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. 2020 Nov 9;10(11):2071. doi: 10.3390/ani10112071

Identification of Candidate Genes and Pathways Associated with Obesity-Related Traits in Canines via Gene-Set Enrichment and Pathway-Based GWAS Analysis

Sunirmal Sheet 1,†, Srikanth Krishnamoorthy 1,†, Jihye Cha 1, Soyoung Choi 1, Bong-Hwan Choi 1,*
PMCID: PMC7695335  PMID: 33182249

Abstract

Simple Summary

Obesity is a serious health issue and is increasing at an alarming rate in several dog breeds, but there is limited information on the genetic mechanism underlying it. Moreover, there have been very few reports on genetic markers associated with canine obesity. These studies were limited to the use of a single breed in the association study. In this study, we have performed a GWAS and supplemented it with gene-set enrichment and pathway-based analyses to identify causative loci and genes associated with canine obesity in 18 different dog breeds. From the GWAS, the significant markers associated with obesity-related traits including body weight (CACNA1B, C22orf39, U6, MYH14, PTPN2, SEH1L) and blood sugar (PRSS55, GRIK2), were identified. Furthermore, the gene-set enrichment and pathway-based analysis (GESA) highlighted five enriched pathways (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms (fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface) which were found to be shared among all the traits.

Abstract

The present study aimed to identify causative loci and genes enriched in pathways associated with canine obesity using a genome-wide association study (GWAS). The GWAS was first performed to identify candidate single-nucleotide polymorphisms (SNPs) associated with obesity and obesity-related traits including body weight and blood sugar in 18 different breeds of 153 dogs. A total of 10 and 2 SNPs were found to be significantly (p < 3.74 × 10−7) associated with body weight and blood sugar, respectively. None of the SNPs were identified to be significantly associated with obesity trait. We subsequently followed up the GWAS analysis with gene-set enrichment and pathway analyses. A gene-set with 1057, 1409, and 1243 SNPs annotated to 449, 933 and 820 genes for obesity, body weight, and blood sugar, respectively was created by sub-setting the GWAS result at a threshold of p < 0.01 for the gene-set enrichment analysis. In total, 84 GO and 21 KEGG pathways for obesity, 114 GO and 44 KEGG pathways for blood sugar, 120 GO and 24 KEGG pathways for body weight were found to be enriched. Among the pathways and GO terms, we highlighted five enriched pathways (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms (fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface) that were found to be shared among all the traits. Our data provide insights into the genes and pathways associated with obesity and obesity-related traits.

Keywords: obesity, post-GWAS, gene-set enrichment, pathway analysis, functional annotation, genetic variants, blood sugar, body weight, canine

1. Introduction

Canine obesity is a global epidemic rising all over the world. Among dogs visiting veterinary practices, roughly 34–59% of dogs are reported to be overweight, with 5–20% obese [1,2,3,4]. The contemporary rise in obesity is triggered by lifestyle; however, the extensive inter-individual deviation in body mass index (BMI) witnessed even under shared environmental conditions can only be attributed to a genetic predisposition to the condition [5]. Genetic mutations within a single gene have been reported to have a large effect on obesity [6]. Inbreeding leading to reduced genetic diversity is also considered as a crucial factor associated with obesity. Inbreeding increases the prevalence of genetic defects because of the homozygosity of a large number of deleterious recessive alleles [7].

In addition, as in human beings, obesity causes several adverse consequences in dogs, such as cardiopulmonary disease, insulin resistance, diabetes, osteoarthritis, cancer, and other endocrine disorders [4,8,9]. Canines are considered to be obese when their body weight exceeds 30% of their optimal body weight [4]. Gaining extra body weight always precedes the onset of obesity [4]. In obese individual, adipocytes release elevated amounts of free fatty acids, cytokines (TNF-α), leptin, glycerol, adiponectin, and several other factors that are involved in regulating insulin secretion, blood sugar level and body weight, which ultimately contributes to the development of insulin resistance [10,11,12]. Previously, many studies have reported that body weight, obesity, and blood sugar have a clear positive association [13,14,15]. The dog is also considered an important model for obesity research because of its clinical and molecular similarity with humans, but the knowledge about the genetic contribution to dog obesity remains insufficient.

Post-genome-wide association study (GWAS) analyses have been performed in several studies to discover obesity-related markers [16,17,18]. Previous association studies have revealed that polymorphism of POMC and TNF gene can affect the body weight and appetite in obesity [16,19]. A few other genes such as FTO, PPARG, BDNF, MC4R, and MC3R have been reported to be associated with obesity in canines [16,17,18]. Till now, only a few studies on the effect of polymorphism of canine genes on obesity have been published. On the other hand, polymorphism of more than 50 genes was reported to be associated with human obesity [4,20]. Therefore, the continuous search for important candidate canine genes associated with obesity is required. It will help to understand the etiology of obesity in canine. GWAS is a widely used method for studying the genetics of complex diseases [21]. GWAS have so far yielded a huge number of associations between single-nucleotide polymorphisms (SNPs) and complex diseases [21,22,23]. However, performing GWAS powerful enough is relatively difficult because of a few factors like small effect sizes of variants in complex disease, and required stringent statistics because of multiple testing problems. As a result, GWAS identifies only large effect sizes of variants [24]. In addition, GWAS does not capture multi-allelic QTL because of the bi-allelic nature of single nucleotide variants (SNVs) and does not account the fact that disease-associated genes or their products work together in a network [22,23,24]. Therefore, a robust GWAS analysis with the aim of detailed biological understanding is challenging. The solutions proposed to overcome these limitations are to focus on post-GWAS analyses such as functional gene and pathway enrichment analysis, protein–protein interaction network analysis, and translational medicine [22,24,25,26].

In the present study, we performed a case-control-based GWAS analysis using SNP genotyping data from Illumina CanineHD BeadChip array and supplemented it with gene-set and pathway-based functional analysis to detect significantly associated candidate genes and pathways with obesity, and related traits (blood sugar and body weight) in dogs.

2. Materials and Methods

2.1. Animals and Phenotype Assignment

A total of 153 dogs from across South Korea were used in the present study and blood samples were collected from them by following relevant guidelines formulated by the Institutional Animal Care and Use Committee (IACUC) of the National Institute of Animal Science (NIAS, RDA, Wanju-gun, South Korea), and protocol consent was obtained for ‘Development of early diagnosis technology for degenerative muscular skeleton system in special-purpose dog’ project. All dogs were diagnosed individually by a veterinarian. Obesity traits were classified into a 5-point scoring system (1–5) using the body condition score (BCS) [27] as given in Table 1. BCS scores of 1 to 4 were used as controls and a score of 5 as a case for the case-control analysis of obesity. For case-control analysis of body weight trait, the dog of a particular breed having higher body weight, higher than the threshold suggested in America Kennel club standards, was selected as case and dogs falling within the suggested standards [28,29] were considered as control. The case–control analysis for blood sugar was distinguished based on the blood glucose concentration. Individuals with fasting blood glucose level over 120 mg/dL were considered as case. Dogs whose fasting blood glucose level is in the normal range (80−120 mg/dL) were considered as control. All dogs were kept under fasting for 12 h from their last meal before the blood sampling in the morning.

Table 1.

Total dogs categorized based on body condition score for obesity case–control analysis.

Body Condition Score 1 2 3 4 5 (Case)
Very thin Underweight Ideal body weight Overweight Obese
Number of animal 7 11 98 11 29
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2.2. Genomic DNA Extraction, SNP Genotyping and Quality Control

Genomic DNA was extracted from blood samples of 153 dogs of 18 different breeds, using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA, USA). The samples were genotyped on an Illumina CanineHD BeadChip (Illumina, San Diego, CA, USA) array, which contains 173,662 SNPs. Quality control (QC) was carried out with PLINK v.1.9 software [24] under the following criteria: minor allele frequency <5%, low genotyping call rate <90%, missing genotype calls >10%, Hardy–Weinberg equilibrium at p < 0.000001. The final genotyping call rate was 98.5%. Following QC filtering, 135,553 SNPs and 152 animals remained for further association analysis.

2.3. Genome-Wide Association Analysis

A genome-wide association study (GWAS) was performed using a logistic regression model implemented in PLINK v.1.9, to test the association between disease trait and allele in each SNP. Significant factors such as age (1–17), sex (85 females 68 males), and breed (18) were fitted in the GWAS statistical model for all the traits. We generated a total of 20 principal components (PCs); the eigenvalues of all the PCs were fit as co-variance to account for population stratification. The GWAS statistical model used was as follows:

y = xβ + zu + e (1)

where y is a phenotype, x and z are design matrices, β is a vector of fixed effects, u is an additive genetic effect for each marker, and e is a vector of residual [30]. A Bonferroni-corrected threshold was applied to correct for multiple testing, and the genome-wide significance threshold was p < 3.74 × 10−7 (~0.05/135,553). Manhattan and quantile-quantile (Q-Q) plots were generated using CMplot package in R [24].

2.4. Gene-Set Enrichment and Pathway Analysis

We conducted gene-set enrichment and pathway analysis for each trait following the methods described by Dadousis et al. [24,25]. The markers were assigned to harboring genes or to genes within a flanking region of 5 kb up- and downstream of the SNP using SnpEff version 4.3 software [24]. A nominal p < 0.01 was used to filter for SNPs from the GWAS analysis for gene-set enrichment and pathway analysis. The reason behind adopting a less stringent threshold for GWAS was to identify SNPs which do not reach the stringent significant thresholds set in GWAS analysis but still contributes to phenotypic variability. Furthermore, gene-set and pathway analyses obtained from GWAS provided additional knowledge about the complex relationships among genes and interrelated pathways which are likely to play role in obesity [31]. Merging less significant but connected SNPs we can understand well how these variants might be collectively related to our phenotypes of interest [31]. The SNP IDs, genes name assigned to SNPs was filtered from a variant call format (VCF) file that was previously mapped using SnpEff version 4.3 software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database and Gene Ontology (GO) database under biological process and molecular function categories were used for functional annotation and enrichment analyses. Over-representation of genes in every GO terms and pathways were tested using a Fisher’s exact test, which was conducted using the web-based gene-enrichment analysis tool, the Database for Annotation, Visualization, and integrated discovery (DAVID, http://david.abcc.ncifcrf.gov/) [32]. False discovery rate (FDR) correction (<0.05) was applied to account for multiple testing [32]. The GO terms/KEGG category pathways with more than 10 and less than 1000 genes were analyzed for narrowing down the functional categories.

3. Result and Discussion

A polygenic complex disease such as obesity is generally controlled by additive genetic effects of a large number of genes which harbors several SNPs with small-effect size [24]. Some of these SNPs do not reach the stringent statistic thresholds, which are adapted to control for multiple testing problems in GWAS. Therefore, in this study, we included GO and pathways enrichment analyses, which were performed using the post-GWAS assigned genes (harboring essential SNPs) to understand the genetic contribution and biological significance of the functional SNPs on three traits (obesity, blood sugar, and body weight).

3.1. Phenotypes

In the current study, 85 females and 68 males between 1 and 17 years of age were used for analysis. As shown in Table 1, a total of 153 dogs are classified into 5 different BCS categories for case/control analysis of obesity trait. The dogs with a BCS score of 5 were designated as obese [27]. Therefore, we assigned this dog group as a case. In total, there were 29 cases and 124 controls for obesity trait. The total number of cases and controls for body weight was 28 and 125. The overlap between the two categories was 28 individuals, which were both obese and cases for body weight. For blood sugar, it was 34 cases and 119 controls (Table 2). Earlier studies have reported that the medium-sized dogs are more likely to be obese than small-sized dogs or toy breed dogs [27,33]. In this study, most of the obese animals were also from the small category dogs (Pomeranian, Yorkshire Terrier, and Miniature Pinscher) or toy-sized (Maltese and Shihtzu) (Table 3). On the other hand, only a few cases were medium-sized dogs such as Beagles and Cocker Spaniel. Possibly, the small number of medium and large-sized dogs in our study is the reason for inconsistent result with the previously reported study [27,33].

Table 2.

Number of cases and controls used in this study for each trait.

Group Number of Animals Number of Control Number of Case
Obesity 153 124 (BCS 1–4) 29 (BCS 5)
Body weight 153 125 28 *
Blood sugar 153 119 (≤120 mg/dL) 34 (≥120 mg/dL)
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* Dogs weighing over the America Kennel club standards were selected as cases; BCS—body condition score.

Table 3.

Summary of phenotypes (including breed, number of male and female dogs investigated, and number of cases) used to perform GWAS in our analysis.

No. Breed Name Number of Dogs Investigated Obesity Body Weight Blood Sugar
85 Females 68 Males Case Case Case
1 Beagle 3 1 1 1
2 Bichon fris 1 0 0 0
3 Chihuahua 6 0 0 2
4 Cocker Spaniel 8 1 1 1
5 Dachshund 3 0 0 0
6 Doberman 3 0 0 1
7 German Shepherd 1 0 0 1
8 Golden Retriever 1 0 0 0
9 Maltese 40 8 7 2
10 Miniature Pinscher 3 5 5 7
11 Mixed 19 1 1 1
12 Parson Russell Terrier 5 5 5 3
13 Pomeranian 7 2 2 6
14 Poodle 20 1 1 2
15 Schnauzer 6 1 1 1
16 Shih tzu 10 2 2 2
17 Spitz 5 0 0 0
18 Yorkshire Terrier 12 2 2 4
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3.2. Genome-Wide Association Study

To find significant loci associated with the three traits (obesity, body weight, and blood sugar), we performed GWAS with 133,553 SNPs which passed our filtering criteria. The Q-Q plot of the association study indicated a significant clear deviation for blood sugar (λ value = 0.961) and body weight (λ value = 0.963), but, for obesity, there was no obvious deviation (λ value = 1.091) (Figure 1, Figure 2 and Figure 3). The p-value obtained from the GWAS was plotted as Manhattan plots (Figure 1, Figure 2 and Figure 3). Our result showed that 2 and 7 SNPs were significantly associated to blood sugar and body weight, respectively, at the genome-wide significance level of p < 3.74 × 10−7 (i.e., Bonferroni corrected threshold (0.05/133,553). The candidate SNPs associated with blood sugar were on chromosomes 25 and 12; whereas for body weight they were on chromosome 9, 26, 37, 1, 39, and 7 (Table 4). Moreover, none of the SNPs were found to be significant (p < 3.74 × 10−7) for obesity.

Figure 1.

Figure 1

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Manhattan plots showing the distribution of p-values of single nucleotide polymorphism (SNP) markers associating with obesity. Greenline designates the genome-wide significant threshold level of p < 3.74 × 10−7, blue line designates suggestive threshold level of p < 7.48 × 10−7, and red line designates suggestive threshold level of p < 5.82 × 10−6. The quantile-quantile (Q-Q) plot of the GWAS is shown on the right side. GWAS—genome-wide association study.

Figure 2.

Figure 2

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Manhattan plot showing the distribution of p-values of single nucleotide polymorphism (SNP) markers associated with body weight. Greenline designates the genome-wide significant threshold level of p < 3.74 × 10−7, blue line designates suggestive threshold level of p < 7.48 × 10−7, and red line designates suggestive threshold level of p < 5.82 × 10−6. The quantile-quantile (Q-Q) plot of the GWAS is shown on the right side. GWAS - genome-wide association study.

Figure 3.

Figure 3

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Manhattan plot showing the distribution of p-values of single nucleotide polymorphism (SNP) markers associating with blood sugar. Greenline designates the genome-wide significant threshold level of p < 3.74 × 10−7, blue line designates suggestive threshold level of p < 7.48 × 10−7, and red line designates suggestive threshold level of p < 5.82 × 10−6. The quantile-quantile (Q-Q) plot of the GWAS is shown on the right side. GWAS - genome-wide association study.

Table 4.

Top SNPs associated with obesity, body weight, and blood sugar in dogs.

Trait SNP ID Chr Position Freq Gene Type
Body weight BICF2P1168261 9 47831552 6.38 × 10−9 CACNA1B Intron variant
G1314f25S201 26 29766195 4.09 × 10−8 C22orf39 Synonymous variant
BICF2P940718 37 5443556 4.50 × 10−8 U6 Intergenic region
BICF2P407675 1 1.06 × 108 1.77 × 10−7 MYH14 Intron variant
BICF2P247463 39 39876923 2.86 × 10−7 - -
BICF2P1124008 7 78278707 3.20 × 10−7 PTPN2 Intergenic region
BICF2S23242598 7 78365053 3.20 × 10−7 SEH1L Intron variant
Blood sugar BICF2P1418953 25 27416887 2.78 × 10−7 PRSS55 Downstream_gene_variant
BICF2G630121162 12 60529545 4.64 × 10−7 GRIK2 Intergenic_region
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The two SNPs significantly associated with blood sugar, BICF2P1418953 (downstream gene variant) and BICF2G630121162 (intragenic region) were annotated to PRSS55 and GRIK2 genes, respectively (Table 4). The serine protease 55 (PRSS55) belongs to the member of a group of membrane-anchored chymotrypsin (S1)-like serine protease that has been proven to play a key role in maintaining homeostasis [34,35,36]. Obesity is directly attributable to homeostasis imbalance [37]. Furthermore, glutamate receptor ionotropic receptor kainite type subunit 2 (GRIK2) is involved in the majority of glutametergic neurotransmission, which has been suggested to take part in the onset of obesity by regulating appetite [38,39].

In addition, BICF2P1168261 (intron variant), G1314f25S201 (synonymous variant), BICF2P940718 (intergenic region), BICF2P407675 (intron variant), BICF2P1124008 (intergenic region), and BICF2S23242598 (intron variant), which were significantly associated with body weight, were located in protein-coding genes Calcium Voltage-Gated Channel Subunit Alpha1 B (CACNA1B), U6 (U6 non-coding small nuclear RNA), Myosin Heavy Chain 14 (MYH14), Protein Tyrosine Phosphatase Non-receptor Type 2 (PTPN2), and SEH1 like Nucleoporin (SEH1L), respectively. The most significantly associated amongst these was BICF2P116826, harbored by calcium voltage-gated channel subunit alpha1 B (CACNA1B), a member of the voltage-gated calcium channels [40]. The voltage-gated calcium channels are the main regulators of calcium signaling in adipogenesis, which eventually contribute to obesity [40,41,42]. In addition, CACNA1B gene has been previously reported to be a therapeutic target for diseases such as lung cancer, prostate cancer and breast cancer [40]. Moreover, the gene, CACNA1B was noticed to be significantly enriched in several other pathways associated with the traits evaluated in this study, suggesting a significant connection with obesity disease. Therefore, we suggest that the CACNA1B gene can be a good candidate marker. The body weight trait in dogs has been examined in several studies based on the GWAS approach [29]. Though no previously reported genes/markers were found to be associated in our study, some of the candidate markers/genes identified in this study were on chromosome X (indicated as chr 39 in this study), which was previously reported to harbor genes/markers found to have a significant effect on body weight in dogs [29]. The lack of overlapping genes/marker with previous reports may be due to use of different breeds in this study, and the inclusion of higher number of large breed dogs, and differences in phenotype assignment.

In addition, compared to obesity in humans, there are only limited reports on genetic markers associated with canine obesity. In humans, more than 220 obesity-related genetic markers have been documented throughout the last decade [16]. Among these, FTO, BDNF, MC4R, PCSK1, MC3R, and PPARG genes were considered as promising candidate genes for canine obesity [16,17,18,43]. Recently, Mankowska et al. reported another candidate gene, POMC, which showed a strong association with weight and appetite in obesity-prone Labrador retrievers [19,20]. In the current study, several different candidate genes were found; however, none were common to those previously reported. However, the identified markers could serve as a good candidate for further studies.

3.3. Gene-Set Enrichment and Pathway Analysis

GWAS was complemented with a functional enrichment analysis to explore the significant pathways/GO terms associated with obesity and other traits. It showed the enrichment of annotated gene-sets which worked together in a network to carry out specific molecular processes. Out of 135,554 SNPs used in GWAS, 91,779 were located within mapped genes or the 5 Kb flanking regions of the mapped genes. A total of 1057, 1409, and 1243 SNPs showed an association for obesity, body weight, and blood sugar at a nominal threshold of p < 0.01, respectively (Supplementary Table S1). These SNPs were mapped to 449, 933, and 820 unique genes for obesity, body weight, and blood sugar trait, respectively (Supplementary Table S1). Subsequently, gene-set and pathway analysis were performed using DAVID. A total of 84, 114, and 120 GO terms and 21, 44, and 24 KEGG pathways were identified to be enriched for obesity, blood sugar, and body weight, respectively (Supplementary Table S2). Out of the total enriched GO terms and KEGG pathways, the top 5 significantly enriched GO terms and pathways are presented in Table 5. The pathways commonly enriched in all the three traits (obesity, blood sugar, and body weight) (Table 6) were: cfa04310—Wnt signaling pathway, cfa04520—adherens junction, cfa05200—pathways in cancer, cfa04360—axon guidance, and cfa04911—insulin secretion. Likewise, a total of 7 GO terms (GO:0045444—fat cell differentiation, GO:0005509—calcium ion binding, GO:0005737—cytoplasm, GO:0005634—nucleus, GO:0015914—phospholipid transport, GO:0007417—central nervous system development, GO:0009986—cell surface) were found to be commonly enriched in all the three evaluated traits (Table 6).

Table 5.

Top 5 Gene Ontology and KEGG pathways significantly enriched using genes associated with obesity, body weight, and blood sugar.

Trait Category Term_ID Term Count % p-Value Genes
Obesity KEGG_PATHWAY cfa04360 Axon guidance 10 0.015278 7.15 × 10−4 DCC, MAPK1, NGEF, EPHA7, CXCR4, GNAI1, ROBO1, UNC5D, LRRC4C, EPHB1
KEGG_PATHWAY cfa04550 Signaling pathways regulating pluripotency of stem cells 10 0.015278 0.0015 MAPK1, FGFR1, BMP2, ONECUT1, FZD1, FZD3, WNT11, FZD2, ZFHX3, KLF4
KEGG_PATHWAY cfa05200 Pathways in cancer 18 0.0275 0.0016 DCC, FGFR1, BMP2, COL4A1, BRAF, GNAI1, FGF9, RUNX1T1, FZD1, FZD3, FZD2, GLI3, CTNNA3, CTNNA2, LAMA2, MAPK1, CXCR4, WNT11
KEGG_PATHWAY cfa04520 Adherens junction 7 0.010695 0.0026 MAPK1, FGFR1, TJP1, PTPRM, SSX2IP, CTNNA3, CTNNA2
KEGG_PATHWAY cfa05217 Basal cell carcinoma 6 0.009167 0.0040 BMP2, FZD1, FZD3, WNT11, FZD2, GLI3
GOTERM_MF_DIRECT GO:0016874 Ligase activity 7 1.682692 3.74 × 10−4 HECW2, UBE3A, SUCLG2, HECTD3, SIAH1, SMURF1, NEDD4L
GOTERM_BP_DIRECT GO:0045892 Negative regulation of transcription, DNA-templated 13 3.125 0.0031 RBFOX2, BCLAF1, RUNX1T1, FZD1, PAX2, CBFA2T3, ADIPOQ, GAS6, ZSCAN10, LHX1, ATP8B1, POU3F3, WNT11
GOTERM_BP_DIRECT GO:0060022 Hard palate development 3 0.721154 0.0034 FZD1, FZD2, MMP25
GOTERM_BP_DIRECT GO:0034115 Negative regulation of heterotypic cell-cell adhesion 3 0.721154 0.0056 APOA1, ADIPOQ, KLF4
GOTERM_BP_DIRECT GO:0002062 Chondrocyte differentiation 5 1.201923 0.0057 SNX19, FGFR1, BMP2, FGF9, NFIB
Body weight KEGG_PATHWAY cfa05033 Nicotine addiction 8 0.900900 0.0021 GABRG3, GRIA2, GRIA1, GABRB1, GABRA5, GRIN2A, GRIN3A, CACNA1B
KEGG_PATHWAY cfa04080 Neuroactive ligand-receptor interaction 23 2.590090 0.0049 GABRG3, GLRA1, GRIK2, GABRB1, OPRK1, GRIN3A, P2RY6, GRIA2, GRIA1, NMUR2, HRH4, ADRA1A, NMBR, PRL, CHRNE, PTAFR, GRID1, GHR
KEGG_PATHWAY cfa05206 MicroRNAs in cancer 13 1.463963 0.0268 KIF23, PDGFA, SOCS1, MET, BMPR2, PIM1, TP63, ZEB1, IRS1, PDCD4, CCND1, CDKN2A, DNMT1
KEGG_PATHWAY cfa04022 cGMP-PKG signaling pathway 14 1.576576 0.0293 EDNRA, KCNU1, KCNMB4, PLCB4, TRPC6, ATP2A3, GTF2IRD1, ADRA1A, CREB5, NOS3, PLCB1, CACNA1D, IRS1, KCNMB2
KEGG_PATHWAY cfa04310 Wnt signaling pathway 12 1.351351 0.0394 DKK2, MAP3K7, CCND1, PLCB4, DKK1, VANGL1, PRICKLE1, MMP7, SIAH1
GOTERM_MF_DIRECT GO:0004725 Protein tyrosine phosphatase activity 15 1.689189 5.27 × 10−5 PTPRB, CDC14A, PTPN2, CDC14B, EPM2A, DUSP10, PTPN13, PTPRT, PTPRU, EYA3, EYA4, EYA1, DUSP26, UBASH3B, PTPN1
GOTERM_BP_DIRECT GO:0045444 Fat cell differentiation 12 1.3513513 1.90 × 10−4 BBS2, METTL8, CCND1, FAM120B, SMAD6, BBS9, FFAR2, SOCS1, OSBPL11, TTC8, PIAS1, PLCB1
GOTERM_CC_DIRECT GO:0005794 Golgi apparatus 43 4.8423423 3.54 × 10−4 GLIS3, ACHE, SYT4, RAB3GAP1, NOS3, CDK5RAP2, JAKMIP2, OLFM3, GOLM1, TERF2, CDK13, KLF5, NMNAT2, MSH6, CLN3, PLD1, MYO6, DNM1L, CCDC88A, LYN, ACO1, BEND5, GOLIM4, PKDCC, NMT2, ATF6, DUSP26, CPE, BACE2, SULF1, DYM, RAB14, SGCE, CWC22, EXT1
GOTERM_BP_DIRECT GO:0007156 Homophilic cell adhesion via plasma membrane adhesion molecules 14 1.5765765 4.51 × 10−4 CADM1, CLSTN2, SDK2, PCDH15, PCDH17, CDH8, CDH13, DSG2, CDH18, FAT1, CDH19, FAT2, CDH26, KIRREL3
GOTERM_CC_DIRECT GO:0043025 Neuronal cell body 16 1.8018018 7.20 × 10−4 GLRA1, GRIK2, DENND1A, GDPD5, GRIN3A, KLHL1, ALCAM, SEZ6L2, APOB, BRINP1, CPE, GRIA1, PSEN2, RAPGEF2, BRINP3, CACNA1B
Blood sugar KEGG_PATHWAY cfa04919 Thyroid hormone signaling pathway 14 1.837270341 8.07 × 10−4 ACTB, THRB, ATP1A1, RCAN2, PLCB3, CCND1, PLCB4, DIO2, GSK3B, PLCG2, PRKACB, PLCB1, AKT3, PIK3R1
KEGG_PATHWAY cfa05206 MicroRNAs in cancer 15 1.968503937 0.0019 IRS2, E2F3, MCL1, MMP16, CDK6, ZEB2, ZEB1, PRKCE, TIMP3, RPS6KA5, CCNE1, CCND1, PLCG2, DNMT1, ZFPM2
KEGG_PATHWAY cfa05223 Non-small cell lung cancer 9 1.181102362 0.0023 FHIT, CCND1, E2F3, PLCG2, CDK6, EGF, AKT3, PIK3R1, EML4
KEGG_PATHWAY cfa04916 Melanogenesis 12 1.57480315 0.0026 WNT1, PLCB3, PLCB4, GSK3B, MITF, EDN1, FZD1, KITLG, PRKACB, PLCB1, WNT7A, CALM1
KEGG_PATHWAY cfa04750 Inflammatory mediator regulation of TRP channels 12 1.57480315 0.0031 PLCB3, IL1R1, PLCB4, PLCG2, IL1RAP, PLA2G6, PRKCH, PRKACB, PRKCE, PLCB1, PIK3R1, CALM1
GOTERM_MF_DIRECT GO:0004222 Metalloendopeptidase activity 15 1.968504 0.0011 ELP3, IRS2, BMP2, LYN, EDN1, HGF, DAB2, SEMA6D, FOXF1, SEMA3C, COL1A1, PDGFD, PAK1, PIK3R1, CSF1R
GOTERM_BP_DIRECT GO:0030335 positive regulation of cell migration 31 4.068241 0.0017 FRK, THRB, MITF, EDN1, ZEB2, PRDM16, EPC1, REL, FOXF1, PRMT6, ETV6, DLG1, SIM2, ALX1, TBL1XR1, BMP2, ASXL1, ZHX2, LMCD1, SMYD2, SHOX2, HDAC4, CCND1, DKK1, DUSP26, PDE2A, HOPX, TFAP2B, DNMT1, RIPPLY2, BMP6
GOTERM_BP_DIRECT GO:0000122 negative regulation of transcription from RNA polymerase II promoter 27 3.543307 0.0017 CYB5R4, CAV2, GALNT1, RAB3C, PKHD1, SLC39A12, PINK1, ARFGEF1, SLC11A2, APP, BDNF, ECE1, PTK2B, FAT1, TMEM192, CDK5RAP2, DLG1, PTPRM, LYN, STC2, PRKCE, PDE2A, VAMP8, GSK3B, CYFIP2, AKAP6, SPAST
GOTERM_CC_DIRECT GO:0048471 perinuclear region of cytoplasm 11 1.44357 0.0039 EPHA5, SEMA6A, ZNF280D, KIF5B, ANK3, ROBO1, SEMA3C, ETV1, RELN, UNC5D, CSF1R
GOTERM_BP_DIRECT GO:0007411 axon guidance 8 1.049869 0.0041 SOX10, EDN3, SEMA6A, SEMA6D, SEMA3C, KITLG, ZEB2, ALX1
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Table 6.

The significantly enriched GO terms and KEGG pathways shared amongst obesity, body weight, and blood sugar.

Category Term_ID Term Genes a
GOTERM_BP_DIRECT GO:0045444 Fat cell differentiation AQP1
GOTERM_MF_DIRECT GO:0005509 Calcium ion binding CACNA1B, GRIK2
GOTERM_CC_DIRECT GO:0005737 Cytoplasm ROBO1, LDHB
GOTERM_CC_DIRECT GO:0005634 Nucleus ATP2B1, CSRNP3, CSRNP2
GOTERM_BP_DIRECT GO:0015914 Phospholipid transport PCTP
GOTERM_BP_DIRECT GO:0007417 Central nervous system development CHD7
GOTERM_CC_DIRECT GO:0009986 Cell surface IL1R1, ROBO1, WNT1
KEGG_PATHWAY cfa04310 Wnt signaling pathway WNT11, MAP3K7, WNT7A
KEGG_PATHWAY cfa04520 Adherens junction MAPK1, CTNNA2, MAP3K7
KEGG_PATHWAY cfa05200 Pathways in cancer CCND1, FZD1, FZD3, FZD2, CTNNA1
KEGG_PATHWAY cfa04360 Axon guidance MAPK1, ROBO1
KEGG_PATHWAY cfa04911 Insulin secretion CACNA1B, CACNA1D, PLCB4
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a Enriched genes in KEGG pathways and GO shared among three traits, GO—Gene Ontology, KEGG—Kyoto Encyclopedia of Genes and Genomes.

3.3.1. Wnt Signaling, Adherens Junction, and Axon Guidance Pathways

Wnt signaling is a crucial pathway for adipogenesis. This pathway is involved in the development of obesity-induced insulin resistance [44,45]. Wnt signaling has been proposed as a targeting pathway to fight obesity due to its critical role in the development of white adipose tissue and brown adipose tissue [46]. Moreover, an important transcription factor of Wnt signaling was previously identified as a candidate gene for type 2 diabetes [47]. In addition, adherens junction and axon guidance were significantly enriched in obesity, blood sugar, and body weight. An adherens junction is an intercellular junction and is important for epithelial adhesion. Adherens junction plays a key role in the regulation of insulin secretion using cadherin protein which ultimately reveals strong association with blood glucose level, obesity, and diabetes [48]. Amongst the enriched genes in these pathways, Wnt family member 1 (WNT1) was previously reported to be associated with obesity and the importance of this gene in obesity was also highlighted [46,49].

3.3.2. Cancer-Related Pathways

The pathways in cancer (cfa05200) were significantly enriched in all the three traits including obesity, blood sugar, and body weight. Apart from that other significantly enriched cancer-related pathways, including, endometrial cancer (cfa05213), melanoma (cfa05218), prostate cancer (cfa05215), proteoglycans in cancer (cfa05205) were detected with some pathways being in common among all traits. The association between cancer and obesity is well documented, obesity increases the risk of various cancers in the human population [50,51]. Further, Lim et al. reported that the increased expression of obesity-associated molecules increases the risk of development of mammary gland tumors in obese female canines [51]. These pathways are enriched with numerous genes and in fact, the mutations in a few key genes have been described to affect obesity or blood sugar trait [12,49,52,53,54]. In total, 64 genes were significantly enriched in cancer-related pathways and several significant genes such as cyclic AMP-responsive element-binding protein 5 (CREB5) [55], AKT serine/threonine kinase 3 (AKT3) [53], cyclin D1 (CCND1) [12,52], Wnt family member 1 (WNT1) [49,54] were identified to be associated with blood sugar and obesity traits.

3.3.3. Insulin Secretion Pathway

Another important pathway enriched with genes harboring SNPs associated with obesity was insulin secretion (cfa04911). This pathway was enriched with 7 genes in total. Insulin secretion occurs in pancreatic β cells and it triggers the fusion of insulin-containing granules with the cell membrane [10]. Insulin secretion is an integral part of blood glucose levels control system. [10]. The positive association between obesity and insulin has been investigated for decades [10,11]. Obesity-associated with insulin resistance is a major risk factor of type 2 diabetes. However, insulin also plays a vital role in obesity by inducing ATP production in mitochondria to prevent 5՛ AMP-activated protein kinase (AMPK) activity during hyperinsulinemia condition [56]. Recently, an important link between the insulin level and obesity was reported: diet-induced hyperinsulinemia assisted by glucagon hormone is an obligatory factor for obesity [11].

3.3.4. Other Enriched Gene Ontology Terms

We have highlighted seven important GO terms, which were significantly enriched in all three traits. These included fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface. The fat cell differentiation or adipocyte formation plays a crucial role in adipogenesis during obesity [46]. Calcium is an important ion for normal physiological functioning and calcium ion binding is involved in calcium signaling pathways which have been shown as key pathways in the regulation of obesity in many studies [40,41,42,57]. The multiple calcium signaling pathways play a most important role in the biological clock, neuronal excitability, and intestinal microbial activity for regulating the food intake and adipocyte metabolism, which subsequently reduces the occurrence of obesity [57]. The genes enriched included CACNA1B, which also harbored the significantly associated SNP BICF2P116826, encoding calcium voltage-gated channel subunit alpha1 B, which is associated with adipogenesis [40,41,42] and most importantly, it was identified as a candidate gene for body weight trait in our association study. Furthermore, the relation of phospholipid transport and obesity has been suggested to play a vital role in lipoprotein metabolism by transferring the phospholipids from triglyceride-rich lipoproteins to high-density lipoproteins [58].

4. Conclusions

The present study is the first to report about a post-GWAS analyses approach to prioritize the identification of genetic loci, pathways, and genes underlying molecular mechanisms of canine obesity and related traits such as body weight and blood sugar in multi-breed dogs. We have identified several significant candidate genes associated with obesity-related traits; in particular, CACNA1B gene harboring SNP BICF2P116826 could be a possible candidate gene for canine obesity. The gene-set and pathway analyses revealed five shared pathways, (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms to be associated with all evaluated traits, which probably explain that obesity is a polygenic trait. Overall, our results provide clues for identification of candidate SNPs and genes which have a significant impact on the etiology of canine obesity.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-2615/10/11/2071/s1, Table S1: List of all SNPs (p < 0.01) associated with obesity, body weight, and blood sugar that were used for gene-set and pathway analysis, Table S2: List of all Gene Ontology and KEGG pathways significantly enriched using genes associated with obesity, body weight, and blood sugar.

Click here for additional data file. (412.7KB, zip)

Author Contributions

Conceptualization, S.S., S.K., and B.-H.C.; methodology, S.S., S.K., J.C., and B.-H.C.; software, J.C., S.C; validation, S.S., S.K., and B.-H.C.; formal analysis, S.S. and S.C.; investigation, B.-H.C.; resources, B.-H.C. and J.C.; data curation, S.S., S.K.; writing—original draft preparation, S.S.; writing—review and editing, S.S. and S.K.; visualization, S.S., S.K.; supervision, S.K., B.-H.C.; project administration, B.-H.C.; funding acquisition, B.-H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the funds from AGENDA project (grant no. PJ012833022019) of the National Institute of Animal Science, Rural Development Administration, Republic of Korea.

Conflicts of Interest

The authors declare that they have no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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