Neoplastic diseases are a serious problem not only in human medicine, but also in veterinary medicine of companion animals. In dogs especially, the incidence of neoplastic diseases seems to be increasing. It is most likely related to the extension of domestic animal life, more accurate diagnostic tools, and the exposure of both humans and animals to the same carcinogens due to similar living and environmental conditions. The sequencing of the genome of the domestic dog (
Mammary gland neoplasms in dogs are a very interesting and extremely important research model, as they account for nearly 14% of all neoplastic lesions in this species and are second only to skin tumours in terms of incidence (11). In histopathological assessments, approximately 40–50% of mammary gland tumours are malignant neoplasms, most of which are of epithelial origin (29). These tumours are the most common type of neoplasm in bitches, occurring more frequently (the incidence rate is 162–198 cases per 100,000 dogs per year) than in other animal species and three times more often than in women (15, 37). In addition to sex, factors that may be associated with the occurrence of mammary gland cancer include age (37), breed (15), physical condition (22) and hormonal exposure (6).
The development of genetic research over the last few decades has made it possible to discover new genetic markers such as single nucleotide polymorphisms (SNPs), to which are mainly attributed genetic variability between individuals. Single nucleotide polymorphisms may also contribute to changes in gene expression, which is why they are considered potential markers of carcinogenesis, and are therefore a valuable tool in the early diagnosis of various types of cancer (13).
Unfortunately, veterinary oncogenomics is not developing as dynamically as human medical oncogenomics. The first cancer genome-wide association study (GWAS) in dogs was published by Shearin
The first GWAS to identify the genetic basis of canine mammary tumours (CMT), conducted on 332 English springer spaniels, showed eight statistically significant SNPs in two sets of four each, one set on chromosome 11 and the other on chromosome 27 (27). The most significant genome-wide associations were detected for SNP
Only a few studies to date have used the GWAS method to look at the association between specific SNPs and the risk of mammary gland cancers in dogs. Most studies by other authors (6, 7, 32, 35) have focused only on analysing the relationship between specific SNP variants in genes commonly known to be associated with increased susceptibility to breast cancer in humans. Canadas
Despite the research outlined above, the genetic basis of CMT is still poorly understood compared to breast cancer in humans, as evidenced by the small number of SNPs found for individual genes that may be associated with the risk of tumour occurrence (23). Therefore, the aim of the study was to identify SNPs associated with the occurrence of CMT in bitches based on GWAS data.
One-hundred and eighteen unrelated bitches of different breeds (primarily golden retrievers, Labrador retrievers, Yorkshire terriers, German shepherds, French bulldogs, and Maltese) and of mixed breed, aged 5 months to 16.5 years (mean 5.8 years) were included in the analysis. Of these, 36 had mammary gland tumours confirmed by histopathological examination (14) of the material collected by veterinarians following lesion removal.
Four types of material were used for DNA isolation: whole venous blood and tissue obtained during sterilisation (in the case of healthy dogs) and whole venous blood neoplastic tissue obtained from the tumour removal procedures (in the case of bitches with a tumour). All samples were sent to the Polish Federation of Cattle Breeders and Dairy Farmers (Warsaw, Poland), where total DNA was isolated from them with a Sherlock AX kit (A&A Biotechnology, Gdansk, Poland), according to the manufacturer’s protocol (1). After isolation, quantitative evaluation was performed using a NanoDrop2000P spectrophotometer (Thermo Scientific, Waltham, MA, USA), which was the means for both the purity (A260/A280) and the concentration (ng/μL) of the DNA to be determined. Genotyping was performed using the CanineHD BeadChip microarray (175,000 SNPs) (Illumina, San Diego, CA, USA).
Statistical analysis was performed in two steps, namely quality control of genotyping data and GWAS based on statistical models with the single SNP effects. First, SNPs with a large number of missing observations were excluded from the dataset. It was assumed that the lower limit of completeness was 95% and markers for which the number of misses reduced completeness to a lower percentage did not take part in further analysis (17). Next, the individuals with a call rate not exceeding 90% were also excluded (27). Another SNP selection criterion was minor allele frequency (MAF), for which a 5% threshold was used (25). Typing with SNPs with low MAF may lead to incorrect detection of phenotypic associations and may also be more prone to genotyping errors. The last selection criterion was the exclusion of markers deviating from the Hardy–Weinberg equilibrium. Marker SNPs for which the P-value of the test for compliance with the theoretical equilibrium frequencies did not exceed 1−10 in the case group and 1−6 in the control group were removed. The SNPs selected by the above criteria were used for further analyses.
The association analysis was based on logistic regression models with a single SNP marker as the explanatory variable. The analytical model was in the general form of:
This approach makes the use possible of different types of genetic models (codominant, dominant, recessive, overdominant and log-additive) and was implemented using the SNPassoc package (16, 18) in R (33). The statistical significance of an association between the SNP and the analysed phenotype was determined on the basis of P-values for the likelihood ratio test. For each SNP, different genetic models were compared in terms of the Akaike criterion. For each genetic model and SNP for which a significant association was detected, the numbers and percentages of analysed SNP genotypes and odds ratios with 95% confidence intervals were calculated.
Single-nucleotide polymorphisms with missing observations (<geno 95%), low MAF (5%) or deviating from the Hardy–Weinberg equilibrium (HWE) were excluded from the dataset (Fig. 1). This selection criteria reduced the number of SNPs from 173,662 to 140,672 (by 19%). Single-nucleotide polymorphisms within individual chromosomes ranged from 1,938 for chromosome 38 to 9,006 for chromosome 1. Selection using these criteria resulted in association analyses using from 70.5% of SNPs for the X chromosome to 86.8% of SNPs for chromosome 35.
To verify the statistical significance of the relationship between individual SNPs and the appearance of a mammary gland tumour, the dominant, recessive, codominant, overdominant, and log-additive models were used based on logistic regression (Table 1). A total of 40 different SNPs with a statistically significant effect on mammary gland tumour appearance were detected. Twelve SNPs (
Single nucleotide polymorphisms statistically significantly associated with the occurrence of mammary gland tumours
Chromosomes | Codominant | Dominant | Recessive | Overdominant | Log-additive |
---|---|---|---|---|---|
1 |
|
|
|
||
2 | - |
|
- | - | - |
3 | - | - | |||
8 | |||||
10 | - | - | - | - | |
11 | - | - | - | ||
12 | - | - | - | - | |
13 | - | - | - | ||
14 | - | - | - | ||
15 | - | - | - | - | |
16 | - | - | |||
19 |
|
- | - |
|
- |
22 | - | - | - | - | |
23 | - | - | |||
24 | - | - | - | - | |
28 | - | - | - | - | |
30 | - | - | - | - | |
32 | - | - | - | - | |
34 | - | - | - | ||
35 | - | - | - | ||
37 | - | - | |||
X | - | - | - | - |
The effect of single nucleotide polymorphisms marked with * was statistically significant at the level 5−8
Significant SNPs; their location and position in base pairs; minor alleles with their overall, in-control-group and in-case-group frequencies; HWE p-values; and candidate gene or locus, or the closest neighbourhood gene or locus are shown in Table 2. The overall minor allele frequency ranged from 14.83% for
Summary of single nucleotide polymorphisms (SNPs) statistically significantly associated with the occurrence of mammary gland tumours
SNP | CHR | Location (bp) | Minor allele | MAF (%) | MAFcontrol (%) | MAFcase (%) | HWE P-value | Candidate gene or locus/nearest gene or locus |
---|---|---|---|---|---|---|---|---|
1 | 65,212,166 | G | 46.19 | 37.20 | 66.67 | 0.85 | ||
1 | 65,293,673 | A | 42.37 | 32.93 | 63.89 | 1.00 | ||
1 | 65,819,801 | A | 46.61 | 56.71 | 23.61 | 0.46 | ||
1 | 14,217,295 | G | 47.03 | 52.44 | 34.72 | 0.47 | ||
1 | 67,922,585 | A | 27.97 | 32.32 | 18.06 | 0.25 | ||
1 | 41,988,950 | A | 14.83 | 6.71 | 33.33 | 0.00 | ||
1 | 66,185,882 | A | 36.86 | 26.22 | 61.11 | 0.00 | ||
2 | 82,135,437 | G | 39.32 | 32.93 | 54.29 | 0.70 | ||
3 | 27,421,551 | G | 41.53 | 51.22 | 19.44 | 0.34 | ||
3 | 36,857,983 | A | 49.57 | 57.93 | 30.00 | 0.06 | ||
3 | 27,777,144 | A | 49.57 | 59.76 | 26.39 | 0.03 | ||
8 | 12664933 | A | 36.75 | 24.07 | 65.28 | 0.05 | ||
8 | 49,696,532 | A | 19.07 | 26.83 | 1.39 | 0.00 | ||
8 | 34,943,508 | A | 17.37 | 11.59 | 30.56 | 0.34 | ||
8 | 56,964,583 | A | 39.57 | 37.80 | 43.94 | 0.02 | ||
10 | 47,442,988 | A | 36.44 | 33.53 | 43.06 | 0.00 | ||
11 | 61,345,663 | G | 47.88 | 56.71 | 27.78 | 0.01 | ||
11 | 52,671,467 | A | 23.73 | 26.22 | 18.06 | 0.01 | ||
12 | 19,343,255 | A | 26.50 | 19.51 | 42.86 | 1.00 | ||
13 | 39,050,399 | A | 19.49 | 10.98 | 38.89 | 0.38 | ||
14 | 45,742,638 | A | 42.80 | 53.05 | 19.44 | 0.19 | ||
14 | 45,744,668 | G | 42.37 | 52.44 | 19.44 | 0.19 | ||
15 | 42,346,461 | A | 27.12 | 35.37 | 8.33 | 0.00 | ||
15 | 42,358,113 | G | 27.12 | 35.37 | 8.33 | 0.00 | ||
15 | 42,352,911 | A | 27.54 | 35.98 | 8.33 | 0.00 | ||
16 | 35,869,183 | A | 23.28 | 14.63 | 44.12 | 1.00 | ||
19 | 46,961,958 | A | 27.35 | 22.56 | 38.57 | 0.06 | ||
22 | 7,706,835 | A | 49.15 | 56.10 | 32.86 | 0.10 | ||
23 | 51,871,689 | G | 43.53 | 55.63 | 16.67 | 0.00 | ||
24 | 29,281,559 | T | 25.42 | 17.07 | 44.44 | 1.00 | ||
28 | 20,841,387 | A | 29.91 | 21.95 | 48.57 | 1.00 | ||
30 | 35,183,072 | A | 42.37 | 31.71 | 66.67 | 0.00 | ||
32 | 29,141,932 | A | 16.53 | 23.17 | 1.39 | 0.09 | ||
34 | 30,205,708 | G | 49.58 | 39.02 | 73.61 | 0.07 | ||
35 | 20,654,940 | A | 22.03 | 14.63 | 38.89 | 0.01 | ||
35 | 15,603,710 | A | 36.44 | 46.34 | 13.89 | 0.05 | ||
37 | 30,735,796 | A | 38.46 | 34.76 | 47.14 | 0.56 | ||
37 | 24,240,716 | G | 38.98 | 35.98 | 45.83 | 0.44 | ||
37 | 9,335,944 | G | 40.25 | 50.00 | 18.06 | 0.03 | ||
X | 32,408,330 | A | 31.62 | 25.61 | 45.71 | 0.20 |
CHR – chromosome; MAF – minor allele frequency; MAFcontrol – minor allele frequency in the control group; MAFcase – minor allele frequency in the case group; HWE – Hardy–Weinberg equilibrium
Odds ratios were calculated to illustrate the risk of mammary gland tumours associated with particular genotypes within particular SNPs and are presented in Tables 3–7. Analysis based on the dominant model (Table 3) showed that the presence of a minor allele in 7 of the 14 SNPs, either in the form of a homozygote or heterozygote, increased the likelihood of developing the disease. The odds ratio ranged from 6.76 for
The risk of mammary gland tumours associated with particular genotypes within single nucleotide polymorphisms (SNPs) for the dominant model
CHR | SNP | Genotype | Genotype number (percentage) | OR (95% CI) | P-value | |
---|---|---|---|---|---|---|
Control | Case | |||||
1 | A/A | 34 (41.5%) | 1 (2.5%) | 24.79 | 1.57 × 10−6 | |
G/A-G/G | 48 (58.5) | 35 (97.2%) | (3.24; 189.80) | |||
G/G | 37 (45.1%) | 2 (5.6%) | 13.98 | 7.08 × 10−6 | ||
A/G-A/A | 45 (54.9%) | 34 (94.4%) | (3.15; 62.08) | |||
2 | A/A | 41 (50.0%) | 3 (8.6%) | 10.67 | 5.15 × 10−6 | |
G/A-G/G | 41 (50.0%) | 32 (91.4%) | (3.03; 37.61) | |||
3 | A/A | 19 (23.2%) | 24 (66.7%) | 0.15 | 7.02 × 10−6 | |
G/A-G/G | 63 (76.8%) | 12 (33.3%) | (0.06; 0.36) | |||
8 | G/G | 49 (59.8%) | 35 (97.2%) | 0.04 | 2.67 × 10−6 | |
A/G-A/A | 33 (40.2%) | 1 (2.8%) | (0.01; 0.33) | |||
12 | G/G | 55 (67.1%) | 8 (22.9%) | 6.87 | 7.92 × 10−6 | |
A/G-A/A | 27 (32.9%) | 27 (77.1%) | (2.76; 17.14) | |||
13 | G/G | 65 (79.3%) | 13 (36.1%) | 6.76 | 6.52 × 10−6 | |
A/G-A/A | 17 (20.7%) | 23 (63.9%) | (2.85; 16.06) | |||
G/G | 38 (46.3%) | 32 (88.9%) | 0.11 | 4.35 × 10−6 | ||
A/G-A/A | 44 (53.7%) | 4 (11.1%) | (0.03; 0.33) | |||
15 | G/G | 37 (45.1%) | 32 (88.9%) | 0.10 | 2.50 × 10−6 | |
A/G-A/A | 45 (54.9%) | 4 (11.1%) | (0.03; 0.32) | |||
A/A | 38 (46.3%) | 32 (88.9%) | 0.11 | 4.35 × 10−6 | ||
G/A-G/G | 44 (53.7%) | 4 (11.1%) | (0.03; 0.33) | |||
16 | G/G | 60 (73.2%) | 8 (23.5%) | 8.86 | 6.14 × 10−7 | |
A/G-A/A | 22 (26.8%) | 26 (76.5%) | (3.49; 22.48) | |||
23 | A/A | 21 (26.2%) | 26 (72.2%) | 0.14 | 2.77 × 10−6 | |
G/A-G/G | 59 (73.8%) | 10 (27.8%) | (0.06; 0.33) | |||
28 | C/C | 51 (62.2%) | 6 (17.1%) | 7.95 | 3.93 × 10−6 | |
A/C-A/A | 31 (37.8%) | 29 (82.9%) | (2.97; 21.31) | |||
32 | G/G | 50 (61.0%) | 35 (97.2%) | 0.04 | 4.52 × 10−6 | |
A/G-A/A | 32 (39.0%) | 1 (2.8%) | (0.01; 0.34) |
CHR – chromosome; OR – odds ratio; CI – confidence interval
The risk of mammary gland tumours associated with particular genotypes within single nucleotide polymorphisms (SNPs) for the recessive model
CHR | SNP | Genotype | Genotype number (percentage) | OR (95% CI) | P-value | |
---|---|---|---|---|---|---|
Control | Case | |||||
1 | A/A-G/A | 54 (65.9%) | 36 (100%) | 0 | 9.24 × 10−6 | |
G/G | 28 (34.1%) | 0 (0%) | 0 | |||
3 | C/C-A/C | 49 (59.8%) | 34 (97.1%) | 0.04 | 3.70 × 10−6 | |
A/A | 33 (40.2%) | 1 (2.9%) | (0.01; 0.33) | |||
8 | G/G-A/G | 77 (95.1%) | 19 (52.8%) | 17.22 | 9.55 × 10−8 | |
A/A | 4 (4.9%) | 17 (47.2%) | (5.19; 57.15) | |||
11 | A/A-G/A | 49 (59.8%) | 35 (97.2%) | 0.04 | 2.67 × 10−6 | |
G/G | 33 (40.2%) | 1 (2.8%) | (0.01; 0.33) | |||
22 | G/G-A/G | 50 (61.0%) | 34 (97.1%) | 0.05 | 6.19 × 10−6 | |
A/A | 32 (39.0%) | 1 (2.9%) | (0.01; 0.35) | |||
34 | A/A-G/A | 69 (84.1%) | 15 (41.7%) | 7.43 | 4.35 × 10−6 | |
G/G | 13 (15.9%) | 21 (58.3%) | (3.05; 18.08) | |||
35 | G/G-A/G | 81 (98.8%) | 26 (72.2%) | 31.15 | 8.60 ×10−6 | |
A/A | 1 (1.2%) | (27.8%) | (3.81; 255.00) |
CHR – chromosome; OR – odds ratio; CI – confidence interval
The risk of mammary gland tumours associated with particular genotypes within single nucleotide polymorphisms (SNPs) for the codominant model
CHR | SNP | Genotype | Genotype number (percentage) | OR | 95% CI | P-value | |
---|---|---|---|---|---|---|---|
Control | Case | ||||||
1 | A/A | 34 (41.5%) | 1 (2.8%) | 1.00 | |||
G/A | 35 (42.7%) | 22 (61.1%) | 2.73 | (2.73; 167.5) | 6.1 × 10−6 | ||
G/G | 13 (15.9%) | 13 (36.1%) | 4.03 | (4.03; 286.7) | |||
G/G | 37 (45.1%) | 2 (5.6%) | 1.00 | ||||
A/G | 36 (43.9%) | 22 (61.1%) | 11.31 | (2.48; 51.61) | 7.1 × 10−6 | ||
A/A | 9 (11.0%) | 12 (33.3%) | 24.67 | (4.67; 130.35) | |||
G/G | 16 (19.5%) | 20 (55.6%) | 1.00 | ||||
A/G | 39 (47.6%) | 15 (41.7%) | 0.13 | (0.13; 0.75) | 8.9 × 10−6 | ||
A/A | 27 (32.9%) | 1 (2.8%) | 0.00 | (0.00; 0.24) | |||
8 | G/G | 46 (56.8%) | 6 (16.7%) | 1.00 | |||
A/G | 31 (38.3%) | 13 (36.1%) | 3.22 | (1.10; 9.37) | 5.7 × 10−8 | ||
A/A | 4 (4.9%) | 17 (47.2%) | 32.58 | (8.18; 129.78) | |||
14 | G/G | 19 (23.2%) | 23 (63.9%) | 1.00 | |||
A/G | 39 (47.6%) | 12 (33.3%) | 0.25 | (0.10; 0.62) | 8.8 × 10−6 | ||
A/A | 24 (29.3%) | 1 (2.8%) | 0.03 | (0.00; 0.28) | |||
16 | G/G | 60 (73.2%) | 8 (23.5%) | 1.00 | |||
A/G | 20 (24.4%) | 22 (64.7%) | 8.25 | (3.18; 21.43) | 3.2 × 10−6 | ||
A/A | 2 (2.4%) | 4 (11.8%) | 15.00 | (2.36; 95.47) | |||
19 | G/G | 56 (68.3%) | 10 (28.6%) | 1.00 | |||
A/G | 15 (18.3%) | 23 (65.7%) | 8.59 | (3.37; 21.89) | 4.8 × 10−6 | ||
A/A | 11 (13.4%) | 2 (5.7%) | 1.02 | (0.20; 5.30) | |||
23 | A/A | 21 (26.2%) | 26 (72.2%) | 1.00 | |||
G/A | 29 (36.2%) | 8 (22.2%) | 0.22 | (0.08; 0.59) | 2.9 × 10−6 | ||
G/G | 30 (37.5%) | 2 (5.6%) | 0.05 | (0.01; 0.25) | |||
37 | G/G | 42 (51.2%) | 4 (11.4%) | 1.00 | |||
A/G | 23 (28.0%) | 29 (82.9%) | 13.24 | (4.14; 42.34) | 1.5 × 10−7 | ||
A/A | 17 (20.7%) | 2 (5.7%) | 1.24 | (0.21; 7.39) |
CHR – chromosome; OR – odds ratio; CI – confidence interval
The risk of mammary gland tumours associated with particular genotypes within single nucleotide polymorphisms (SNPs) for the overdominant model
CHR | SNP | Genotype | Genotype number (percentage) | OR (95% CI) | P-value | |
---|---|---|---|---|---|---|
Control | Case | |||||
1 | G/G-A/A | 43 (52.4%) | 33 (91.7%) | 0.10 | 9.99 × 10−6 | |
A/G | 39 (47.6%) | 3 (8.3%) | (0.03; 0.35) | |||
8 | G/G-A/A | 71 (86.6%) | 16 (44.4%) | 8.07 | 3.03 × 10−6 | |
A/G | 11 (13.4%) | 20 (55.6%) | (3.23; 20.13) | |||
G/G-A/A | 62 (75.6%) | 10 (30.3%) | 7.13 | 6.16 × 10−6 | ||
A/G | 20 (24.4%) | 23 (69.7%) | (2.91; 17.49) | |||
10 | G/G-A/A | 65 (79.3%) | 13 (36.1%) | 6.76 | 6.52 × 10−6 | |
A/G | 17 (20.7%) | 23 (63.9%) | (2.85; 16.06) | |||
11 | G/G-A/A | 51 (62.2%) | 35 (97.2%) | 0.05 | 7.59 × 10−6 | |
A/G | 31 (37.8%) | 1 (2.8%) | (0.01; 0.36) | |||
19 | G/G-A/A | 67 (81.7%) | 12 (34.3%) | 8.56 | 7.49 × 10−7 | |
A/G | 15 (18.3%) | 23 (65.7%) | (3.50; 20.95) | |||
37 | A/A-G/G | 57 (69.5%) | 9 (25.0%) | 6.84 | 5.72 × 10−6 | |
G/A | 25 (30.5%) | 27 (75.0%) | (2.81; 16.64) | |||
G/G-A/A | 59 (72.0%) | 6 (17.1%) | 12.4 | 2.14 × 10−8 | ||
A/G | 23 (28.0%) | 29 (82.9%) | (4.55; 33.78) | |||
X | G/G-A/A | 62 (75.6%) | 11 (31.4%) | 6.76 | 6.79 × 10−6 | |
A/G | 20 (24.4%) | 24 (68.6%) | (2.82; 16.20) |
CHR – chromosome; OR – odds ratio; CI – confidence interval
The risk of mammary gland tumours associated with particular genotypes within single nucleotide polymorphisms (SNPs) for the log-additive model
CHR | SNP | Genotype | OR | 95% CI | P-value |
---|---|---|---|---|---|
1 | 0, 1, 2 | 4.66 | (2.17; 9.99) | 9.95 × 10−6 | |
0, 1, 2 | 4.21 | (2.11; 8.37) | 5.17 × 10−6 | ||
0, 1, 2 | 0.23 | (0.12; 0.46) | 2.40 × 10−6 | ||
0, 1, 2 | 3.25 | (1.87; 5.67) | 8.34 × 10−6 | ||
3 | 0, 1, 2 | 0.23 | (0.11; 0.47) | 4.23 × 10−6 | |
0, 1, 2 | 0.28 | (0.15; 0.52) | 8.86 × 10−6 | ||
8 | 0, 1, 2 | 5.46 | (2.76; 10.81) | 1.60 × 10−8 | |
13 | 0, 1, 2 | 5.39 | (2.50; 11.61) | 2.19 × 10−6 | |
14 | 0, 1, 2 | 0.22 | (0.11; 0.45) | 1.62 × 10−6 | |
0, 1, 2 | 0.23 | (0.12; 0.46) | 2.89 × 10−6 | ||
16 | 0, 1, 2 | 5.87 | (2.65; 13.03) | 1.16 × 10−6 | |
23 | 0, 1, 2 | 0.23 | (0.12; 0.45) | 4.49 × 10−7 | |
24 | 0, 1, 2 | 4.78 | (2.27; 10.08) | 6.34 × 10−6 | |
30 | 0, 1, 2 | 3.39 | (1.91; 6.03) | 6.58 × 10−6 | |
34 | 0, 1, 2 | 3.92 | (2.07; 7.41) | 2.84 × 10−6 | |
35 | 0, 1, 2 | 0.22 | (0.10; 0.46) | 2.81 × 10−6 | |
37 | 0, 1, 2 | 0.26 | (0.13; 0.51) | 9.59 × 10−6 |
CHR – chromosome; OR – odds ratio; CI – confidence interval
The recessive model (Table 4) showed an increase in the probability of developing a mammary gland tumour in the case of a minor allele homozygous for three markers, namely
For the codominant model analysis, the reference genotype is a major allele homozygote. The appearance of each subsequent minor allele in the genotype increased the probability of developing a mammary gland tumour in the case of the
The analysis based on the overdominant model made the identification possible of markers for which the heterozygous genotype was the risk factor of tumour incidence (Table 6). These included
Finally, the markings 0, 1, and 2 were observed to correspond to the number of minor alleles in each genotype in the log-additive model. In 9 of 17 SNPs (
In our study, we identified 40 SNPs located on 22 chromosomes that showed a statistically significant relationship with the possibility of CMT. These results correspond with those obtained by Melin
In the group of SNPs that were indicated as significant in our GWAS analysis, several transpired to be located within genes that may play an important role in mammary gland cancer. One of the important polymorphic sites found in
In addition, we found one SNP on chromosome 1 (
On chromosome 30, where the
Many of the SNPs that have been associated with canine mammary cancer are located in regions of the chromosomes where genes associated with carcinogenesis are also found. On chromosome 1, five out of seven SNPs are in the area between base pair 65,212,166 and base pair 67,922,585, where the
Although CMTs are among the most common tumours affecting bitches and seem to be quite extensively studied, the innate carcinogenic process is still under-researched, especially with regard to its genetic background. For this reason, any research aimed at identifying the genetic profile, especially regarding the impact of SNPs on the risk of mammary gland cancer in dogs, is extremely important. The results obtained in our GWAS analysis examining the occurrence of mammary gland cancer in dogs showed that the basis for the development of this tumour is highly complex. The SNPs indicated in our study are located in areas of genes related to the processes of carcinogenesis, tumour development, and metastasis, as well as determining the susceptibility to a particular treatment method. As our results are promising, it seems necessary to screen a larger number of individuals for the selected SNPs, as well as to examine the linkage disequilibrium within the selected regions of the genome.