Development and validation of KASP markers for the milk traits genes in Kazakh horse

ABSTRACT

There is an increasing demand for horse milk, but the current domestic performance of the horse milk has not yet conducted a systematic in-depth study. We carried out a pilot study on 13 single nucleotide polymorphisms (SNPs) located within five genes in Kazakh horse using Kompetitive Allele Specific PCR (KASP^TM). An association study with average daily milk production of 60 Kazakh mares was performed for 6/13 polymorphic SNPs. The results showed that only two SNPs rs114262445 (NUMB), rs1136347938 (CA8) out of the 6 polymorphic markers were significantly associated with milk production in Kazakh horses. These results indicate the potential of using SNPs for the NUMB and CA8 genes as candidate genes for selection and farm production outputs. This study is relevant for future horse genomic studies.

KEYWORDS:

• Kazakh horse
• KASP
• NUMB
• CA8

Introduction

Kazakh horse is an autochthonous breed in central Asia, adapted to harsh environment. Milk performance and sports trait were developed by artificial selection (Bahetiguli 2011). Mare’s milk has a sweet taste that ranges from bluish-white to slightly brown colouration. Important components of the milk are lactose, protein, fat, vitamins, enzymes, and minerals (Mazhitova et al. 2015). Mare’s milk is not a new fad, but it is coming back into fashion. In Europe, Belgium, France, the Norway and Netherlands, mare’s milk is a staple food. Koumiss is the main product of mare milk, in Mongolia, Kazakhstan and Kyrgyzstan the tradition is to ferment dairy beverage called kumis, or airag in Mongolia (Akuzawa et al. 2011). A small proportion of people from ancient times have been producing koumiss, and appreciated it for its nutritional and health-promoting properties (Mazhitova et al. 2015).

Koumiss healthcare value can be summarized as the following aspects. In Russia and Mongolia set up special ‘koumiss medical center’ (Li et al. 2006), koumiss could combine with traditional medicine to assist treatment, such as cardiovascular diseases, alimentary system diseases, nervous system disease, tuberculosis, anaemia, gynaecological and glycuresis (Wu 1986). Favourable influences on the alimentary canal’s activity, the nervous system, cardiovascular diseases, Phthisis, endocrine glands and the immune system have been reported (Jaqielski 1872; Mccaskie 1878; Stoianova et al. 1988; Fedechko et al. 1995). An increasing interest has been shown for the manufacture of koumiss at an industrial level in human nutrition and health.

Recent advances in next-generation sequencing (NGS) technology have provided researchers with unprecedented tools to increase the discovery of genetic variation associated with traits of economic importance in animals (Petersen et al. 2013; Crisà et al. 2016; Stefaniuk et al. 2016; Purfield et al. 2017). But this method was rarely reported to be used to study the milk traits of horse. To date, in horses, many genes have been investigated and significantly associated with important phenotypic traits, such as coat colour variations (MC1R, PMEL) (Andersson et al. 2013; Corso et al. 2017), Morphometric Traits (WWOX, ANKRD1, ECA3, ZFAT) (Meira et al. 2014; Abri et al. 2017; Grilz-Seger et al. 2019; Gurgul et al. 2019), reproduction traits (STK31,TSPY, AEG1, CRISP1) (Giese et al. 2002; Sabeur et al. 2008). However, few genes were related to milk traits of horse. Several of the genes that we identified here confirm previously documented selection signatures in Kazakh horses, such as NUMB, ADCY8, CA8, LGALS2, SLC25A30 (Liu et al. 2018).

The aim of this study was to investigate the polymorphism of 13 SNPs of 5 genes on milk production using genotyping methods in Kazakh horse reared in Fuyun of Xinjiang. Furthermore, association study was performed between milk traits of horse and polymorphic SNPs. The ultimate purpose was to find and describe polymorphisms that could prove useful for the breeding of future specialized breed in Kazakh horses.

Materials and methods

Animals

Sixty Kazakh horse (5-7rd lactations) reared under grazing conditions (Fuyun: 45°–48°03′N88°10′-90°31′E).

Collecting data

We sampled milk from two months to five months of lactation, we collected every 10 days and took four time points at 12 am, 14, 16, and 18 pm, weighed and recorded the milk yield of each Kazakh horse at each time point. The mare’s milk sampling step was the same during the whole experiment. The mares and foals were separated before milked, they could to see, smell each other, but the foal was not able to suckle. The 2 teats of the mare were hand milked as completely as possible. We used four time points to calculated daily milk production, and average milk of day was calculated by 12 daily milk yield records for four months.

Selection of SNP

A total of 5 genes have been chosen based on the results of prior signatures of selection (Liu et al. 2018), and 13 single nucleotide polymorphisms (SNPs) of these genes were collected from the database of NCBI.

Genomic DNA extraction and genotyping

Genomic DNA was isolated using a DNA kit from GeneOnBioTech. Kompetitive Allele Specific PCR (KASPTM, LGC Genomics, Teddington, Middlesex, UK) genotyping was used for the bi-allelic discrimination of the selected 13 SNPs (Table 1). Genotype data for each animal were exported for the statistical analysis.

Table 1. Selected SNPs used in the study for genotyping the Kazakh horse.

SNP	Gene name	Allele substitution	Position	Chromosome
rs1142624455	NUMB	G/A	18986402	24
rs1135897257^a	NUMB	G/C	18986342	24
rs1147862644^a	NUMB	A/G	18986408	24
rs1142665928	ADCY8	T/C	71925075	9
rs1145707607^a	ADCY8	C/G	71918156	9
rs1138137602^a	ADCY8	T/C	71988831	9
rs1145271262^a	ADCY8	A/T	72101552	9
rs1139280228	LGALS2	G/A	34838131	28
rs1145501348	LGALS2	C/T	34838364	28
rs1142041349	SLC25A30	A/C	25148894	17
rs782901229^a	SLC25A30	C/G	25143775	17
rs1137896578^a	SLC25A30	T/C	25146198	17
rs1136347938	CA8	A/G	24074470	9

Note: ^anon-polymorphic loci.

Primer design

Software of prime 5 was used to design primer of 5 genes. Primer sequences are given in Table 2.

Table 2. Primer information of five genes.

Gene	Position	Primer sequence
NUMB	18986402	F1: GAAGGTCGGAGTCAACGGATTATTCCCTTGGAAAGGCGG
		F2: GAAGGTGACCAAGTTCATGCTATTCCCTTGGAAAGGCGA
		R1: AGTTCTCCAGCCTCCTCCAC
NUMB	18986342	F1: GAAGGTCGGAGTCAACGGATTAAAGGCTGGCTGCAGGGG
		F2: GAAGGTGACCAAGTTCATGCTAAAGGCTGGCTGCAGGGC
		R1: GAAGGTGACCAAGTTCATGCTAAAGGCTGGCTGCAGGGC
NUMB	18986408	F1: GAAGGTCGGAGTCAACGGATTTTGGAAAGGCGGTGCTGA
		F2: GAAGGTGACCAAGTTCATGCTTTGGAAAGGCGGTGCTGG
		R1: GCCAGTTCTCCAGCCTCCTC
ADCY8	71925075	F1: GAAGGTCGGAGTCAACGGATTGCAGAAAGAGACCTGCATAGAT
		F2: GAAGGTGACCAAGTTCATGCTGCAGAAAGAGACCTGCATAGAC
		R1: GATCATGATCGCCATCTACG
ADCY8	71918156	F1: GAAGGTCGGAGTCAACGGATTCCTCCTTGGTCCCCAGC
		F2: GAAGGTGACCAAGTTCATGCTCCTCCTTGGTCCCCAGG
		R1: GACATCCATAGAGACGCTTTGTC
		F1: GAAGGTCGGAGTCAACGGATTGCTGCTTAATTAAATAGGTCTCTATATT
ADCY8	71988831	F2: GAAGGTGACCAAGTTCATGCTGCTGCTTAATTAAATAGGTCTCTATATC
		R1: GAGGGTCACGGGAAAGAGAG
		F1: GAAGGTCGGAGTCAACGGATTGACCGCCAGCCGGGGGA
ADCY8	72101552	F2: GAAGGTGACCAAGTTCATGCTGACCGCCAGCCGGGGGT
		R1: TGGGCACCTCGCTGCTGCAG
LGALS2	34838131	F1: GAAGGTCGGAGTCAACGGATTTCGCTCTCAAAGGTCACGG
		F2: GAAGGTGACCAAGTTCATGCTTCGCTCTCAAAGGTCACGA
		R1: TCCTGCCTCCCTGACACG
LGALS2	34838364	F1: GAAGGTCGGAGTCAACGGATTCCAGCTGTCGCCATCCC
		F2: GAAGGTGACCAAGTTCATGCTCCCAGCTGTCGCCATCCT
		R1: CAACCCACGCTTCTGTGAATC
SLC25A30	25148894	F1: GAAGGTCGGAGTCAACGGATTCCTTCTGGGCGTTCGACAA
		F2: GAAGGTGACCAAGTTCATGCTCCTTCTGGGCGTTCGACAC
		R1: GCACCATCAAGATAGGCACTTAC
SLC25A30	25143775	F1: GAAGGTCGGAGTCAACGGATTGGGTTTGAGGCCAGCGC
		F2: GAAGGTGACCAAGTTCATGCTGGGTTTGAGGCCAGCGG
		R1: ATGGTATCTTCTTCATAGCTCAAGC
SLC25A30	25146198	F1: GAAGGTCGGAGTCAACGGATTCCCTCTTGCTGGTAAATGTTAAT
		F2: GAAGGTGACCAAGTTCATGCTCCCTCTTGCTGGTAAATGTTAAC
		R1: CAAAGCAGCACCATTCAAGG
CA8	24074470	F1: GAAGGTCGGAGTCAACGGATTTAGAATTTATTTTCTTTTTTCAGTTCTA
		F2: GAAGGTGACCAAGTTCATGCTAGAATTTATTTTCTTTTTTCAGTTCTG
		R1: TAACTTCATACAGTTCAAATTCGTG

PCR amplification

PCR amplification was conducted in a 5 μLvolume containing 2.5 μL of 2×Master Mix, primer 0.07 μl, genomic DNA 30–50 μg, ddH₂O supplement of 5 μL. The PCR conditions were as follows: an initial step at 95°C (one cycle for 10 min), 10 cycles for 20 s at 95°C, 60 s at 61–55°C temperature for each primer pair, and 26 cycles for 20 s at 95°C, 60 s at 55°C.

Data analysis

The genotyping results received from LGC Genomics were analysed with Douglas software from LGC Genomics (www.lgcgroup.com). Further, a genetic association study was conducted on polymorphic SNPs. We performed factorial ANOVA (Bonferroni correction) and T-test using the software SPSS19.0. The chi-squared test (χ²) was used to determine whether the populations were in Hardy-Weinberg equilibrium (HWE). HWE analysis was performed for setting the genetic association studies.

Ethics of experimentation

All experimental procedures involving animals were approved (animal protocol numbers: 2018001）by the Animal Care and Use Committee of Xinjiang Agricultural University, Urumqi, Xinjiang, China.

Results

Result of KASP

A total of 6 SNPs among the selected 13 SNPs were polymorphic across the Kazakh horse and further used for the association study (Figure S1–S5). In these figures: black represent none sample, red represents homozygous, purple represents heterozygous, and blue represents mutant homozygous (Figures 1–5).

Figure 1. Genotyping results of NUMB gene (g.18986402 A>G). Note: Black represent none sample, red represents homozygous, purple represents heterozygous, and blue represents mutant homozygous. The same below.

Figure 2. Genotyping results of ADCY8 gene (g.71925075C>T).

Figure 3. Genotyping results of LGALS2 gene, a was represented site of g.34838131A>G; b was represented site of g.34838364T>C.

Figure 4. Genotyping results of SLC25A30 gene (g.25148894C>A).

Figure 5. Genotyping results of CA8 gene (g.24074470G>A).

Polymorphism analysis

The different genotype frequencies are shown in Table 3. Fisher exact test of SPSS 19.0 software was used to calculation χ² and P-value. The g.71925075 C > T locus (rs1142665928) was not in HWE, the other five SNPs were in HWE.

Table 3. List of polymorphic SNPs and genotypes frequencies in Kazakh horse breeds.

SNP	Allele substitution	Genotype	Genotype frequencies	χ^2	P
rs1142624455	A > G	GG	0.966	0.00	0.691
		GA	0.034
		AA	0
rs1142665928	C > T	TT	0.74	10.619	0.005
		TC	0.1
		CC	0.16
rs1139280228	A > G	GG	0.80	2.77	0.096
		GA	0
		AA	0.20
rs1145501348	T > C	CC	0.875	1.047	1.00
		CT	0.107
		TT	0.018
rs1142041349	C > A	AA	0.862	2.259	0.294
		AC	0.103
		CC	0.035
rs1136347938	G > A	AA	0.867	2.258	0.295
		AG	0.100
		GG	0.033

Correlation analysis of genotype and milk yield in Kazakh horse

The two SNPs had significant effects on milk yield in Kazakh horse breed (Table 4 and Figure 6).

Figure 6. A:association analysis of g.18986402G>A loci of NUMB and daily milk yield in Kazakh horse; B:association analysis of g.24074470A>G loci of CA8 and daily milk yield in Kazakh horse.

Table 4. List of SNPs found to be associated with milk yield in Kazakh horse breeds.

SNP	Gene	Genotype	Milk yield of daily
rs1142624455	NUMB	GG (58)	9.80 ± 3.28
		GA (2)	3.85 ± 0.98**(P = 0.01)
rs1136347938	CA8	AA (52)	9.47 ± 3.28^b
		AG (6)	10.73 ± 2.49^b
		GG (2)	16.08 ± 0.76^a

Note: ** indicates a very significant difference. Different lowercase letters show significant difference (P<0.05).

Discussion

The KASP genotyping system has low-labour and economic advantages over other genotyping assays (Landoulsi et al. 2017). The results of the current study showed that the horse NUMB g.18986402 A > G and CA8 g.24074470G > A might play an important role in affecting milk yield traits and could be linked to major genes that affect milk yield traits in horse. Current findings revealed the significant association and the effects of those loci and the investigated milk traits.

The NUMB gene was first discovered in Drosophila (Uemura et al. 1989). NUMB is a member of the closely conserved protein family, it plays key role in brain development, nervous system development. The cell fate determinant Numb influences developmental decisions by antagonizing the Notch signalling pathway (Mcgill et al. 2003; Rasin et al. 2007). Mammalian Numb expression promotes the ubiquitination of membrane tethered Notch1 and the degradation of the intracellular domain following receptor activation, and Notch signaling plays an important role in cellular differentiation, proliferation, and apoptotic events at all stages of development, functioning as an essential communication mechanism to direct cell fate selection of neighbouring cells (Artavanis-Tsakonas et al. 1999). If the Notch signaling pathway is out of regulation, the mammary epithelium will always be in a hyperplastic state, which will affect the lactation process. In China, NUMB gene was found to have an important effect on mammary gland of mice (Zhang 2016). The expression of this gene reached the highest level in late pregnancy; it regulates the lactation process by regulating the Notch signaling pathway, indicating that NUMB gene may play an important regulatory role in lactation (Zhang 2016).

In this study, mutation in the SNP g.24074470A > G of NUMB gene is associated with a decrease in milk yield of Kazakh horse. The reason may be that the expression of NUMB gene inhibits the Notch signaling pathway, thereby hindering the lactation process.

CA8 gene was found to have wide range effect on a variety of disease such as Novel Syndrome Characterized (Türkmen et al. 2009), femoral bone mineral density (Mori et al. 2009), and thermal antinociception (Fu et al. 2017), however, no studies on animals were reported. This gene product, carbonic anhydrase-related protein VIII (CARP VIII), is predominantly present in cerebellar Purkinje cells, where it interacts with the inositol 1,4,5-trisphosphate receptor type 1, a calcium channel (Aspatwar et al. 2013). Hou studied the differential gene of different lactation stage in cow, and CA8 gene was found in the lactation stage of dairy cow (Hou 2009). The CA8 gene may be involved in the regulation of aerobic glycolysis and increase cell viability. Therefore, so it is speculated that the product of CA8 gene promotes the absorption of milk components by increasing the activity of mammary epithelial cells. In this study, mutation in the SNP g.24074470A > G of CA8 gene has three genotypes of GG, AA and AG in the Kazakh population. Furthermore, milk yield of GG genotype was significantly higher than for the other two genotypes. So, this mutation may affect the ability of lactation in Kazakh horses.

Conclusions

In this study, 13 SNPs of five genes were genotyped using KASP genotyping assays; however, only 6 SNPs were polymorphic. We found evidence of significant associations between two intragenic SNPs and milk yield traits in Kazakh horses. These two SNPs are located within two different genes not yet reported before as candidates for horse milk yield. These two SNPs within the genes NUMB and CA8 turned out as promising molecular markers for daily milk yield in Kazakh horses.

Acknowledgements

The authors thank Yuehui Ma and Lin Jiang.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous (grant number 2019D01B14); the National Natural Science Foundation of China (grant number31560620).