blood leukocytes from dogs bearing multicentric non-Hodgkin lymphomas and healthy dogs: A comparative study. PLoS ONE 2019, 14, e0211898. [Google Scholar] [CrossRef] [PubMed]
  15. Elvers, I.; Turner-Maier, J.; Swofford, R.; Koltookian, M.; Johnson, J.; Stewart, C.; Zhang, C.Z.; Schumacher, S.E.; Beroukhim, R.; Rosenberg, M.; et al. Exome sequencing of lymphomas from three dog breeds reveals somatic mutation patterns reflecting genetic background. Genome Res. 2015, 25, 1634–1645. [Google Scholar] [CrossRef]
  16. Veldhoen, N.; Stewart, J.; Brown, R.; Milner, J. Mutations of the p53 gene in canine lymphoma and evidence for germ line p53 mutations in the dog. Oncogene 1998, 16, 249–255. [Google Scholar] [CrossRef] [PubMed]
  17. Schofield, I.; Stevens, K.B.; Pittaway, C.; O’Neill, D.G.; Fecht, D.; Dobson, J.M.; Brodbelt, D.C. Geographic distribution and environmental risk factors of lymphoma in dogs under primary-care in the UK. J. Small Anim. Pract. 2019, 60, 746–754. [Google Scholar] [CrossRef]
  18. Pinello, K.C.; Santos, M.; Leite-Martins, L.; Niza-Ribeiro, J.; de Matos, A.J. Immunocytochemical study of canine lymphomas and its correlation with exposure to tobacco smoke. Vet. World 2017, 10, 1307–1313. [Google Scholar] [CrossRef]
  19. Takashima-Uebelhoer, B.B.; Barber, L.G.; Zagarins, S.E.; Procter-Gray, E.; Gollenberg, A.L.; Moore, A.S.; Bertone-Johnson, E.R. Household chemical exposures and the risk of canine malignant lymphoma, a model for human non-Hodgkin’s lymphoma. Environ. Res. 2012, 112, 171–176. [Google Scholar] [CrossRef]
  20. Gavazza, A.; Presciuttini, S.; Barale, R.; Lubas, G.; Gugliucci, B. Association between canine malignant lymphoma, living in industrial areas, and use of chemicals by dog owners. J. Vet. Intern. Med. 2001, 15, 190–195. [Google Scholar] [CrossRef]
  21. Marconato, L.; Leo, C.; Girelli, R.; Salvi, S.; Abramo, F.; Bettini, G.; Comazzi, S.; Nardi, P.; Albanese, F.; Zini, E. Association between waste management and cancer in companion animals. J. Vet. Intern. Med. 2009, 23, 564–569. [Google Scholar] [CrossRef]
  22. Tonomura, N.; Elvers, I.; Thomas, R.; Megquier, K.; Turner-Maier, J.; Howald, C.; Sarver, A.L.; Swofford, R.; Frantz, A.M.; Ito, D.; et al. Genome-wide Association Study Identifies Shared Risk Loci Common to Two Malignancies in Golden Retrievers. PLoS Genet. 2015, 11, e1004922. [Google Scholar] [CrossRef] [PubMed]
  23. Hayward, J.J.; Castelhano, M.G.; Oliveira, K.C.; Corey, E.; Balkman, C.; Baxter, T.L.; Casal, M.L.; Center, S.A.; Fang, M.; Garrison, S.J.; et al. Complex disease and phenotype mapping in the domestic dog. Nat. Commun. 2016, 7, 10460. [Google Scholar] [CrossRef]
  24. Labadie, J.D.; Elvers, I.; Feigelson, H.S.; Magzamen, S.; Yoshimoto, J.; Dossey, J.; Burnett, R.; Avery, A.C. Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors. BMC Genom. 2020, 21, 464. [Google Scholar] [CrossRef]
  25. Dutrow, E.V.; Serpell, J.A.; Ostrander, E.A. Domestic dog lineages reveal genetic drivers of behavioral diversification. Cell 2022, 185, 4737–4755.e18. [Google Scholar] [CrossRef] [PubMed]
  26. Hsu, W.T.; Williamson, P.; Khatkar, M.S. Analysis of dog breed diversity using a composite selection index. Sci. Rep. 2023, 13, 1674. [Google Scholar] [CrossRef]
  27. Jagannathan, V.; Drogemuller, C.; Leeb, T.; Dog Biomedical Variant Database, C. A comprehensive biomedical variant catalogue based on whole genome sequences of 582 dogs and eight wolves. Anim. Genet. 2019, 50, 695–704. [Google Scholar] [CrossRef]
  28. Mortlock, S.A.; Khatkar, M.S.; Williamson, P. Comparative Analysis of Genome Diversity in Bullmastiff Dogs. PLoS ONE 2016, 11, e0147941. [Google Scholar] [CrossRef] [PubMed]
  29. Mortlock, S.-A.; Booth, R.; Mazrier, H.; Khatkar, M.; Williamson, P. Visualization of Genome Diversity in German Shepherd Dogs. Bioinform. Biol. Insights 2016, 9, 37–42. [Google Scholar] [CrossRef]
  30. Soh, P.X.Y.; Hsu, W.T.; Khatkar, M.S.; Williamson, P. Evaluation of genetic diversity and management of disease in Border Collie dogs. Sci. Rep. 2021, 11, 6243. [Google Scholar] [CrossRef]
  31. Vaysse, A.; Ratnakumar, A.; Derrien, T.; Axelsson, E.; Rosengren Pielberg, G.; Sigurdsson, S.; Fall, T.; Seppala, E.H.; Hansen, M.S.; Lawley, C.T.; et al. Identification of genomic regions associated with phenotypic variation between dog breeds using selection mapping. PLoS Genet. 2011, 7, e1002316. [Google Scholar] [CrossRef]
  32. Shariflou, M.R.; James, J.W.; Nicholas, F.W.; Wade, C.M. A genealogical survey of Australian registered dog breeds. Vet. J. 2011, 189, 203–210. [Google Scholar] [CrossRef] [PubMed]
  33. Parker, H.G. Genomic analyses of modern dog breeds. Mamm. Genome 2012, 23, 19–27. [Google Scholar] [CrossRef] [PubMed]
  34. Parker, H.G.; Dreger, D.L.; Rimbault, M.; Davis, B.W.; Mullen, A.B.; Carpintero-Ramirez, G.; Ostrander, E.A. Genomic Analyses Reveal the Influence of Geographic Origin, Migration, and Hybridization on Modern Dog Breed Development. Cell Rep. 2017, 19, 697–708. [Google Scholar] [CrossRef]
  35. Hédan, B.; Cadieu, É.; Rimbault, M.; Vaysse, A.; Dufaure de Citres, C.; Devauchelle, P.; Botherel, N.; Abadie, J.; Quignon, P.; Derrien, T.; et al. Identification of common predisposing loci to hematopoietic cancers in four dog breeds. PLoS Genet. 2021, 17, e1009395. [Google Scholar] [CrossRef] [PubMed]
  36. Bennett, P.F.; Taylor, R.; Williamson, P. Demographic risk factors for lymphoma in Australian dogs: 6201 cases. J. Vet. Intern. Med. 2018, 32, 2054–2060. [Google Scholar] [CrossRef] [PubMed]
  37. Cheng, K.Y.; Soh, P.; Bennett, P.F.; Williamson, P. Lymphoma in Australian Border Collies: Survey results and pedigree analyses. Aust. Vet. J. 2019, 97, 14–22. [Google Scholar] [CrossRef]
  38. Sinnwell, J.P.; Therneau, T.M.; Schaid, D.J. The kinship2 R package for pedigree data. Hum. Hered. 2014, 78, 91–93. [Google Scholar] [CrossRef]
  39. Rainer, J.; Taliun, D.; D’Elia, Y.; Pattaro, C.; Domingues, F.S.; Weichenberger, C.X. FamAgg: An R package to evaluate familial aggregation of traits in large pedigrees. Bioinformatics 2016, 32, 1583–1585. [Google Scholar] [CrossRef]
  40. Coster, A.J.R. Pedigree: Pedigree Functions. R Package, Version 1.4. 2013, Volume 1. Available online: https://cran.r-project.org/web/packages/pedigree/index.html (accessed on 8 January 2020).
  41. Browning, B.L.; Zhou, Y.; Browning, S.R. A One-Penny Imputed Genome from Next-Generation Reference Panels. Am. J. Hum. Genet. 2018, 103, 338–348. [Google Scholar] [CrossRef]
  42. Chang, C.C.; Chow, C.C.; Tellier, L.C.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 2015, 4, 7. [Google Scholar] [CrossRef] [PubMed]
  43. Wijnrocx, K.; Francois, L.; Stinckens, A.; Janssens, S.; Buys, N. Half of 23 Belgian dog breeds has a compromised genetic diversity, as revealed by genealogical and molecular data analysis. J. Anim. Breed. Genet. 2016, 133, 375–383. [Google Scholar] [CrossRef]
  44. Bennett, P.; Williamson, P.; Taylor, R. Review of Canine Lymphoma Treated with Chemotherapy-Outcomes and Prognostic Factors. Vet. Sci. 2023, 10, 342. [Google Scholar] [CrossRef] [PubMed]
  45. Vail, D.M. Levels of evidence in canine oncology trials--a case in point. Vet. Comp. Oncol. 2013, 11, 167–168. [Google Scholar] [CrossRef] [PubMed]
  46. Pratap, S.; Scordino, T.S. Molecular and cellular genetics of non-Hodgkin lymphoma: Diagnostic and prognostic implications. Exp. Mol. Pathol. 2019, 106, 44–51. [Google Scholar] [CrossRef] [PubMed]
  47. Yang, J.; Lee, S.H.; Goddard, M.E.; Visscher, P.M. GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 2011, 88, 76–82. [Google Scholar] [CrossRef]
  48. Storey, J.D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 2003, 100, 9440–9445. [Google Scholar] [CrossRef]
  49. Turner, D. qqman: An R package for visualizing GWAS results using Q-Q and manhattan plots. J. Open. Source Softw. 2018, 3, 731. [Google Scholar] [CrossRef]
  50. He, F.; Ding, S.; Wang, H.; Qin, F. IntAssoPlot: An R Package for Integrated Visualization of Genome-Wide Association Study Results with Gene Structure and Linkage Disequilibrium Matrix. Front. Genet. 2020, 11, 260. [Google Scholar] [CrossRef]
  51. Sabik, O.L.; Calabrese, G.M.; Taleghani, E.; Ackert-Bicknell, C.L.; Farber, C.R. Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data. Cell Rep. 2020, 32, 108145. [Google Scholar] [CrossRef]
  52. Tenenbaum, D. KEGGREST. R Package Version 1.24.1. Available online: https://bioconductor.riken.jp/packages/3.8/bioc/manuals/KEGGREST/man/KEGGREST.pdf. (accessed on 8 January 2020).
  53. Esau, D. Viral Causes of Lymphoma: The History of Epstein-Barr Virus and Human T-Lymphotropic Virus 1. Virol. Res. Treat. 2017, 8, 1–5. [Google Scholar] [CrossRef] [PubMed]
  54. Torres, H.A.; Kontoyiannis, D.P.; Aguilera, E.A.; Younes, A.; Luna, M.A.; Tarrand, J.J.; Nogueras, G.M.; Raad, I.I.; Chemaly, R.F. Cytomegalovirus infection in patients with lymphoma: An important cause of morbidity and mortality. Clin. Lymphoma Myeloma 2006, 6, 393–398. [Google Scholar] [CrossRef] [PubMed]
  55. Mehravaran, H.; Makvandi, M.; Samarbaf Zade, A.; Neisi, N.; Kiani, H.; Radmehr, H.; Shahani, T.; Hoseini, S.Z.; Ranjbari, N.; Nahid Samiei, R. Association of Human Cytomegalovirus with Hodgkin’s Disease and Non-Hodgkin’s lymphomas. Asian Pac. J. Cancer Prev. 2017, 18, 593–597. [Google Scholar] [CrossRef] [PubMed]
  56. Yates, A.D.; Achuthan, P.; Akanni, W.; Allen, J.; Allen, J.; Alvarez-Jarreta, J.; Amode, M.R.; Armean, I.M.; Azov, A.G.; Bennett, R.; et al. Ensembl 2020. Nucleic Acids Res. 2020, 48, D682–D688. [Google Scholar] [CrossRef]
  57. Ng, P.C.; Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31, 3812–3814. [Google Scholar] [CrossRef]
  58. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef]
  59. Steinig, E.J.; Neuditschko, M.; Khatkar, M.S.; Raadsma, H.W.; Zenger, K.R. netview p: A network visualization tool to unravel complex population structure using genome-wide SNPs. Mol. Ecol. Resour. 2016, 16, 216–227. [Google Scholar] [CrossRef]
  60. Allaire, J.J.; Gandrud, C.; Russell, K.; Yetman, C. networkD3: D3 JavaScript Network Graphs from R. R Package Version 0.4. Available online: https://cran.r-project.org/package=networkD3 (accessed on 8 January 2020).
  61. Lee, S.H.; Wray, N.R.; Goddard, M.E.; Visscher, P.M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 2011, 88, 294–305. [Google Scholar] [CrossRef]
  62. Van Rooyen, L.J.; Hooijberg, E.; Reyers, F. Breed prevalence of canine lymphoma in South Africa. J. S. Afr. Vet. Assoc. 2018, 89, e1–e11. [Google Scholar] [CrossRef]
  63. Onions, D.E. A prospective survey of familial canine lymphosarcoma. J. Natl. Cancer Inst. 1984, 72, 909–912. [Google Scholar]
  64. Lobetti, R.G. Lymphoma in 3 related Rottweilers from a single household. J. S. Afr. Vet. Assoc. 2009, 80, 103–105. [Google Scholar] [CrossRef] [PubMed]
  65. Cole, K.A.; Chuaqui, R.F.; Katz, K.; Pack, S.; Zhuang, Z.; Cole, C.E.; Lyne, J.C.; Linehan, W.M.; Liotta, L.A.; Emmert-Buck, M.R. cDNA sequencing and analysis of POV1 (PB39): A novel gene up-regulated in prostate cancer. Genomics 1998, 51, 282–287. [Google Scholar] [CrossRef] [PubMed]
  66. Xu, H.; Yang, W.; Perez-Andreu, V.; Devidas, M.; Fan, Y.; Cheng, C.; Pei, D.; Scheet, P.; Burchard, E.G.; Eng, C.; et al. Novel susceptibility variants at 10p12.31-12.2 for childhood acute lymphoblastic leukemia in ethnically diverse populations. J. Natl. Cancer Inst. 2013, 105, 733–742. [Google Scholar] [CrossRef]
  67. Shimozono, N.; Jinnin, M.; Masuzawa, M.; Masuzawa, M.; Wang, Z.; Hirano, A.; Tomizawa, Y.; Etoh-Kira, T.; Kajihara, I.; Harada, M.; et al. NUP160-SLC43A3 is a novel recurrent fusion oncogene in angiosarcoma. Cancer Res. 2015, 75, 4458–4465. [Google Scholar] [CrossRef]
  68. Liao, J.; Tao, X.; Ding, Q.; Liu, J.; Yang, X.; Yuan, F.E.; Yang, J.A.; Liu, B.; Xiang, G.A.; Chen, Q. SSRP1 silencing inhibits the proliferation and malignancy of human glioma cells via the MAPK signaling pathway. Oncol. Rep. 2017, 38, 2667–2676. [Google Scholar] [CrossRef] [PubMed]
  69. Wu, W.; He, K.; Guo, Q.; Chen, J.; Zhang, M.; Huang, K.; Yang, D.; Wu, L.; Deng, Y.; Luo, X.; et al. SSRP1 promotes colorectal cancer progression and is negatively regulated by miR-28-5p. J. Cell. Mol. Med. 2019, 23, 3118–3129. [Google Scholar] [CrossRef]
  70. Jeronimo, C.; Robert, F. The histone chaperone FACT: A guardian of chromatin structure integrity. Transcription 2022, 13, 16–38. [Google Scholar] [CrossRef] [PubMed]
  71. Ding, Q.; He, K.; Luo, T.; Deng, Y.; Wang, H.; Liu, H.; Zhang, J.; Chen, K.; Xiao, J.; Duan, X.; et al. SSRP1 Contributes to the Malignancy of Hepatocellular Carcinoma and Is Negatively Regulated by miR-497. Mol. Ther. 2016, 24, 903–914. [Google Scholar] [CrossRef]
  72. Wagner, J.L. Molecular Organization of the Canine Major Histocompatibility Complex. J. Hered. 2003, 94, 23–26. [Google Scholar] [CrossRef]
  73. Wang, S.S.; Abdou, A.M.; Morton, L.M.; Thomas, R.; Cerhan, J.R.; Gao, X.; Cozen, W.; Rothman, N.; Davis, S.; Severson, R.K.; et al. Human leukocyte antigen class I and II alleles in non-Hodgkin lymphoma etiology. Blood 2010, 115, 4820–4823. [Google Scholar] [CrossRef]
  74. Zhong, C.; Gragert, L.; Maiers, M.; Hill, B.T.; Garcia-Gomez, J.; Gendzekhadze, K.; Senitzer, D.; Song, J.; Weisenburger, D.; Goldstein, L.; et al. The association between HLA and non-Hodgkin lymphoma subtypes, among a transplant-indicated population. Leuk. Lymphoma 2019, 60, 2899–2908. [Google Scholar] [CrossRef] [PubMed]
  75. Amiot, L.; Onno, M.; Lamy, T.; Dauriac, C.; Le Prise, P.Y.; Fauchet, R.; Drenou, B. Loss of HLA molecules in B lymphomas is associated with an aggressive clinical course. Br. J. Haematol. 1998, 100, 655–663. [Google Scholar] [CrossRef] [PubMed]
  76. Zhou, B.; Hao, Q.; Liang, Y.; Kong, E. Protein palmitoylation in cancer: Molecular functions and therapeutic potential. Mol. Oncol. 2023, 17, 3–26. [Google Scholar] [CrossRef]
  77. Yamashita, A.; Taniwaki, T.; Kaikoi, Y.; Yamazaki, T. Protective role of the endoplasmic reticulum protein mitsugumin23 against ultraviolet C-induced cell death. FEBS Lett. 2013, 587, 1299–1303. [Google Scholar] [CrossRef] [PubMed]
  78. Hong, J.Y.; Oh, I.H.; McCrea, P.D. Phosphorylation and isoform use in p120-catenin during development and tumorigenesis. Biochim. Biophys. Acta 2016, 1863, 102–114. [Google Scholar] [CrossRef] [PubMed]
  79. Noordhuis, M.G.; Fehrmann, R.S.; Wisman, G.B.; Nijhuis, E.R.; van Zanden, J.J.; Moerland, P.D.; Ver Loren van Themaat, E.; Volders, H.H.; Kok, M.; ten Hoor, K.A.; et al. Involvement of the TGF-beta and beta-catenin pathways in pelvic lymph node metastasis in early-stage cervical cancer. Clin. Cancer Res. 2011, 17, 1317–1330. [Google Scholar] [CrossRef]
  80. Wang, W.; Fei, Y.; Liu, S.L. CTNND(1) 755 T>G Promoter Polymorphism and Risk of Pancreatic Carcinoma in Chinese. J. Clin. Lab. Anal. 2017, 31, e22055. [Google Scholar] [CrossRef]
  81. Ousingsawat, J.; Schreiber, R.; Kunzelmann, K. TMEM16F/Anoctamin 6 in Ferroptotic Cell Death. Cancers 2019, 11, 625. [Google Scholar] [CrossRef]
  82. Ousingsawat, J.; Wanitchakool, P.; Schreiber, R.; Kunzelmann, K. Contribution of TMEM16F to pyroptotic cell death. Cell Death Dis. 2018, 9, 300. [Google Scholar] [CrossRef]
  83. Reya, T.; Clevers, H. Wnt signalling in stem cells and cancer. Nature 2005, 434, 843–850. [Google Scholar] [CrossRef]
  84. Rim, E.Y.; Clevers, H.; Nusse, R. The Wnt Pathway: From Signaling Mechanisms to Synthetic Modulators. Annu. Rev. Biochem. 2022, 91, 571–598. [Google Scholar] [CrossRef]
  85. Janovská, P.; Bryja, V. Wnt signalling pathways in chronic lymphocytic leukaemia and B-cell lymphomas. Br. J. Pharmacol. 2017, 174, 4701–4715. [Google Scholar] [CrossRef]
  86. Zhan, T.; Rindtorff, N.; Boutros, M. Wnt signaling in cancer. Oncogene 2017, 36, 1461–1473. [Google Scholar] [CrossRef]
  87. Grainger, S.; Traver, D.; Willert, K. Wnt Signaling in Hematological Malignancies. Prog. Mol. Biol. Transl. Sci. 2018, 153, 321–341. [Google Scholar] [CrossRef]
  88. Linke, F.; Zaunig, S.; Nietert, M.M.; von Bonin, F.; Lutz, S.; Dullin, C.; Janovská, P.; Beissbarth, T.; Alves, F.; Klapper, W.; et al. WNT5A: A motility-promoting factor in Hodgkin lymphoma. Oncogene 2017, 36, 13–23. [Google Scholar] [CrossRef]
  89. El Ayachi, I.; Fatima, I.; Wend, P.; Alva-Ornelas, J.A.; Runke, S.; Kuenzinger, W.L.; Silva, J.; Silva, W.; Gray, J.K.; Lehr, S.; et al. The WNT10B Network Is Associated with Survival and Metastases in Chemoresistant Triple-Negative Breast Cancer. Cancer Res. 2019, 79, 982–993. [Google Scholar] [CrossRef]
  90. Lazzaroni, F.; Del Giacco, L.; Biasci, D.; Turrini, M.; Prosperi, L.; Brusamolino, R.; Cairoli, R.; Beghini, A. Intronless WNT10B-short variant underlies new recurrent allele-specific rearrangement in acute myeloid leukaemia. Sci. Rep. 2016, 6, 37201. [Google Scholar] [CrossRef]
  91. Choi, J.H.; Zhong, X.; McAlpine, W.; Liao, T.C.; Zhang, D.; Fang, B.; Russell, J.; Ludwig, S.; Nair-Gill, E.; Zhang, Z.; et al. LMBR1L regulates lymphopoiesis through Wnt/β-catenin signaling. Science 2019, 364, eaau0812. [Google Scholar] [CrossRef]
  92. Morin, R.D.; Mendez-Lago, M.; Mungall, A.J.; Goya, R.; Mungall, K.L.; Corbett, R.D.; Johnson, N.A.; Severson, T.M.; Chiu, R.; Field, M.; et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 2011, 476, 298–303. [Google Scholar] [CrossRef]
  93. Lohr, J.G.; Stojanov, P.; Lawrence, M.S.; Auclair, D.; Chapuy, B.; Sougnez, C.; Cruz-Gordillo, P.; Knoechel, B.; Asmann, Y.W.; Slager, S.L.; et al. Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc. Natl. Acad. Sci. USA 2012, 109, 3879–3884. [Google Scholar] [CrossRef]
  94. Ye, H.; Lu, L.; Ge, B.; Gao, S.; Ma, Y.; Liang, B.; Yu, K.; Yang, K. MLL2 protein is a prognostic marker for gastrointestinal diffuse large B-cell lymphoma. Int. J. Clin. Exp. Pathol. 2015, 8, 13043–13050. [Google Scholar]
  95. Ortega-Molina, A.; Boss, I.W.; Canela, A.; Pan, H.; Jiang, Y.; Zhao, C.; Jiang, M.; Hu, D.; Agirre, X.; Niesvizky, I.; et al. The histone lysine methyltransferase KMT2D sustains a gene expression program that represses B cell lymphoma development. Nat. Med. 2015, 21, 1199–1208. [Google Scholar] [CrossRef]
  96. da Silva Almeida, A.C.; Abate, F.; Khiabanian, H.; Martinez-Escala, E.; Guitart, J.; Tensen, C.P.; Vermeer, M.H.; Rabadan, R.; Ferrando, A.; Palomero, T. The mutational landscape of cutaneous T cell lymphoma and Sézary syndrome. Nat. Genet. 2015, 47, 1465–1470. [Google Scholar] [CrossRef] [PubMed]
  97. Johansson, P.; Klein-Hitpass, L.; Grabellus, F.; Arnold, G.; Klapper, W.; Pförtner, R.; Dührsen, U.; Eckstein, A.; Dürig, J.; Küppers, R. Recurrent mutations in NF-κB pathway components, KMT2D, and NOTCH1/2 in ocular adnexal MALT-type marginal zone lymphomas. Oncotarget 2016, 7, 62627–62639. [Google Scholar] [CrossRef] [PubMed]
  98. Froimchuk, E.; Jang, Y.; Ge, K. Histone H3 lysine 4 methyltransferase KMT2D. Gene 2017, 627, 337–342. [Google Scholar] [CrossRef]
  99. Ji, M.M.; Huang, Y.H.; Huang, J.Y.; Wang, Z.F.; Fu, D.; Liu, H.; Liu, F.; Leboeuf, C.; Wang, L.; Ye, J.; et al. Histone modifier gene mutations in peripheral T-cell lymphoma not otherwise specified. Haematologica 2018, 103, 679–687. [Google Scholar] [CrossRef] [PubMed]
  100. Bellan, C.; De Falco, G.; Lazzi, S.; Micheli, P.; Vicidomini, S.; Schürfeld, K.; Amato, T.; Palumbo, A.; Bagella, L.; Sabattini, E.; et al. CDK9/CYCLIN T1 expression during normal lymphoid differentiation and malignant transformation. J. Pathol. 2004, 203, 946–952. [Google Scholar] [CrossRef]
  101. De Falco, G.; Leucci, E.; Onnis, A.; Bellan, C.; Tigli, C.; Wirths, S.; Cerino, G.; Cocco, M.; Crupi, D.; De Luca, A.; et al. Cdk9/Cyclin T1 complex: A key player during the activation/differentiation process of normal lymphoid B cells. J. Cell. Physiol. 2008, 215, 276–282. [Google Scholar] [CrossRef]
  102. Šmerc, A.; Sodja, E.; Legiša, M. Posttranslational modification of 6-phosphofructo-1-kinase as an important feature of cancer metabolism. PLoS ONE 2011, 6, e19645. [Google Scholar] [CrossRef]
  103. Yang, Y.; Ishak Gabra, M.B.; Hanse, E.A.; Lowman, X.H.; Tran, T.Q.; Li, H.; Milman, N.; Liu, J.; Reid, M.A.; Locasale, J.W.; et al. MiR-135 suppresses glycolysis and promotes pancreatic cancer cell adaptation to metabolic stress by targeting phosphofructokinase-1. Nat. Commun. 2019, 10, 809. [Google Scholar] [CrossRef] [PubMed]
  104. Nadanaka, S.; Kitagawa, H. Heparan sulphate biosynthesis and disease. J. Biochem. 2008, 144, 7–14. [Google Scholar] [CrossRef] [PubMed]
  105. Liu, N.W.; Huang, X.; Liu, S.; Lu, Y. EXT1, Regulated by MiR-665, Promotes Cell Apoptosis via ERK1/2 Signaling Pathway in Acute Lymphoblastic Leukemia. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2019, 25, 6491–6503. [Google Scholar] [CrossRef]
  106. Caron, A.; Briscoe, D.M.; Richard, D.; Laplante, M. DEPTOR at the Nexus of Cancer, Metabolism, and Immunity. Physiol. Rev. 2018, 98, 1765–1803. [Google Scholar] [CrossRef]
  107. Hua, H.; Kong, Q.; Zhang, H.; Wang, J.; Luo, T.; Jiang, Y. Targeting mTOR for cancer therapy. J. Hematol. Oncol. 2019, 12, 71. [Google Scholar] [CrossRef] [PubMed]
  108. Zhang, H.; Chen, J.; Zeng, Z.; Que, W.; Zhou, L. Knockdown of DEPTOR induces apoptosis, increases chemosensitivity to doxorubicin and suppresses autophagy in RPMI-8226 human multiple myeloma cells in vitro. Int. J. Mol. Med. 2013, 31, 1127–1134. [Google Scholar] [CrossRef] [PubMed]
  109. Zandvliet, M. Canine lymphoma: A review. Vet. Q. 2016, 36, 76–104. [Google Scholar] [CrossRef]
  110. Jabłońska, E.; Białopiotrowicz, E.; Szydłowski, M.; Prochorec-Sobieszek, M.; Juszczyński, P.; Szumera-Ciećkiewicz, A. DEPTOR is a microRNA-155 target regulating migration and cytokine production in diffuse large B-cell lymphoma cells. Exp. Hematol. 2020, 88, 56–67.e52. [Google Scholar] [CrossRef]
  111. Dagan, L.N.; Jiang, X.; Bhatt, S.; Cubedo, E.; Rajewsky, K.; Lossos, I.S. miR-155 regulates HGAL expression and increases lymphoma cell motility. Blood 2012, 119, 513–520. [Google Scholar] [CrossRef] [PubMed]
  112. Due, H.; Svendsen, P.; Bødker, J.S.; Schmitz, A.; Bøgsted, M.; Johnsen, H.E.; El-Galaly, T.C.; Roug, A.S.; Dybkær, K. miR-155 as a Biomarker in B-Cell Malignancies. BioMed Res. Int. 2016, 2016, 9513037. [Google Scholar] [CrossRef]
  113. Ahmadvand, M.; Eskandari, M.; Pashaiefar, H.; Yaghmaie, M.; Manoochehrabadi, S.; Khakpour, G.; Sheikhsaran, F.; Montazer Zohour, M. Over expression of circulating miR-155 predicts prognosis in diffuse large B-cell lymphoma. Leuk. Res. 2018, 70, 45–48. [Google Scholar] [CrossRef]
  114. Huskova, H.; Korecka, K.; Karban, J.; Vargova, J.; Vargova, K.; Dusilkova, N.; Trneny, M.; Stopka, T. Oncogenic microRNA-155 and its target PU.1: An integrative gene expression study in six of the most prevalent lymphomas. Int. J. Hematol. 2015, 102, 441–450. [Google Scholar] [CrossRef]
  115. Tanaka, T.; Iino, M. Sec8 regulates cytokeratin8 phosphorylation and cell migration by controlling the ERK and p38 MAPK signalling pathways. Cell Signal. 2015, 27, 1110–1119. [Google Scholar] [CrossRef] [PubMed]
  116. Tanaka, T.; Kikuchi, N.; Goto, K.; Iino, M. Sec6/8 regulates Bcl-2 and Mcl-1, but not Bcl-xl, in malignant peripheral nerve sheath tumor cells. Apoptosis Int. J. Program. Cell Death 2016, 21, 594–608. [Google Scholar] [CrossRef] [PubMed]
  117. Singh, K.; Briggs, J.M. Functional Implications of the spectrum of BCL2 mutations in Lymphoma. Mutat. Res. Rev. Mutat. Res. 2016, 769, 1–18. [Google Scholar] [CrossRef]
  118. Schuetz, J.M.; Johnson, N.A.; Morin, R.D.; Scott, D.W.; Tan, K.; Ben-Nierah, S.; Boyle, M.; Slack, G.W.; Marra, M.A.; Connors, J.M.; et al. BCL2 mutations in diffuse large B-cell lymphoma. Leukemia 2012, 26, 1383–1390. [Google Scholar] [CrossRef]
  119. Liu, J.Z.; Erlich, Y.; Pickrell, J.K. Case-control association mapping by proxy using family history of disease. Nat. Genet. 2017, 49, 325–331. [Google Scholar] [CrossRef]
Figure 1. Distribution of age at first diagnosis for 71 lymphoma cases.
Figure 1. Distribution of age at first diagnosis for 71 lymphoma cases.
Vetsci 10 00581 g001
Figure 2. Pedigree of 54 lymphoma cases descending from dog_811. The most direct pathway for each lymphoma-affected dog to dog_811 is shown; some individuals may be related through other pedigree pathways but these pathways are not shown. Dotted lines connect instances where an individual is repeated in the pedigree. The year of birth and genotype ID, if available or relevant, is indicated below the dog ID for each dog.
Figure 2. Pedigree of 54 lymphoma cases descending from dog_811. The most direct pathway for each lymphoma-affected dog to dog_811 is shown; some individuals may be related through other pedigree pathways but these pathways are not shown. Dotted lines connect instances where an individual is repeated in the pedigree. The year of birth and genotype ID, if available or relevant, is indicated below the dog ID for each dog.
Vetsci 10 00581 g002
Figure 3. NetView relationship network of 215 dogs based on a pedigree kinship matrix. Clustered based on a k-value of 10. A total of 54 lymphoma cases (22 genotyped), 114 normal (12 genotyped) dogs, and 40 other genotyped dogs with unknown health status are shown.
Figure 3. NetView relationship network of 215 dogs based on a pedigree kinship matrix. Clustered based on a k-value of 10. A total of 54 lymphoma cases (22 genotyped), 114 normal (12 genotyped) dogs, and 40 other genotyped dogs with unknown health status are shown.
Vetsci 10 00581 g003
Figure 4. Genome-wide association analysis results for the quantitative phenotype (31 lymphoma cases, 27 carriers, and 119 controls). (A) Manhattan plot of −log10 p-values from mixed linear model association analysis. (B) QQ-plot of expected and observed −log10 p-values; shaded area indicates 95% confidence interval.
Figure 4. Genome-wide association analysis results for the quantitative phenotype (31 lymphoma cases, 27 carriers, and 119 controls). (A) Manhattan plot of −log10 p-values from mixed linear model association analysis. (B) QQ-plot of expected and observed −log10 p-values; shaded area indicates 95% confidence interval.
Vetsci 10 00581 g004
Figure 5. Regional Manhattan plots for the quantitative phenotype results on (A) chromosome 18 and (B) chromosome 27. The blue line indicates a chromosome q-value cut-off of 0.05 and the red line indicates a q-value cut-off of 0.1.
Figure 5. Regional Manhattan plots for the quantitative phenotype results on (A) chromosome 18 and (B) chromosome 27. The blue line indicates a chromosome q-value cut-off of 0.05 and the red line indicates a q-value cut-off of 0.1.
Vetsci 10 00581 g005
Figure 6. Regional association plot for the most significant region on chromosome 18. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut-offs of 0.05 and 0.1, respectively. The top associated SNP for this chromosome, 18:38704682, is highlighted in purple. The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from NCBI, are shown in the middle box. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Figure 6. Regional association plot for the most significant region on chromosome 18. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut-offs of 0.05 and 0.1, respectively. The top associated SNP for this chromosome, 18:38704682, is highlighted in purple. The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from NCBI, are shown in the middle box. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Vetsci 10 00581 g006
Figure 7. Regional association plot for a region of interest on chromosome 18. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut offs of 0.05 and 0.1, respectively. The top associated SNP in this region is highlighted (purple dot). The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from the NCBI, are shown in the middle box, with viral infection or cancer-associated genes identified through KEGG pathways highlighted in red. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Figure 7. Regional association plot for a region of interest on chromosome 18. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut offs of 0.05 and 0.1, respectively. The top associated SNP in this region is highlighted (purple dot). The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from the NCBI, are shown in the middle box, with viral infection or cancer-associated genes identified through KEGG pathways highlighted in red. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Vetsci 10 00581 g007
Figure 8. Regional association plot for the region of interest on chromosome 27. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut offs of 0.05 and 0.1, respectively. The top associated SNP in this region is highlighted (purple dot). The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from the NCBI, are shown in the middle box, with viral infection or cancer-associated genes identified through KEGG pathways highlighted in red. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Figure 8. Regional association plot for the region of interest on chromosome 27. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut offs of 0.05 and 0.1, respectively. The top associated SNP in this region is highlighted (purple dot). The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from the NCBI, are shown in the middle box, with viral infection or cancer-associated genes identified through KEGG pathways highlighted in red. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Vetsci 10 00581 g008
Figure 9. Regional association plot for the most significant region on chromosome 27. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut offs of 0.05 and 0.1, respectively. The top associated SNP in this region is highlighted (purple dot). The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from the NCBI, are shown in the middle box, with viral infection or cancer-associated genes identified through KEGG pathways highlighted in red. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Figure 9. Regional association plot for the most significant region on chromosome 27. The top of the plot indicates −log10 p-values for each SNP from the mixed linear model association analysis. Red and blue lines indicate q-value cut offs of 0.05 and 0.1, respectively. The top associated SNP in this region is highlighted (purple dot). The protein-coding genes (CanFam3.1, assembly GCF_000002285.3) in the region, fetched from the NCBI, are shown in the middle box, with viral infection or cancer-associated genes identified through KEGG pathways highlighted in red. Linkage disequilibrium (r2) in the region is shown in the bottom triangle.
Vetsci 10 00581 g009
Figure 10. NetView relationship network of genotyped lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) based on a distance matrix. Clustered based on a k-value of 10.
Figure 10. NetView relationship network of genotyped lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) based on a distance matrix. Clustered based on a k-value of 10.
Vetsci 10 00581 g010
Figure 11. Genome-wide association results for the binary phenotype (31 lymphoma cases, 119 controls). (A) Manhattan plot of −log10 p-values from mixed linear model association analysis. (B) QQplot of expected and observed −log10 p-values; shaded area indicates 95% confidence interval.
Figure 11. Genome-wide association results for the binary phenotype (31 lymphoma cases, 119 controls). (A) Manhattan plot of −log10 p-values from mixed linear model association analysis. (B) QQplot of expected and observed −log10 p-values; shaded area indicates 95% confidence interval.
Vetsci 10 00581 g011
Figure 12. Regional Manhattan plots for the GWAS results using a binary phenotype on (A) chromosome 13, (B) chromosome 14, (C) chromosome 18, and (D) chromosome 27. The blue line indicates a chromosome q-value cut-off of 0.05 and the red line indicates a q-value cut-off of 0.1.
Figure 12. Regional Manhattan plots for the GWAS results using a binary phenotype on (A) chromosome 13, (B) chromosome 14, (C) chromosome 18, and (D) chromosome 27. The blue line indicates a chromosome q-value cut-off of 0.05 and the red line indicates a q-value cut-off of 0.1.
Vetsci 10 00581 g012
Table 1. Frequency of homozygosity and heterozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) for significant SNPs (q < 0.1) on chromosome 18. CanFam3.1 (assembly GCF_000002285.3) genes were fetched from NCBI. A1 = minor allele; A2 = major allele; Freq = frequency; b = beta-coefficient; SE = standard error; Lym = lymphoma; Carr = carriers; Cont = controls.
Table 1. Frequency of homozygosity and heterozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) for significant SNPs (q < 0.1) on chromosome 18. CanFam3.1 (assembly GCF_000002285.3) genes were fetched from NCBI. A1 = minor allele; A2 = major allele; Freq = frequency; b = beta-coefficient; SE = standard error; Lym = lymphoma; Carr = carriers; Cont = controls.
Location (Chr:BP) p-Value q-Value A1 A2 A1
Freq
b SE Gene Frequency of A1A1 (No. of Dogs) Frequency of A1A2 (No. of Dogs)
Lym.Carr.Cont.Lym.Carr.Cont.
18:38704682 1.32 × 10−50.022 A G 0.32 0.36 0.08 -0.26 (8) 0.19 (5) 0.08 (9) 0.55 (17) 0.33 (9) 0.37 (44)
18:38233567 2.80 × 10−50.022 A G 0.12 0.55 0.13 -0.06 (2) 0 (0) 0 (0) 0.42 (13) 0.41 (11) 0.13 (15)
18:38719709 4.12 × 10−50.022 G A 0.32 0.34 0.08 -0.23 (7) 0.22 (6) 0.08 (9) 0.55 (17) 0.37 (10) 0.34 (41)
18:38502268 4.15 × 10−50.022 A G 0.14 0.50 0.12 ZDHHC50.1 (3) 0 (0) 0 (0) 0.39 (12) 0.41 (11) 0.16 (19)
18:38507461 4.15 × 10−50.022 A G 0.14 0.50 0.12 ZDHHC50.1 (3) 0 (0) 0 (0) 0.39 (12) 0.41 (11) 0.16 (19)
18:38510335 4.15 × 10−50.022 A G 0.14 0.50 0.12 ZDHHC50.1 (3) 0 (0) 0 (0) 0.39 (12) 0.41 (11) 0.16 (19)
18:37737740 5.70 × 10−50.022 C A 0.14 0.49 0.12 -0.06 (2) 0 (0) 0 (0) 0.45 (14) 0.41 (11) 0.16 (19)
18:39140112 5.97 × 10−50.022 G A 0.25 0.38 0.09 LOC1006889970.16 (5) 0.07 (2) 0.03 (4) 0.58 (18) 0.33 (9) 0.34 (41)
18:41229735 6.06 × 10−50.022 A G 0.44 0.33 0.08 LOC100684610 (OR4C5) 0.45 (14) 0.22 (6) 0.13 (15) 0.42 (13) 0.56 (15) 0.49 (58)
18:38350947 8.02 × 10−50.023 A G 0.12 0.51 0.13 -0.06 (2) 0 (0) 0 (0) 0.42 (13) 0.41 (11) 0.13 (16)
18:38456518 8.02 × 10−50.023 A G 0.12 0.51 0.13 CTNND10.06 (2) 0 (0) 0 (0) 0.42 (13) 0.41 (11) 0.13 (16)
18:40281122 9.65 × 10−50.026 A G 0.42 0.31 0.08 -0.42 (13) 0.19 (5) 0.13 (16) 0.42 (13) 0.56 (15) 0.45 (53)
18:40286669 1.22 × 10−40.030 G A 0.43 0.31 0.08 -0.42 (13) 0.19 (5) 0.13 (16) 0.42 (13) 0.56 (15) 0.47 (56)
18:38326192 1.54 × 10−40.030 A G 0.14 0.46 0.12 OR10C10 0.06 (2) 0 (0) 0 (0) 0.42 (13) 0.41 (11) 0.17 (20)
18:41436427 1.63 × 10−40.030 C A 0.50 −0.31 0.08 -0.1 (3) 0.07 (2) 0.35 (42) 0.42 (13) 0.67 (18) 0.45 (53)
18:41854962 1.64 × 10−40.030 C G 0.15 0.42 0.11 -0.1 (3) 0.04 (1) 0.01 (1) 0.32 (10) 0.44 (12) 0.17 (20)
18:37862012 1.69 × 10−40.030 A T 0.18 0.40 0.11 -0.19 (6) 0.04 (1) 0 (0) 0.32 (10) 0.41 (11) 0.24 (28)
18:37867871 1.69 × 10−40.030 A G 0.18 0.40 0.11 OR5B21 0.19 (6) 0.04 (1) 0 (0) 0.32 (10) 0.41 (11) 0.24 (28)
18:40667579 2.48 × 10−40.042 C A 0.47 0.30 0.08 -0.45 (14) 0.22 (6) 0.15 (18) 0.45 (14) 0.56 (15) 0.5 (60)
18:40653765 2.87 × 10−40.046 A C 0.47 0.30 0.08 -0.45 (14) 0.22 (6) 0.15 (18) 0.45 (14) 0.56 (15) 0.53 (63)
18:39177075 3.56 × 10−40.053 A G 0.32 0.32 0.09 -0.23 (7) 0.15 (4) 0.08 (9) 0.55 (17) 0.44 (12) 0.38 (45)
18:40418483 3.64 × 10−40.053 A G 0.47 0.29 0.08 -0.42 (13) 0.22 (6) 0.16 (19) 0.48 (15) 0.52 (14) 0.52 (62)
18:55380537 3.89 × 10−40.054 A T 0.44 −0.30 0.08 TMEM109 0 (0) 0 (0) 0.29 (35) 0.48 (15) 0.59 (16) 0.47 (56)
18:37249960 4.14 × 10−40.055 A G 0.14 0.40 0.11 LOC483451 0.1 (3) 0 (0) 0.01 (1) 0.39 (12) 0.37 (10) 0.16 (19)
18:39727438 5.07 × 10−40.065 G A 0.31 0.29 0.08 -0.29 (9) 0.11 (3) 0.08 (9) 0.45 (14) 0.48 (13) 0.35 (42)
18:38838399 6.64 × 10−40.082 G A 0.48 0.26 0.08 -0.45 (14) 0.33 (9) 0.21 (25) 0.45 (14) 0.37 (10) 0.43 (51)
18:38807806 7.17 × 10−40.085 A G 0.15 0.41 0.12 SSRP1 0.06 (2) 0 (0) 0 (0) 0.45 (14) 0.37 (10) 0.2 (24)
18:40076496 8.15 × 10−40.093 G A 0.21 0.34 0.10 -0.13 (4) 0.04 (1) 0.02 (2) 0.48 (15) 0.41 (11) 0.29 (34)
Table 2. Frequency of homozygosity and heterozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) for significant SNPs (q < 0.1) on chromosome 27. CanFam3.1 (assembly GCF_000002285.3) genes were fetched from NCBI. Non-protein-coding genes are indicated in italics. A1 = minor allele; A2 = major allele; Freq = frequency; b = beta-coefficient; SE = standard error; Lym = lymphoma; Carr = carriers; Cont = controls.
Table 2. Frequency of homozygosity and heterozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) for significant SNPs (q < 0.1) on chromosome 27. CanFam3.1 (assembly GCF_000002285.3) genes were fetched from NCBI. Non-protein-coding genes are indicated in italics. A1 = minor allele; A2 = major allele; Freq = frequency; b = beta-coefficient; SE = standard error; Lym = lymphoma; Carr = carriers; Cont = controls.
Location (Chr:BP) p-Value q-Value A1 A2 A1 Freq b SE Gene Frequency of A1A1 (No. of Dogs) Frequency of A1A2 (No. of Dogs)
Lym. Carr.Cont. Lym. Carr.Cont.
27:8892980 3.55 × 10−50.038 C G 0.22 0.36 0.09 ANO6 0.16 (5) 0.04 (1)0.04 (5) 0.55 (17) 0.37 (10) 0.25 (30)
27:3594560 4.29 × 10−50.038 G A 0.15 0.42 0.10 BIN2 0.13 (4) 0.07 (2)0 (0) 0.45 (14) 0.26 (7) 0.18 (21)
27:5364442 4.35 × 10−50.038 A G 0.18 0.39 0.10 LOC1110927350.16 (5) 0.07 (2)0.01 (1) 0.42 (13) 0.33 (9) 0.22 (26)
27:5112245 5.50 × 10−50.038 A G 0.24 0.34 0.09 SPATS2 0.29 (9) 0.11 (3)0.02 (2) 0.39 (12) 0.52 (14) 0.27 (32)
27:3234647 6.07 × 10−50.038 G A 0.18 0.39 0.10 SCN8A 0.13 (4) 0.07 (2)0.03 (3) 0.48 (15) 0.26 (7) 0.19 (23)
27:8331252 1.13 × 10−40.053 G A 0.12 0.44 0.11 -0.06 (2) 0.04 (1)0 (0) 0.45 (14) 0.26 (7) 0.13 (15)
27:3197949 1.26 × 10−40.053 G A 0.18 0.37 0.10 SCN8A 0.13 (4) 0.07 (2) 0.03 (3) 0.48 (15) 0.26 (7) 0.21 (25)
27:5467028 1.33 × 10−40.053 G A 0.36 0.30 0.08 -0.32 (10) 0.11 (3) 0.09 (11) 0.52 (16) 0.48 (13) 0.41 (49)
27:8646723 1.87 × 10−40.066 G A 0.13 0.41 0.11 -0.06 (2) 0.04 (1) 0 (0) 0.45 (14) 0.26 (7) 0.17 (20)
27:8883501 2.21 × 10−40.070 A G 0.15 0.37 0.10 ANO6 0.13 (4) 0.04 (1) 0.01 (1) 0.42 (13) 0.26 (7) 0.17 (20)
27:8767784 2.67 × 10−40.077 T A 0.14 0.40 0.11 LOC1110928900.06 (2) 0.04 (1) 0 (0) 0.45 (14) 0.26 (7) 0.18 (21)
27:3501246 3.35 × 10−40.082 C A 0.37 −0.30 0.08 -0.03 (1) 0.11 (3) 0.2 (24) 0.29 (9) 0.33 (9) 0.47 (56)
27:3054141 3.62 × 10−40.082 A G 0.23 0.32 0.09 -0.13 (4) 0.07 (2) 0.04 (5) 0.55 (17) 0.48 (13) 0.25 (30)
27:8842830 4.22 × 10−40.082 A C 0.13 0.38 0.11 LOC1110928900.06 (2) 0.04 (1) 0.01 (1) 0.45 (14) 0.26 (7) 0.14 (17)
27:8884575 4.22 × 10−40.082 A G 0.13 0.38 0.11 ANO6 0.06 (2) 0.04 (1) 0.01 (1) 0.45 (14) 0.26 (7) 0.14 (17)
27:1953293 4.40 × 10−40.082 G A 0.23 0.33 0.09 LOC1110927990.13 (4) 0.07 (2) 0.03 (3) 0.52 (16) 0.48 (13) 0.3 (36)
27:3154712 4.58 × 10−40.082 A G 0.34 0.29 0.08 FIGNL2 0.26 (8) 0.15 (4) 0.08 (10) 0.55 (17) 0.41 (11) 0.4 (48)
27:5603116 4.67 × 10−40.082 A G 0.39 0.28 0.08 WNT10B 0.42 (13) 0.26 (7) 0.05 (6) 0.35 (11) 0.48 (13) 0.52 (62)
27:5478927 5.41 × 10−40.083 A G 0.31 0.29 0.08 LMBR1L 0.23 (7) 0.22 (6) 0.03 (4) 0.52 (16) 0.33 (9) 0.43 (51)
27:7188905 5.43 × 10−40.083 A G 0.28 0.30 0.09 -0.23 (7) 0.11 (3) 0.04 (5) 0.48 (15) 0.44 (12) 0.34 (41)
27:3104073 5.47 × 10−40.083 A G 0.23 0.31 0.09 ANKRD33 0.13 (4) 0.07 (2) 0.03 (4) 0.52 (16) 0.48 (13) 0.26 (31)
27:5806033 6.73 × 10−40.089 A G 0.22 0.32 0.10 CCNT1 0.13 (4) 0.22 (6) 0.01 (1) 0.55 (17) 0.33 (9) 0.25 (30)
27:5817551 6.73 × 10−40.089 G A 0.22 0.32 0.10 -0.13 (4) 0.22 (6) 0.01 (1) 0.55 (17) 0.33 (9) 0.25 (30)
27:5512765 6.89 × 10−40.089 A G 0.36 0.28 0.08 RHEBL1 0.29 (9) 0.11 (3) 0.08 (9) 0.52 (16) 0.48 (13) 0.47 (56)
27:6657558 7.32 × 10−40.089 G A 0.23 0.31 0.09 -0.19 (6) 0.07 (2) 0.03 (3) 0.45 (14) 0.37 (10) 0.31 (37)
27:6660738 7.32 × 10−40.089 A G 0.23 0.31 0.09 -0.19 (6) 0.07 (2) 0.03 (3) 0.45 (14) 0.37 (10) 0.31 (37)
27:1488082 7.65 × 10−40.090 G A 0.35 0.27 0.08 LOC4865040.29 (9) 0.11 (3) 0.12 (14) 0.45 (14) 0.44 (12) 0.39 (46)
27:5822515 8.18 × 10−40.093 G A 0.22 0.31 0.09 -0.13 (4) 0.22 (6) 0.01 (1) 0.55 (17) 0.37 (10) 0.25 (30)
27:1719544 9.13 × 10−40.100 A G 0.34 0.27 0.08 TARBP2 0.26 (8) 0.07 (2) 0.17 (20) 0.45 (14) 0.41 (11) 0.31 (37)
Table 3. Frequency of homozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a linear regression of haplotype blocks for chromosome 18. Genes in the region were fetched from NCBI (CanFam3.1, assembly CF_000002285.3). Lym = lymphoma; Carr = carriers; Cont = controls.
Table 3. Frequency of homozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a linear regression of haplotype blocks for chromosome 18. Genes in the region were fetched from NCBI (CanFam3.1, assembly CF_000002285.3). Lym = lymphoma; Carr = carriers; Cont = controls.
No.
of
SNPs
Start SNP End SNP Haplotype p-Value Genes in Region Frequency of Homozygosity
(No. of Dogs)
Lym. Carr. Cont.
12 18:38038090 18:38129762 AACAAAACAACG 7.75 × 10−8LOC106559970, OR08G08,
LOC483477, COR5BC3,
LOC483479, LOC610022, LOC483480
0.06 (2) 0 (0) 0 (0)
2 18:39123584 18:39140112 AG 1.65 × 10−7LOC100688997 0.1 (3) 0.07 (2) 0.02 (2)
2 18:37724157 18:37737740 CC 5.77 × 10−7LOC100686230 0.06 (2) 0 (0) 0 (0)
5 18:38383741 18:38426687 GGGAG 6.09 × 10−7CTNND1 0.06 (2) 0 (0) 0 (0)
3 18:38456518 18:38478390 AAC 6.09 × 10−7CTNND1, BTBD18 0.06 (2) 0 (0) 0 (0)
5 18:38498673 18:38511357 AAAAT 2.31 × 10−6ZDHHC5 0.1 (3) 0 (0) 0 (0)
2 18:55380537 18:55385901 TG 3.36 × 10−6TMEM109 0.52 (16) 0.41 (11) 0.24 (28)
2 18:37862012 18:37867871 AA 6.12 × 10−6OR5B21 0.19 (6) 0.04 (1) 0 (0)
2 18:37862012 18:37867871 TG 6.12 × 10−6OR5B21 0.48 (15) 0.56 (15) 0.76 (91)
2 18:38317284 18:38326192 AA 1.07 × 10−5OR10C10 0.06 (2) 0 (0) 0 (0)
14 18:13130318 18:13325298 AGAAAAAAGAAGAG 1.27 × 10−5COG5 0 (0) 0 (0) 0 (0)
3 18:41713196 18:41740841 GGG 4.05 × 10−5LOC106559997 0.06 (2) 0.07 (2) 0.33 (39)
3 18:38805092 18:38807806 AGA 4.45 × 10−5SSRP1 0.06 (2) 0 (0) 0 (0)
2 18:41422687 18:41436427 AC 6.03 × 10−5-0.1 (3) 0.07 (2) 0.35 (42)
3 18:39168396 18:39195972 AAA 6.86 × 10−5LOC483503, OR04E05 0.1 (3) 0.04 (1) 0.01 (1)
5 18:39846804 18:39860197 AGGAA 8.23 × 10−5LOC483541, LOC100685982 0.16 (5) 0.04 (1) 0.01 (1)
2 18:40663070 18:40667579 GC 8.38 × 10−5-0.45 (14) 0.22 (6) 0.15 (18)
3 18:38719709 18:38732149 GAA 8.47 × 10−5SLC43A3 0.23 (7) 0.22 (6) 0.08 (9)
Table 4. Frequency of homozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a linear regression of haplotype blocks for chromosome 27. Genes in the region were fetched from the NCBI (CanFam3.1, assembly GCF_000002285.3). Non-protein-coding genes are indicated in italics. Lym = lymphoma; Carr = carriers; Cont = controls.
Table 4. Frequency of homozygosity in lymphoma cases (N = 31), carriers (N = 27), and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a linear regression of haplotype blocks for chromosome 27. Genes in the region were fetched from the NCBI (CanFam3.1, assembly GCF_000002285.3). Non-protein-coding genes are indicated in italics. Lym = lymphoma; Carr = carriers; Cont = controls.
No. of SNPs Start SNP End SNP Haplotype p-Value Genes in Region Frequency of Homozygosity
(No. of Dogs)
Lym. Carr. Cont.
9 27:5104085 27:5241391 AAAAGCGAA 9.37 × 10−9SPATS2 0.29 (9) 0.07 (2) 0.02 (2)
6 27:4662318 27:4728424 GAAGGA 1.27 × 10−8ASIC1, RACGAP1, LOC111092823 0.13 (4) 0.07 (2) 0 (0)
5 27:5304179 27:5340118 GAAAC 2.04 × 10−8PRPH, LOC100856405 0.16 (5) 0.07 (2) 0.01 (1)
4 27:5919807 27:5983459 AGAA 8.05 × 10−8cOR8S6P, LOC102156933, LOC100855426, COR8S14, LOC111092874 0.1 (3) 0.07 (2) 0 (0)
4 27:8010819 27:8043205 AGAA 1.11 × 10−7LOC102154451, LOC102155835 0.06 (2) 0.04 (1) 0 (0)
5 27:3590788 27:3615721 AGAGA 1.33 × 10−7BIN2, SMAGP 0.13 (4) 0.07 (2) 0 (0)
5 27:5762992 27:5817551 GAGAG 1.74 × 10−7LOC111092830, CCNT1 0.13 (4) 0.22 (6) 0.01 (1)
9 27:8396219 27:8548130 AGCGCGGGG 2.27 × 10−7SCAF11, ARID2 0.06 (2) 0.04 (1) 0 (0)
2 27:8314156 27:8331252 AG 2.57 × 10−7LOC1110928410.06 (2) 0.04 (1) 0 (0)
13 27:3197949 27:3347721 GTCGAGGAG GGGC 3.97 × 10−7SCN8A 0.13 (4) 0.07 (2) 0 (0)
4 27:5919807 27:5983459 AAAA 2.70 × 10−6cOR8S6P, LOC102156933, LOC100855426, COR8S14, LOC111092874 0.16 (5) 0.11 (3) 0.45 (53)
3 27:5261130 27:5265185 GGA 4.07 × 10−6DNAJC22 0.29 (9) 0.19 (5) 0.08 (9)
4 27:8583844 27:8646723 CAAG 5.07 × 10−6LOC1021553000.06 (2) 0.04 (1) 0 (0)
3 27:8842830 27:8861426 AAA 8.77 × 10−6LOC1110928900.06 (2) 0.04 (1) 0.01 (1)
6 27:8883501 27:8885135 AGAAAA 8.77 × 10−6ANO6 0.06 (2) 0.04 (1) 0.01 (1)
2 27:8767784 27:8775933 TG 1.81 × 10−5LOC1110928900.06 (2) 0.04 (1) 0 (0)
3 27:5281537 27:5282177 CAG 2.24 × 10−5C1QL4 0.23 (7) 0.22 (6) 0.05 (6)
6 27:21881521 27:21942273 AAAGGG 2.33 × 10−5LOC1110928340.03 (1) 0.04 (1) 0.02 (2)
3 27:5861077 27:5870632 GGA 2.99 × 10−5LYZF2 0.35 (11) 0.3 (8) 0.12 (14)
4 27:1460779 27:1471371 AAGG 3.26 × 10−5LOC4865040.32 (10) 0.22 (6) 0.06 (7)
3 27:1250330 27:1251390 AGA 3.96 × 10−5LOC607625 0.1 (3) 0.07 (2) 0.01 (1)
5 27:4735827 27:4744789 AAGCA 3.96 × 10−5AQP5, AQP2 0.35 (11) 0.22 (6)0.09 (11)
3 27:5261130 27:5265185 AGA 4.21 × 10−5DNAJC22 0.03 (1)0 (0)0.13 (15)
3 27:3102259 27:3104073 GAA 4.74 × 10−5ANKRD33 0.13 (4)0.07 (2)0.03 (4)
5 27:11605144 27:11653990 CGAGA 5.17 × 10−5LOC1065579030.1 (3)0.3 (8)0.05 (6)
2 27:2965369 27:2966261 GC 5.84 × 10−5-0.87 (27)0.74 (20)0.53 (63)
5 27:21532742 27:21616626 GCAGC 7.16 × 10−5RASSF8, LOC106557908 0.03 (1)0.04 (1)0.02 (2)
19 27:4373882 27:4565172 AAAGAGGAGGAGAGAAAAG 8.25 × 10−5LARP4, FAM186A, LIMA1 0.13 (4)0.07 (2)0.03 (4)
4 27:6639027 27:6661792 CGAA 9.24 × 10−5PFKM 0.19 (6)0.07 (2)0.03 (3)
Table 5. Frequency of homozygosity and heterozygosity in lymphoma cases (N = 31) and controls (N = 119) for significant SNPs (q < 0.1) in the binary phenotype GWAS. CanFam3.1 (assembly GCF_000002285.3) genes were fetched from NCBI. Non-protein-coding genes are indicated in italics. A1 = minor allele; A2 = major allele; Freq = frequency; b = beta-coefficient; SE = standard error; Lym = lymphoma; Cont = controls.
Table 5. Frequency of homozygosity and heterozygosity in lymphoma cases (N = 31) and controls (N = 119) for significant SNPs (q < 0.1) in the binary phenotype GWAS. CanFam3.1 (assembly GCF_000002285.3) genes were fetched from NCBI. Non-protein-coding genes are indicated in italics. A1 = minor allele; A2 = major allele; Freq = frequency; b = beta-coefficient; SE = standard error; Lym = lymphoma; Cont = controls.
Location (Chr:BP) p-Value q-Value A1 A2 A1
Freq
b SE Gene Frequency of A1A1 (No. of Dogs) Frequency of A1A2 (No. of Dogs)
Lym. Cont. Lym. Cont.
13:17399337 5.78 × 10−50.087 A C 0.02 0.63 0.16 EXT1 0 (0) 0 (0) 0.19 (6) 0.01 (1)
13:18910196 3.42 × 10−50.077 A G 0.02 0.69 0.17 DEPTOR 0 (0) 0 (0) 0.16 (5) 0.01 (1)
13:18913685 3.42 × 10−50.077 A G 0.02 0.69 0.17 DEPTOR 0 (0) 0 (0) 0.16 (5) 0.01 (1)
13:20734477 7.72 × 10−50.087 A G 0.03 0.53 0.13 LOC111098540 0 (0) 0 (0) 0.23 (7) 0.03 (3)
14:3945297 5.97 × 10−50.084 A G 0.07 0.36 0.09 EXOC4 0.1 (3) 0 (0) 0.19 (6) 0.08 (10)
14:4095927 5.97 × 10−50.084 A G 0.07 0.36 0.09 -0.1 (3) 0 (0) 0.19 (6) 0.08 (10)
14:54260890 8.46 × 10−50.084 A G 0.02 0.72 0.18 -0 (0) 0 (0) 0.13 (4) 0.01 (1)
18:37737740 3.07 × 10−40.066 C A 0.12 0.25 0.07 -0.06 (2) 0 (0) 0.45 (14) 0.16 (19)
18:37862012 4.81 × 10−40.074 A T 0.17 0.21 0.06 -0.19 (6) 0 (0) 0.32 (10) 0.24 (28)
18:37867871 4.81 × 10−40.074 A G 0.17 0.21 0.06 OR5B21 0.19 (6) 0 (0) 0.32 (10) 0.24 (28)
18:38233567 1.74 × 10−40.053 A G 0.11 0.29 0.08 -0.06 (2) 0 (0) 0.42 (13) 0.13 (15)
18:38350947 3.76 × 10−40.066 A G 0.11 0.27 0.08 -0.06 (2) 0 (0) 0.42 (13) 0.13 (16)
18:38456518 3.76 × 10−40.066 A G 0.11 0.27 0.08 CTNND1 0.06 (2) 0 (0) 0.42 (13) 0.13 (16)
18:38502268 2.16 × 10−40.053 A G 0.12 0.26 0.07 ZDHHC5 0.1 (3) 0 (0) 0.39 (12) 0.16 (19)
18:38507461 2.16 × 10−40.053 A G 0.12 0.26 0.07 ZDHHC5 0.1 (3) 0 (0) 0.39 (12) 0.16 (19)
18:38510335 2.16 × 10−40.053 A G 0.12 0.26 0.07 ZDHHC5 0.1 (3) 0 (0) 0.39 (12) 0.16 (19)
18:38704682 4.49 × 10−50.053 A G 0.32 0.20 0.05 -0.26 (8) 0.08 (9) 0.55 (17) 0.37 (44)
18:38719709 1.45 × 10−40.053 G A 0.30 0.18 0.05 -0.23 (7) 0.08 (9) 0.55 (17) 0.34 (41)
18:39140112 8.21 × 10−50.053 G A 0.26 0.22 0.06 LOC100688997 0.16 (5) 0.03 (4) 0.58 (18) 0.34 (41)
18:39727438 3.38 × 10−40.066 G A 0.31 0.17 0.05 -0.29 (9) 0.08 (9) 0.45 (14) 0.35 (42)
18:40281122 8.35 × 10−50.053 A G 0.41 0.18 0.05 -0.42 (13) 0.13 (16) 0.42 (13) 0.45 (53)
18:40286669 9.66 × 10−50.053 G A 0.42 0.18 0.05 -0.42 (13) 0.13 (16) 0.42 (13) 0.47 (56)
18:40418483 2.44 × 10−40.056 A G 0.47 0.17 0.05 -0.42 (13) 0.16 (19) 0.48 (15) 0.52 (62)
18:40653765 2.05 × 10−40.053 A C 0.47 0.18 0.05 -0.45 (14) 0.15 (18) 0.45 (14) 0.53 (63)
18:40667579 1.95 × 10−40.053 C A 0.46 0.17 0.05 -0.45 (14) 0.15 (18) 0.45 (14) 0.5 (60)
18:41229735 6.77 × 10−50.053 A G 0.43 0.19 0.05 LOC100684610 0.45 (14) 0.13 (15) 0.42 (13) 0.49 (58)
18:41436427 1.54 × 10−40.053 C A 0.52 −0.17 0.05 -0.1 (3) 0.35 (42) 0.42 (13) 0.45 (53)
18:41713196 4.34 × 10−40.073 A G 0.15 0.24 0.07 -0.06 (2) 0.03 (3) 0.45 (14) 0.18 (21)
18:41726488 1.92 × 10−40.053 A G 0.15 0.26 0.07 -0.06 (2) 0.02 (2) 0.45 (14) 0.18 (22)
18:41854962 1.82 × 10−40.053 C G 0.13 0.24 0.06 -0.1 (3) 0.01 (1) 0.32 (10) 0.17 (20)
18:42270324 3.78 × 10−40.066 A G 0.07 0.33 0.09 -0 (0) 0.01 (1) 0.35 (11) 0.08 (9)
27:3054141 8.03 × 10−40.096 A G 0.22 0.18 0.05 -0.13 (4) 0.04 (5) 0.55 (17) 0.25 (30)
27:3154712 2.20 × 10−40.041 A G 0.34 0.18 0.05 FIGNL2 0.26 (8) 0.08 (10) 0.55 (17) 0.4 (48)
27:3197949 7.78 × 10−50.027 G A 0.18 0.23 0.06 SCN8A 0.13 (4) 0.03 (3) 0.48 (15) 0.21 (25)
27:3234647 4.84 × 10−50.027 G A 0.17 0.24 0.06 SCN8A 0.13 (4) 0.03 (3) 0.48 (15) 0.19 (23)
27:3501246 1.13 × 10−40.027 C A 0.38 −0.18 0.05 -0.03 (1) 0.2 (24) 0.29 (9) 0.47 (56)
27:3594560 2.29 × 10−50.027 G A 0.14 0.27 0.06 BIN2 0.13 (4) 0 (0) 0.45 (14) 0.18 (21)
27:4561667 7.57 × 10−40.095 A C 0.23 0.18 0.05 LIMA1 0.13 (4) 0.05 (6) 0.58 (18) 0.25 (30)
27:5112245 6.08 × 10−50.027 A G 0.22 0.20 0.05 SPATS2 0.29 (9) 0.02 (2) 0.39 (12) 0.27 (32)
27:5364442 4.76 × 10−50.027 A G 0.17 0.24 0.06 LOC111092735 0.16 (5) 0.01 (1) 0.42 (13) 0.22 (26)
27:5467028 1.07 × 10−40.027 G A 0.36 0.18 0.05 -0.32 (10) 0.09 (11) 0.52 (16) 0.41 (49)
27:5478927 2.36 × 10−40.042 A G 0.30 0.19 0.05 LMBR1L 0.23 (7) 0.03 (4) 0.52 (16) 0.43 (51)
27:5512765 5.03 × 10−40.072 A G 0.36 0.17 0.05 RHEBL1 0.29 (9) 0.08 (9) 0.52 (16) 0.47 (56)
27:5603116 4.20 × 10−40.063 A G 0.37 0.17 0.05 WNT10B 0.42 (13) 0.05 (6) 0.35 (11) 0.52 (62)
27:5806033 1.17 × 10−40.027 A G 0.19 0.24 0.06 CCNT1 0.13 (4) 0.01 (1) 0.55 (17) 0.25 (30)
27:5817551 1.17 × 10−40.027 G A 0.19 0.24 0.06 -0.13 (4) 0.01 (1) 0.55 (17) 0.25 (30)
27:5822515 1.17 × 10−40.027 G A 0.19 0.24 0.06 -0.13 (4) 0.01 (1) 0.55 (17) 0.25 (30)
27:7188905 5.73 × 10−40.078 A G 0.27 0.18 0.05 -0.23 (7) 0.04 (5) 0.48 (15) 0.34 (41)
27:8331252 3.49 × 10−50.027 G A 0.11 0.30 0.07 -0.06 (2) 0 (0) 0.45 (14) 0.13 (15)
27:8646723 8.78 × 10−50.027 G A 0.13 0.28 0.07 -0.06 (2) 0 (0) 0.45 (14) 0.17 (20)
27:8767784 1.51 × 10−40.030 T A 0.13 0.26 0.07 LOC111092890 0.06 (2) 0 (0) 0.45 (14) 0.18 (21)
27:8842830 2.80 × 10−40.044 A C 0.12 0.24 0.07 LOC111092890 0.06 (2) 0.01 (1) 0.45 (14) 0.14 (17)
27:8883501 1.39 × 10−40.030 A G 0.14 0.24 0.06 ANO6 0.13 (4) 0.01 (1) 0.42 (13) 0.17 (20)
27:8884575 2.80 × 10−40.044 A G 0.12 0.24 0.07 ANO6 0.06 (2) 0.01 (1) 0.45 (14) 0.14 (17)
27:8892980 6.60 × 10−50.027 C G 0.22 0.21 0.05 ANO6 0.16 (5) 0.04 (5) 0.55 (17) 0.25 (30)
27:11672173 6.00 × 10−40.078 A C 0.16 0.23 0.07 LOC106557903 0.03 (1) 0 (0) 0.55 (17) 0.24 (28)
Table 6. Frequency of homozygosity in lymphoma cases (N = 31) and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a logistic regression of haplotype blocks for chromosome 18. Genes in the region were fetched from NCBI (CanFam3.1, assembly GCF_000002285.3). Lym = lymphoma; Cont = controls.
Table 6. Frequency of homozygosity in lymphoma cases (N = 31) and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a logistic regression of haplotype blocks for chromosome 18. Genes in the region were fetched from NCBI (CanFam3.1, assembly GCF_000002285.3). Lym = lymphoma; Cont = controls.
No. of SNPs Start SNP End SNP Haplotype Odds Ratio p-Value Genes in Region Frequency of Homozygosity (No. of Dogs)
Lym. Cont.
14 18:38233567 18:38383741 AAGAGGGAAAAAGG 5.61 7.66 × 10−5 LOC483485, LOC610080,
LOC106559985, OR10C10, LOC610127, COR5BA2
0.06 (2) 0 (0)
3 18:38502268 18:38510335 AAA 4.91 7.86 × 10−5 ZDHHC5 0.1 (3) 0 (0)
3 18:38502268 18:38510335 GGG 0.204 7.86 × 10−5 ZDHHC5 0.52 (16) 0.84 (100)
2 18:37862012 18:37867871 AA 3.98 9.97 × 10−5 OR5B21 0.19 (6) 0 (0)
2 18:37862012 18:37867871 TG 0.251 9.97 × 10−5 OR5B21 0.48 (15) 0.76 (91)
Table 7. Frequency of homozygosity in lymphoma cases (N = 31) and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a logistic regression of haplotype blocks for chromosome 27. Genes in the region were fetched from the NCBI (CanFam3.1, assembly GCF_000002285.3). Lym = lymphoma; Cont = controls.
Table 7. Frequency of homozygosity in lymphoma cases (N = 31) and controls (N = 119) at highly significant haplotypes (p < 0.0001) from a logistic regression of haplotype blocks for chromosome 27. Genes in the region were fetched from the NCBI (CanFam3.1, assembly GCF_000002285.3). Lym = lymphoma; Cont = controls.
No. of SNPs Start SNP End SNP Haplotype Odds Ratio p-Value Genes in Region Frequency of Homozygosity
(No. of Dogs)
Lym. Cont.
8 27:5112245 27:5241391 AAAGCGAA 4.94 2.82 × 10−6 SPATS2 0.29 (9) 0.02 (2)
2 27:3590788 27:3594560 AG 6.26 5.33 × 10−6 BIN2 0.13 (4) 0 (0)
12 27:3211312 27:3347721 TCGAGGAGGGGC 5.95 7.53 × 10−6 SCN8A 0.13 (4) 0 (0)
2 27:5806033 27:5817551 AG 5.31 1.51 × 10−5 CCNT1 0.13 (4) 0.01 (1)
2 27:5806033 27:5817551 GA 0.188 1.51 × 10−5 CCNT1 0.32 (10) 0.74 (88)
4 27:8752431 27:8792430 GTGG 5.24 6.12 × 10−5 LOC111092890 0.06 (2) 0 (0)
2 27:5592820 27:5603116 CA 3.65 6.83 × 10−5 WNT10B 0.42 (13) 0.05 (6)
Table 8. REML results for the proportion of phenotypic variance explained by all autosomal SNPs, significant chromosomes, and regions within each significant chromosome. V(G)/Vp indicates proportion of phenotypic variance (Vp) over genetic variance (V(G)) (heritability). V(G)/Vp_L is heritability transformed to the underlying liability scale. SE: standard error.
Table 8. REML results for the proportion of phenotypic variance explained by all autosomal SNPs, significant chromosomes, and regions within each significant chromosome. V(G)/Vp indicates proportion of phenotypic variance (Vp) over genetic variance (V(G)) (heritability). V(G)/Vp_L is heritability transformed to the underlying liability scale. SE: standard error.
Chromosome Prevalence V(G)/Vp ± SE (%) V(G)/Vp_L ± SE (%) p-Value
All autosomes 0.025 100 ± 20.52 106.09 ± 21.77 1.43 × 10−6
0.05 129.37 ± 26.55
0.1 160.4 ± 32.92
Chr 18 0.025 31.27 ± 12.77 33.17 ± 13.54 1.21 × 10−3
0.05 40.45 ± 16.52
0.1 50.16 ± 20.48
Chr 18 37 to 56 Mb region 0.025 40.91 ± 12.1 43.4 ± 12.83 7.10 × 10−6
0.05 52.93 ± 15.65
0.1 65.63 ± 19.4
Chr 27 0.025 40.71 ± 11.26 43.19 ± 11.94 1.34 × 10−6
0.05 52.67 ± 14.56
0.1 65.3 ± 18.06
Chr 27 1 to 9 Mb region 0.025 26.12 ± 9.6 27.71 ± 10.18 2.50 × 10−7
0.05 33.79 ± 12.42
0.1 41.9 ± 15.4
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Soh, P.X.Y.; Khatkar, M.S.; Williamson, P. Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis. Vet. Sci. 2023, 10, 581. https://doi.org/10.3390/vetsci10090581

AMA Style

Soh PXY, Khatkar MS, Williamson P. Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis. Veterinary Sciences. 2023; 10(9):581. https://doi.org/10.3390/vetsci10090581

Chicago/Turabian Style

Soh, Pamela Xing Yi, Mehar Singh Khatkar, and Peter Williamson. 2023. "Lymphoma in Border Collies: Genome-Wide Association and Pedigree Analysis" Veterinary Sciences 10, no. 9: 581. https://doi.org/10.3390/vetsci10090581

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop