Genes & Genomics

, Volume 39, Issue 3, pp 285–293

Association of MITF loci with coat color spotting patterns in Ethiopian cattle

  • Zewdu Edea
  • Hailu Dadi
  • Tadelle Dessie
  • Il-Hoi Kim
  • Kwan-Suk Kim
Research Article

DOI: 10.1007/s13258-016-0493-4

Cite this article as:
Edea, Z., Dadi, H., Dessie, T. et al. Genes Genom (2017) 39: 285. doi:10.1007/s13258-016-0493-4
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Abstract

The genetics of coat color have been the focus of investigation for decades because beyond its aesthetic values, coat color is associated with thermo-tolerance, production and health traits. Despite the fascinating coat color phenotypes observed in Ethiopian cattle populations, up to now, there are no studies performed to identify and characterize polymorphisms associated with such variation at the genome level. In an attempt to identify and map the genetic basis of coat color variation in Ethiopian cattle, a genome-wide association study (GWAS), selection signatures test and network analysis were performed in 187 cattle populations genotyped on Illumina high-density chip. Loci significant at the genome-wide level (P ≤ 8.29 × 10−7) and show selection signals (FST − 5SNP window = 0.13) were mainly localized on BTA22 (31.53–31.99) within the MITF gene. Network and functional annotation clustering analyses revealed that the candidate genes are involved in important pathways including melanogenesis. The results of the present study suggest a role of the MITF gene and its interaction with other genes in determining the spotting patterns observed in the Begait and Fogera cattle populations.

Keywords

Ethiopian cattle Genome-wide study Spotting patterns 

Introduction

Coat color is an important qualitative trait as it is associated with thermo-tolerance, parasite loads, production, and health traits (Hansen 2004; Hughes et al. 1994; Olson et al. 2003). In cattle, aberrant formation and migration of melanocytes during embryonic development lead to white-spotting or piebaldism (Haase et al. 2013). The phenotypes of white spotting patterns have received the focus of scholars because animals with higher proportion of white/light coat absorb less solar radiation (Stewart 1953), associated with production (Becerril et al. 1994; Reinsch et al. 1999), and health traits (Anderson 1991; Brown et al. 1994). The genetics of coat color in general and white-spotting in particular have been found to be controlled by complex gene interactions (Bennett and Lamoreux 2003; Fontanesi et al. 2010b). A mounting body of evidence shows that variation in the proto-oncogene receptor tyrosine kinase (KIT) gene has been shown to affect spotting patterns in mammalian species including cattle (Hayes et al. 2010), horses (Haase et al. 2009), pigs (Andersson et al. 2011) and mice (Baxter et al. 2004). Microphthalmia-associated transcription factor (MITF) is gene known to be associated with white spotting patterns in cattle (Philipp et al. 2011a; Qanbari et al. 2014), dogs (Rothschild et al. 2006) and horse (Hauswirth et al. 2012). In mice, mutations in this gene lead to loss of pigmentation and several defects (Hodgkinson et al. 1993; Steingrimsson et al. 2004).

Thus far, the vast majority of the white spotting phenotype related studies were based on candidate gene- approaches focusing on few commercial beef and dairy cattle breeds (Herford, Holstein, Simmental and Angus) (Grosz and MacNeil 1999; Olson, 1999; Philipp et al. 2011b) subjected to strong human-mediated selection. However, the patterns of coat color observed in modern and traditional cattle breed populations are quite variable. It therefore can be hypothesized that the observed variation in spotting patterns is determined by different coat color genes and mutations. Traditional populations such as Ethiopian cattle show a considerable variation in coat colors and patterns. Some of these populations are characterized as being black and white coat patterns (Begait and Fogera), solid white (Borana and Ogaden) and black, red or brown (Arsi and Guraghe). Along with environmental adaptation, breed hybridization has contributed to the array of coat color phenotypes observed among Ethiopian cattle populations. Hence, Ethiopian cattle represent an excellent model for studies of coat color genetics. Despite the fascinating coat color observed among Ethiopian cattle populations, up to now, there are no studies performed to map and characterize polymorphisms associated with coat color variation. The availability of high-density bovine SNP chips can aid to achieve a better understanding of the genetics of coat color and subsequent application of markers assisted breeding for a desired coat color phenotype. In this study, we carried out genome-wide association study (GWAS) on 187 animals genotyped on high-density indicine derived chip (60283 SNPs) to map genomic regions associated with coat color spotting patterns in Ethiopian cattle populations. Moreover, we have performed genome-wide selection signatures test evidence of selective sweep for coat color variation. Using GWAS and selection signatures test, we identified potential candidate loci for spotting patterns within the MITF gene.

Materials and methods

Cattle populations, quality control and genotyping

A total of 187 animals representing six Ethiopian cattle populations were sampled from diverse agro-ecological zones of the country. These six populations include Begait (n = 36), Fogera (n = 38), Borana (n = 39), Ogaden (n = 20), Guraghe (n = 26) and Arsi (n = 28). The Begait and Fogera cattle populations are being characterized by spotted coat color of white and black and assigned to the spotted group. The Borana and Ogaden populations inhabit low altitude environments and show solid white coat color, whereas the Arsi and Guraghe populations dominate high altitude areas and mainly display solid black, red or brown coat colors (Supplementary Fig. 1) and assigned to the non-spotted group. The sampled populations were grouped based on their coat color patterns (spotted, n = 74 vs. non-spotted, n = 113). Nasal swab samples were collected using the Performagene LIVESTOCK nasal swab (DNA Genotek., Kanata, ON, Canada). All samples were genotyped using the GeneSeek Genomic Profiler HD BeadChip (GeneSeek, Lincoln, NE, USA), an Illumina Infinium array consisting of SNPs derived mainly from Bos. indicus according to Illumina’s standard protocols (http://www.illumina.com). Among 69,704 autosomal SNPs, a total of 60,283 markers were retained after applying editing criteria of call rate ≥98%, minor allele frequency (MAF) ≥0.05 and Hardy–Weinberg equilibrium (HWE) (<0.0001). In addition, individual samples with a call rate ≤ 95% were removed.

Genome-wide association and selection signature analyses

A case–control genome-wide association study on 187 individuals representing spotted and predominantly non-spotted populations was performed using the SNP and Variation Suite version 8.4.4 (Golden Helix Inc., Bozeman, MT, USA, www.goldenhelix.com). Significant SNPs were determined when the P value was less than the genome-wide type I error rate, adjusted with Bonferroni correction by using α/K, where α = 0.05 and K = number of SNPs. Significant threshold value was set at P < 8.29 × 10−7 = 0.05/60,283. In addition, haplotype blocks were detected according to (Gabriel et al. 2002) and haplotype association was carried out based on χ2 tests. Genome-wide association tests were also performed using single-locus mixed linear model GWAS (EMMAX) (Kang et al. 2010), which includes a kinship matrix as a random effect and implemented using the SNP and Variation Suite. For the significant loci on BTA22, linkage disequilibrium (LD) was performed by using Haploview software with default settings (Barrett et al. 2005).

ARLEQUIN software version 3.5 (Excoffier and Lischer 2010) was also employed to estimate diversity indices, molecular variance, and departure from Hardy–Weinberg equilibrium (HWE) for the significantly differentiated loci within the MITF gene on BTA22. Moreover, pairwise FST was calculated between populations predominantly displaying spotting patterns and solid colored groups in 5 SNP sliding windows according to (Weir and Cockerham 1984) to test signatures of selection for coat color variation. The FST approach is valuable to detect loci that were under selection in different breeds (Biswas and Akey 2006). As in (Kijas et al. 2012; Zhang et al. 2013), only the top 0.1% FST values were considered to represent evidence of positive selection. Within the significant and highly differentiated SNPs and or regions, we explored for potential candidate genes that play a role in coat color variation from NCBI database (http://www.ncbi.nlm.nih.gov/). Gene network analysis offers a useful clue into the genetic architecture underlying complex traits (Fortes et al. 2011). Hence, we further performed gene network analysis using the annotated genes by employing GeneMANIA (www.genemania.org/plugin/) implemented in Cytoscape software 3.4 (Shannon et al. 2003). Functional annotation clustering of the candidate genes was performed using the web-based DAVID Bioinformatics resources (https://david.ncifcrf.gov/). As recommended by the software, fisher exact test (P value < 0.05) was considered to identify significantly enriched terms.

Results

Genome-wide association study (GWAS) and candidate regions

Our GWAS using the additive model identified a total of 14 SNPs that reached genome-wide significant levels (P ≤ 8.29 × 10−7) containing 10 annotated genes. Manhattan plot of −log10 (P value) across the genome is shown in Fig. 1 The most significant SNP (BovineHD0600020037) on BTA6 (72.09 Mb) was located in close vicinity to two known cattle coat color genes (KIT and KDR). Of 14 SNPs significant at the genome-wide levels, 3 of them were located on BTA 22 (31.77–31.95 Mb) within MITF gene, a gene known to affect spotting patterns in mammals (Table 1). In addition, our single-locus mixed model association analysis revealed that the region between 31.77 and 31.95 Mb on BTA22 harboring three MITF loci (BovineHD2200009075, BovineHD2200009082, and BovineHD2200009113). Together, the three loci explained about 11% of the phenotypic variation (data not shown). The SNP (BovineHD0600020037) on BTA6 near the KIT/KDR explained the largest proportion of variance (15%).
Fig. 1

Manhattan plot for the genome-wide association study for coat color variation in Ethiopian cattle populations following the additive model in trend/correlation test. The dashed and the solid lines denote the genome-wide and suggestive significance thresholds, respectively

Table 1

Results of 14 significance SNPs identified through GWAS for coat color pattern variation in Ethiopian cattle populations

Marker

Chr

Position

−Log10 P

Bonf. P

Nearest gene

BovineHD0600020037

6

72096909

10.49

1.96E−06

KIT, KDR

BovineHD0700002072

7

8133249

9.94

6.86E−06

LOC512249

BovineHD2800013810

28

44855822

9.00

6.05E−05

LOC101903324

BovineHD2200017413

22

60013680

8.68

0.000

GATA2

BovineHD2200009113

22

31958429

8.43

0.000

MITF

BovineHD0800007084

8

23590639

8.16

0.000

FOCAD

BovineHD0500035880

5

32691596

7.92

0.001

RAPGEF3

BovineHD0600019156

6

69326735

7.77

0.001

CWH43

BovineHD2200017410

22

60001708

7.77

0.001

GATA2

BovineHD2200009082

22

31810687

7.74

0.001

MITF

ARS-BFGL-NGS-1781

4

94379469

7.55

0.002

NRF1

BovineHD2200009075

22

31770075

7.50

0.002

MITF

BovineHD1100014549

11

49533980

7.47

0.002

TCF7L1

BovineHD2000005526

20

18376442

7.39

0.002

ERCC8

Linkage disequilibrium and haplotype association test

A linkage disequilibrium (LD) analysis was performed for SNPs on BTA 22 using the Haploview software. We detected a haplotype block composed of four SNPs in spotted population spanning a 188 kb region containing MITF gene, whereas the non-spotted population showed no block in this region. Four SNPs (BovineHD2200009075, BovineHD2200009082, BovineHD2200009098 and BovineHD22000090113) within MITF gene are in complete linkage disequilibrium with each other (D’ = 1) (Fig. 2). In this block, four haplotypes were inferred including AAGT, GGAG, AAGG and AAAG) at frequencies of 0.70, 0.19, 0.03 and 0.07, respectively. In contrary, of the inferred haplotypes, the GGAG haplotype was the most frequent (0.41) in the non-spotted population (Supplementary Table 1). The high frequency of AAGT haplotype in Begait and Fogera cattle (spotted populations) seems that the selection signatures on coat color tend to favor the AAGT haplotype in Begait and Fogera cattle populations. Our haplotype association test further detected two SNPs and the most significant (−log10 = 8.13) SNP (BovineHD2200009098) is located within the MITF gene.
Fig. 2

Genome-wide haplotype association for coat color variation (left) and haplotype block in the MITF region in spotted Ethiopian cattle population (right). The dashed and the solid lines denote the genome-wide and suggestive significance thresholds, respectively

Selection signatures and diversity indices

To capture footprints of selection for coat color phenotypes, a genome-wide selection signatures test was carried out using the five SNP sliding-windows between the two groups of cattle populations. Considering the top 0.1% smoothed FST values as outliers, 18 regions distributed over 14 autosomal chromosomes were found to be under directional selection (Supplementary Table 1). Figure 3 shows a plot of the genome-wide distribution of smoothed FST values across the autosomes. Concurrent with GWAS results, our genome-wide scans using smoothed FST for 60,283 SNPs revealed that the largest number of loci under selection was observed on bovine chromosome 22 (BTA22). In this study, the average estimated FST was only 0.01, but two SNPs (BovineHD2200009075 and BovineHD2200009113) located within the MITF gene had FST values of 0.15–0.17 which are high according to Wright (1978).
Fig. 3

Manhattan plots of selection signals in the genome of Ethiopian cattle populations displaying spotted and non-spotted coat color patterns. a Across autosomes. b On BTA22

Fig. 4

Representation of the candidate selected gene network for GWAS. The candidate genes are colored in black and genes colored in gray represent associated genes. The solid green line illustrates gene interaction. The rectangles represent pathways

Likewise, the highest FST window was detected on BTA22 at 31.7–31.8 Mb (FST − 5SNP window = 0.13; Supplementary Table 1 and Fig. 3) containing the MITF gene. The KIT gene on BTA 6 (71.8–72.0) Mb) (FST − 5SNP window = 0.08) has previously been reported to be involved in white spotting patterns. Interestingly, our single markers based FST analysis detected a strong selection signal (FST = 0.19) on BTA6 at the position 72.0 Mb close to the KIT and KDR genes (data not shown). We also calculated genetic diversity indices, haplotype distribution and compared the genotypes of 4 MITF loci variability in cattle of six populations (Table 2). Analysis of molecular variance (AMOVA) showed that genetic differentiation between spotted and non-spotted populations for the four loci amounted to be 14.59%. For these loci, high and significant differentiation (P < 0.05) was observed between the two spotted and the other four cattle populations, but no significant differentiation (P > 0.05) was observed among the non-spotted populations. The largest genetic differentiation (FST = 0.23) was observed between the Begait and Guraghe cattle. Over the four loci, the average expected heterozygosity (He) varied from 0.33 ± 0.05 in Begait to 0.51 ± 0.00 in Ogaden cattle (Table 2). The inference of diversity indices for the MITF loci revealed that a higher level of genetic variation was retained in the unspotted populations. In particular, for the upstream loci (BovineHD2200009075), the observed heterozygosity (Ho) ranged from 0.28 in spotted Begait to 0.55 in non-spotted Ogaden cattle. For the same loci, the average homozygous genotype (A/A) was the most frequent (0.69) in the Begait cattle, whereas it was only 0.19 in Guraghe cattle (Table 2). These results revealed that the homozygous (A/A) genotype was favored in spotted population, whereas the heterozygous (AG) genotype was common in the other cattle populations. An inbreeding coefficient of 0.013 and 0.014 for the upstream locus (BovineHD2200009075) was obtained for Arsi and Begait cattle, respectively. None of the loci was deviated from Hardy–Weinberg equilibrium (HWE) (P > 0.05).
Table 2

Genetic variability of the MITF gene loci in 6 Ethiopian cattle populations

SNP ID

Genotype and Haplotype

Begait

Fogera

Borana

Ogaden

Guraghe

Arsi

Spotted

Non-spotted

BovineHD2200009075

AA

0.69

0.61

0.26

0.25

0.19

0.29

0.65

0.25

 

AG

0.28

0.36

0.62

0.55

0.62

0.50

0.32

0.58

 

GG

0.03

0.03

0.12

0.20

0.19

0.21

0.03

0.18

 

MAF

0.17

0.21

0.44

0.47

0.50

0.46

0.19

0.46

 

Ho/He

0.28/0.28

0.37/0.33

0.62/0.49

0.55/0.50

0.62/0.50

0.50/0.50

0.32/0.31

0.57/0.50

BovineHD2200009082

AA

0.67

0.61

0.23

0.25

0.19

0.29

0.64

0.24

 

AG

0.31

0.36

0.59

0.60

0.54

0.50

0.34

0.56

 

GG

0.02

0.03

0.18

0.15

0.27

0.21

0.03

0.20

 

MAF

0.18

0.21

0.47

0.45

0.46

0.46

0.20

0.48

 

Ho/He

0.31/0.30

0.37/0.33

0.59/0.50

0.60/0.50

0.54/0.50

0.50/0.50

0.34/0.32

0.56/0.50

BovineHD2200009098

AA

0.03

0.05

0.26

0.20

0.27

0.21

0.04

0.24

 

AG

0.39

0.53

0.49

0.55

0.54

0.54

0.46

0.52

 

GG

0.58

0.42

0.25

0.25

0.19

0.25

0.50

0.24

 

MAF

0.22

0.32

0.50

0.47

0.46

0.48

0.27

0.50

 

Ho/He

0.39/0.34

0.53/0.43

0.49/0.50

0.55/0.50

0.54/0.50

0.54/0.50

0.46/0.39

0.52/0.50

BovineHD2200009113

GG

0.08

0.05

0.31

0.30

0.38

0.36

0.07

0.34

 

GT

0.36

0.58

0.59

0.55

0.58

0.48

0.47

0.55

 

TT

0.56

0.37

0.10

0.15

0.04

0.18

0.46

0.12

 

MAF

0.26

0.34

0.40

0.42

0.33

0.41

0.30

0.39

 

Ho/He

0.36/0.39

0.58/0.45

0.59/0.48

0.55/0.49

0.58/0.44

0.46/0.48

0.47/0.42

0.55/0.48

Haplotype

AAGT

0.74

0.66

0.40

0.36

0.28

0.39

0.70

0.36

 

GGAG

0.17

0.21

0.42

0.42

0.39

0.39

0.19

0.41

 

AAGG

0.04

0.03

0.09

0.10

0.10

0.05

0.03

0.08

 

AAAG

0.04

0.11

0.04

0.01

0.08

0.10

0.07

0.06

 

AGAG

0.01

0.04

0.04

0.01

0.02

 

GGGG

0.01

0.03

0.06

0.06

0.04

 

GGGT

0.02

0.02

0.01

 

GGAT

0.03

0.01

 

GAGT

0.02

0.00

 

AAAT

0.04

0.00

MAF minor allele frequency, He observed heterozygosity, He expected heterozygosity

Gene network and functional annotation analyses

Currently, network based GWAS analysis have been gaining a momentum and offers insights into the in-depth understanding of molecular basis of complex phenotypes (Wang et al. 2009). Referencing the human genome, we performed network analysis to identify relevant pathways containing the candidate genes. Accordingly, results revealed that the candidate genes involved in different pathways including melanogenesis, Kit rector, acute myeloid leukemia, and cancer. Furthermore, our results demonstrated that melanogenesis is controlled by complex networks of several genes rather than the action of single genes. The network harbors genes (KIT, MITF, TRY, TYRP1, LEF1 and FC7L1) involved in melanogenesis (Fig. 4). Some of the Gene Ontology (GO) terms were found to be related to developmental pigmentation (GO: 0048066; P = 1.96E−05), pigmentation (GO: 0043473; P = 3.14E−04) and pigment cell differentiation (GO: 0050931; P = 0.031055366). Functional annotation clustering analysis identified 14 terms associated with the candidate genes, in which, three genes (MITF, KIT, and TCF7L1) are participated in melanogenesis (Table 3).
Table 3

Results of candidate genes functional clustering analysis

Category

Terms

Genes

Fisher exact

INTERPRO

Tyrosine-protein kinase, receptor class III, conserved site

KIT, KDR

4.40E−06

KEGG_PATHWAY

Melanogenesis

MITF, KIT, TCF7L1

7.30E−05

INTERPRO

Immunoglobulin

KIT, KDR

2.00E−04

UP_KEYWORDS

Tyrosine-protein kinase

KIT, KDR

2.40E−04

INTERPRO

Tyrosine-protein kinase, catalytic domain

KIT, KDR

4.30E−04

INTERPRO

Tyrosine-protein kinase, active site

KIT, KDR

4.70E−04

KEGG_PATHWAY

Rap1 signaling pathway

RAPGEF3, KIT, KDR

7.20E−04

SMART

SM00219

KIT, KDR

7.80E−04

KEGG_PATHWAY

Acute myeloid leukemia

KIT, TCF7L1

1.10E−03

INTERPRO

Serine-threonine/tyrosine-protein kinase catalytic domain

KIT, KDR

1.30E−03

UP_KEYWORDS

Immunoglobulin domain

KIT, KDR

1.80E−03

INTERPRO

Immunoglobulin subtype 2

KIT, KDR

2.20E−03

SMART

SM00408

KIT, KDR

3.90E−03

KEGG_PATHWAY

Pathways in cancer

MITF, KIT, TCF7L1

4.40E−03

Discussion

White spotting in bovine sub-species has been the subject of interest for decades (Olson 1999). Most of the earlier works were based on candidate gene approaches which cannot capture variation at the whole-genome level and also limited to few commercial breeds (Grosz and MacNeil 1999; Philipp et al. 2011b). To cast light on the genetics of coat color variation in Ethiopian indigenous cattle populations, we applied GWAS, selection signature, network, and functional annotation analyses. To our knowledge, this is the first effort to map genomic regions associated with coat variation in Ethiopian cattle populations using high-density SNPs.

Signatures of selection at the whole-genome level were investigated by estimating divergence in allele frequencies between the spotted and non-spotted groups, based on pairwise FST approach. Our genome-level scans did detect selection signal on BTA22 (31.53–3.9 Mb) harboring the MITF gene. The significant genetic differentiation observed between the cattle populations with spotted and non-spotted phenotypes could be a result of breeders’ preference and/or natural selection for ecological adaptation. Following domestication, farm animals have been selected for specific coat color phenotypes (Andersson and Georges 2004). In cattle populations such as Borana and Ogaden adapted under hot environments, solid white color is the highly valued and preferred trait among the cattle breeders over any other color phenotypes. On the other hand, populations adapted under high-altitude and cooler areas such as the Arsi cattle, mainly show black coat color. Physiologically, animals that display lighter coat color tend to adapt better to high levels of solar radiation (Olson 1999). In contrast, black colored animals absorb more heat and poorly adapted to heat stress environments (Gaughan et al. 2008). Based on selection theory, directional selection reduces variation at selected and neutral linked loci. The lower genetic diversity noted for MITF loci in spotted populations was more likely influenced by selection. The high frequency of AAGT haplotype of MITF loci in Begait (0.74) and Fogera cattle (0.66) suggests that the selection of coat color tend to favor this haplotype over the other. On the other hand, the GGAG haplotype is the most frequent and likely associated with solid coat color phenotypes.

MITF and KIT as candidates for spotting patterns

MITF is a transcription factor that plays a central role in regulating melanocyte (Hou and Pavan, 2008) via binding to a highly conserved M-box and E-box motif upstream of the tyrosinase promoter (Bauer et al. 2009; Bertolotto et al. 1996; Yasumoto et al. 1994). Previous studies have also shown that the MC1R gene which determines coat color in cattle (Klungland et al. 1995) expression is regulated by the MITF (Qanbari et al. 2014). In German Fleckvieh cattle, mutations of MITF gene were associated with a dominant white and deafness (Philipp et al. 2011b). In their candidate gene studies, (Fontanesi et al. 2012) detected association of polymorphisms within MITF gene at 32 Mb with piebaldism in Holstein and Simmental cattle breeds (Fontanesi et al. 2012). In mice, mutations in this gene have resulted in white spotting and deafness (Hodgkinson et al. 1993; Moore, 1995; Steingrimsson et al. 1994). Also, mutations within MITF reported being associated with white spotting phenotypes in swamp Buffalo (Yusnizar et al. 2015). GWAS and selection signals analyses with a sample size of 99 detected signals of positive selection containing MITF genes in Finnsheep in the comparison of white and non-white coated individuals (Li et al. 2014).

Studies in dogs have shown that regulatory variations in the MITF gene are associated with spotting patterns (Karlsson et al. 2007). Additionally, it has been suggested that mutation in the promoter regions of MITF could be associated with the distribution of the white coat color in spotted German Fleckvieh cattle (Philipp et al. 2011b).The methods we applied have consistently detected BovineHD2200009075 SNP in the upstream of MITF gene to highly differentiated between spotted and non-spotted cattle populations. It is, therefore, possible that the locus in the upstream of the MITF gene and in strong LD with other loci may affect the spotting patterns in Begait and Fogera cattle populations.

KIT encodes stem cell growth factor receptor and act as an important survival factor for migrating and proliferation of melanoblasts (Blume-Jensen et al. 1991; Steel et al. 1992). Genetic evidence shows that polymorphisms within the KIT gene have been shown to affect spotting patterns in Hereford, but not in Holstein cattle (Fontanesi et al. 2010b; Hayes et al. 2010). In dairy and beef cattle breeds, mutations within the KIT gene influencing spotting phenotypes were detected (Fontanesi et al. 2010a).Variants at the KIT are also responsible for the dominant white coat color in pigs (Moller et al. 1996) and mice (Baxter et al. 2004). Additionally, KIT contains QTL for UV resistance (Pausch et al. 2012). Genome-wide case/control test with a total sample size of 90 identified KIT as a candidate gene for the roan phenotype in pigs (Cho et al. 2011).

In conclusion, the results of the present study provide new insights into the genetics of coat color variation in indigenous Ethiopian cattle populations. The MITF gene is widely known to be associated with spotting patterns and plays a vital role in the regulation of the TYR, TRP-1, and DCT genes (Busca and Ballotti 2000; Widlund and Fisher 2003). As the SNPs upstream of the gene are important in regulating the level of gene expression, we suggest that the new variant in the upstream of MITF gene is the most likely candidate for spotting phenotypes in Begait and Fogera cattle populations. Further functional studies, including candidate gene sequencing are, however, necessary to validate and refine our results.

Acknowledgements

This work was supported by the research grant of Chungbuk National University in 2014 and Agenda (PJ01040601) of the National Institute of Animal Science, Rural Development Administration (RDA), Korea.

Compliance with ethical standards

Conflict of interest

Zewdu Edea declares that he has no conflict of interest., Hailu Dadi declares that he has no conflict of interest., Tadelle Dessie declares that he has no conflict of interest., Il-Hoi Kim declares that he has no conflict of interest. Kwan-Suk Kim declares that he has no conflict of interest.

Ethical approval

The research was conducted in the absence of any ethical issue on animal research.

Supplementary material

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Supplementary material 1 (DOCX 240 kb)
13258_2016_493_MOESM2_ESM.xls (24 kb)
Supplementary material 2 (XLS 23 kb)

Copyright information

© The Genetics Society of Korea and Springer-Science and Media 2016

Authors and Affiliations

  1. 1.Department of Animal ScienceChungbuk National UniversityCheongjuSouth Korea
  2. 2.Department of BiotechnologyAddis Ababa Science and Technology UniversityAddis AbabaEthiopia
  3. 3.International Livestock Research Institute (ILRI)Addis AbabaEthiopia