Overview of SNPs Associated with Trans Fat Content in Cow’s Milk

Abstract

Lipids consumed with milk derivatives are one of the main parts of the human diet. Trans fatty acids in milk are causing a debate about their impact on the incidence of cardiovascular disease, pathological abnormalities, and cancer. The fatty acid profile of milk is influenced by a large number of different factors, one of which is genetic. The development of genetic studies, including Genome-Wide Association Studies (GWAS), may help define genomic regions associated with fatty acid content in milk, including trans fatty acids. This article provides an overview of international studies on the identification of genomic regions and SNPs associated with the trans fatty acids in cow’s milk. The results are based on research of cattle such as Norwegian Red cattle, Holstein, Jersey, and Brown Swiss. The presented review shows that 68 SNPs were localized on chromosomes 1, 2, 4–6, 8–10, 12, 14–20, 22–25, and 27–29. Further research in this direction will provide new information that will serve as an impetus for the creation of modern breeding technologies and increase the performance of the manufacture of high-quality dairy products. The search for genetic markers associated with the content of TFA in milk is a promising direction in agricultural science and will allow more complete breeding work with cattle.

1. Introduction

In recent years, the problem of rationing the maintenance of non-saturated fatty acids trans-isomers (TFA) in animal origin products has remained relevant all over the world, while Russian legislation practically does not regulate the content of TFA, which is hazardous to human health.

TFAs are spatial isomers of naturally occurring unsaturated fatty acids (FA) found in animal fats. They are formed in small quantities in the rumen of ruminants during the digestion of plant materials rich in unsaturated fatty acids, and they enter animal fats, including milk fat, and then transfer into milk processing products with a high fat content, such as sour cream and butter. The proportion of cow’s milk, cheese, and butter TFAs ranges from 3.2% to 6.2% of the total amount of fatty acids. Goat and sheep dairy products contain from 2.7% to 7.1% of these compounds [1,2,3].

Feeding lipids in the rumen undergo two important microbial transformations: lipolysis and biohydrogenation (decomposition of FA double-carbon bonds under the action of metabolic hydrogen). As a result of the rapid and intense biohydrogenation of double bonds of unsaturated fatty acids released during synthesis in the rumen, saturated fatty acids are formed. The initial stage in this process is an isomerization reaction catalyzed by the isomerase, that converts the double bond from the cis- to the trans-isomer.

Milk fat contains TFAs, represented mostly by C18:1 isomers. The most common among them is vaccenic acid (18:1t11), which accounts for more than 60% of the total TFAs. Vaccenic acid can be transformed to rumenic acid by the action of the stearol coenzyme A-desaturase. Isomers C16:1 and C18:2 make up about 15% of the total TFAs. Milk obtained in summer contains twice as many trans fatty acids as winter milk [4,5].

The effects of TFAs on the body and human health are varied and depend on many factors, such as the origin, structure, composition, and type. Despite extensive discussion of the adverse health effects of industrial TFAs, the impact of natural TFAs on health is still doubtful [6].

According to a number of foreign researchers, TFA can block the mechanisms of gene expression associated with lipid compounds at the level of metabolism [7,8,9].

An anticancer effect of TFAs has been established, which showed an inhibition of tumor development and stopped the formation of metastases due to impaired signaling, suppression of DNA synthesis, and the process of programmed death of tumor cells. In addition, there is an increased synthesis of cytokines and immunoglobulins A (IgA) and M (IgM), and the production of inflammation signals is also reduced. TFAs contribute to a reduction in the proportion of fat reserves while maintaining muscle mass through their influence on hormones that regulate lipid metabolism [10,11,12,13].

Milk TFAs are natural and are able to provide many health benefits, including boosting immune function, reducing excess fat, and completely removing or slowing down the process of neoplasm of tumors of various types of cancer [14,15]. This information warrants further research as it suggests that naturally occurring TFAs are metabolized very differently from industrially produced ones [15].

The composition of fatty acids in cow’s milk depends on many factors, but the quality of the diet and [16] habitat play the greatest role. However, data obtained on lactating dairy cows showed that even in herds in which all cows are kept and fed similarly, there is a three-fold distinction in the concentration of CLA in milk fat [17]. Differences in milk fatty acid content between various breeds have been proven by different groups of scientists. Maurice-Van Eijndhoven et al. [18], when comparing four breeds bred in the Netherlands, revealed a clear and significant difference in the concentration of short- and medium-chain acids and linoleic acid (C18:2c9,t11), and in the percentage of fat in milk. Carroll et al. [14] and Ramalho et al. [19] observed similar differences between breeds in milk fat composition and concentration. The advent of technologies for relatively inexpensive whole-genome genotyping of cows makes it possible to study genetic changes in cattle in order to study the genetic architecture of trans fatty acids in cow’s milk.

This article presents a review of studies looking for genomic regions and single-nucleotide polymorphisms (SNPs) associated with trans fatty acids in cow’s milk, collecting information on their effects on various traits in cattle.

2. SNPs Associated with Trans Fat Content in Cow’s Milk

The Cattle Quantitative Trait Locus (QTL) Database (Cattle QTLdb) contains data on four large studies on the search for SNPs related to the composition and content of fatty acids in cow’s milk, which were performed on Norwegian Red cattle [20], Holstein [21,22], Jersey [23], and Brown Swiss cattle [23,24,25],

Table 1. SNPs associated with trans fat content in cow’s milk presented in the Cattle QTLdb (genome version ARS-UCD1.2).

No.	Traits	SNP	Breed	BTA	Position	Consequence	Candidate Gene	Allele	References
1	C18:1t11	rs41569243	Norwegian Red cattle	1	45,564,535	intergenic variant		T/A	[20]
2	C18:1t11	rs29019625	Norwegian Red cattle	1	142,894,021	intergenic variant	SLC37A1	A/G	[20]
3	C18:2t11,c15	rs43232419	Brown Swiss	1	44,571,298	intron variant	TBC1D23	T/G	[25]
4	C18:1t11	rs110831311	Brown Swiss	1	147,265,560	intergenic variant		A/G	[25]
5	C18:1t16, C18:1t6–8	rs43705173	Brown Swiss	2	79,518,124	downstream gene variant	STAT1	G/A	[24]
6	C18:1t16	rs43706906	Brown Swiss	2	79,553,228	intron variant	STAT1	G/C	[24]
7	C18:2c9,t11	rs110614098	Brown Swiss	2	129,910,992	intron variant	EPHB2	G/A	[25]
8	C18:1t11	rs110847444	Brown Swiss	4	88,230,965	intergenic variant		T/C	[23]
9	C18:2c9,t11, C18:2t11,c15	rs29004170	Brown Swiss	4	92,436,328	upstream gene variant	LEP	G/C	[24]
10	MTCFAR	rs41590819	Norwegian Red cattle	5	75,374,050	downstream gene variant	CSF2RB	C/G	[20]
11	C18:1t10	rs41623140	Norwegian Red cattle	5	92,068,616	intergenic variant		T/G	[20]
12	C18:1t10	rs43703011	Brown Swiss	6	85,451,298	missense variant	CSN2	T/G	[24]
13	C18:2c9,t11	rs43703013	Brown Swiss	6	85,451,132	missense variant	CSN2	C/G	[24]
14	C18:1t9	rs29024681	Norwegian Red cattle	6	85,662,337	upstream/downstream gene variant	CSN3	G/A	[20]
15	C18:1t9	rs29024683	Norwegian Red cattle	6	85,662,408	upstream/downstream gene variant	CSN3	A/G	[20]
16	C18:1t9	rs29024684	Norwegian Red cattle	6	85,662,466	upstream/downstream gene variant	CSN3	C/A	[20]
17	C18:1t9	rs29024685	Norwegian Red cattle	6	85,662,516	upstream/downstream gene variant	CSN3	A/G	[20]
18	C18:1t9	rs41587868	Norwegian Red cattle	6	72,916,033	intergenic variant		G/A	[20]
19	C18:1t9	rs41653769	Norwegian Red cattle	6	70,588,722	intron variant	KDR	A/G	[20]
20	C18:1t16	rs42960052	Brown Swiss	6	20,546,001	intergenic variant		A/G	[25]
21	C18:1t6–8	rs41651324	Brown Swiss	6	76938995	intergenic variant		C/T	[25]
22	C18:2t11,c15	rs41567758	Brown Swiss	6	69,216,812	intergenic variant		A/C	[23]
23	C18:1t16	rs41573488	Brown Swiss	8	3,790,608	intergenic variant		T/C	[25]
24	C18:1t10	rs41593428	Norwegian Red cattle	9	65,223,783	intron variant	CEP162	C/G	[20]
25	C18:1t10	rs41611219	Norwegian Red cattle	9	23,244,267	intron variant	ME1	C/A	[20]
26	TFA	rs108973184	Brown Swiss	9	102,953,472	intergenic variant		T/C	[25]
27	C18:1t10	rs41568929	Norwegian Red cattle	10	44,720,992	intron variant	GNG2	T/C	[18]
28	C18:1t11	rs41575963	Brown Swiss	12	26,784,974	intergenic variant		A/C	[21]
29	MTRANSFA	rs41662646	Norwegian Red cattle	12	77,525,632	intergenic variant		G/A	[20]
30	C18:1t6–8	rs109738802	Brown Swiss	12	78,577,324	intergenic variant		C/T	[25]
31	C18:1c9,t11	rs109326954	Jersey, Holstein	14	611,020	missense variant	DGAT1	C/A	[22]
32	C18:1t9	rs110445169	Brown swiss	14	20,267,955	intron variant	AGPAT6	T/C	[24]
33	C16:1t9, C18:1t4	rs110757796	Brown swiss	14	44,677,959	missense variant	FABP4	T/C	[24]
34	C18:1T4	rs110642420	Brown Swiss	14	5,880,036	intergenic variant		C/T	[25]
35	C18:1t11	rs42357017	Brown Swiss	15	20,213,507	intron variant	ZC3H12C	C/T	[23]
36	MTCFAR	rs29012314	Norwegian Red cattle	15	28,602,093	upstream gene variant	TMPRSS13	C/T	[20]
37	MTCFAR	rs29019684	Norwegian Red cattle	15	57,539,478	intergenic variant		G/A	[20]
38	C18:1t10	rs41655008	Brown Swiss	15	69,449,535	intergenic variant		C/T	[25]
39	C18:1t11	rs41578757	Brown Swiss	16	26,461,264	intron variant	DISP1	T/A	[23]
40	C18:1t11	rs109178989	Brown Swiss	17	25,016,349	intergenic variant		A/G	[23]
41	C18:1T6–8	rs110937773	Brown Swiss	17	34,849,206	intron variant	FGF2	T/C	[24]
42	MTCFAR	rs41633197	Norwegian Red cattle	17	46,687,151	intron variant	RIMBP2	A/G	[20]
43	MTCFAR	rs41637627	Norwegian Red cattle	17	44,150,664	upstream gene variant	ZNF891	G/T	[20]
44	MTCFAR	rs41660449	Norwegian Red cattle	17	51,342,347	intron variant	NCOR2	T/G	[20]
45	C18:1T4	rs41596865	Brown Swiss	17	59,367,351	intergenic variant		C/T	[25]
46	C18:1T11	rs41611446	Brown Swiss	17	20,279,742	intergenic variant		G/A	[25]
47	C18:1t16	rs43710288	Brown Swiss	18	19,117,794	missense variant	NOD2	A/T	[24]
48	C18:1T4	rs109578101	Brown Swiss	19	42,415,682	3 prime UTR variant	STAT5A	C/T	[24]
49	C18:3	rs137182814	Brown Swiss	19	42,335,251	splice region variant, synonymous variant	STAT5A	G/C	[24]
50	TFA, С18:1t11	rs110773010	Brown Swiss	19	10,076,852	intron variant	TRIM37	G/A	[25]
51	C18:1t4	rs41255713	Brown Swiss	19	15,905,377	upstream gene variant	CCL2	G/A	[24]
52	C18:1t10	rs109136815	Brown swiss	20	31,870,046	synonymous variant	GHR	A/G	[24]
53	C18:3c9,t11,c15	rs109794490	Brown Swiss	22	21,455,728	intergenic variant		C/T	[25]
54	C18:2t9,t12	rs211032652	Brown Swiss	23	35,333,764	synonymous variant	PRL	C/T	[24]
55	C18:1t10	rs41589759	Norwegian Red cattle	23	41,632,536	intron variant	JARID2	G/A	[20]
56	MTCFAR	rs41642031	Norwegian Red cattle	23	23,196,063	intergenic variant		G/C	[20]
57	C18:1t16	rs41641235	Brown Swiss	23	22,032,364	intergenic variant		T/G	[25]
58	C18:1t9	rs29024014	Norwegian Red cattle	24	23,015,448	intron variant	NOL4	A/G	[20]
59	C18:1t9	rs41644943	Norwegian Red cattle	24	6,108,284	intergenic variant		G/A	[20]
60	MTRANSFA	rs41567529	Norwegian Red cattle	25	34,827,662	intron variant	CUX1	T/C	[20]
61	C18:2c9,t11	rs41622946	Brown Swiss	25	32,138,950	intergenic variant		T/C	[25]
62	C18:3c9,t11,c15	rs41624917	Brown Swiss	26	15,524,965	intron variant	PLCE1	G/A	[24]
63	C16:1t9	rs41650170	Brown Swiss	27	13,628,042	intron variant	TENM3	G/A	[25]
64	C18:2c9,t11	rs3423094014	Brown Swiss	28	4,467,528	intron variant	DISC1	G/T	[25]
65	C18:2t6	rs42187261	Holstein	29	40,247,432	synonymous variant	FADS1	G/A	[21]
66	C18:2t10, C18:2t6	rs210169303	Holstein	29	40,386,173	3 prime UTR variant	FADS2	G/A	[21]
67	C14:1t	rs109772589	Holstein	29	40,387,345	3 prime UTR variant	FADS2	G/A	[21]
68	C18:1t11	rs42176310	Brown Swiss	29	28,440,651	intron variant	CCDC15	A/C	[23]

Grey rows—SNPs identified by using GWAS.

. Such SNPs were localized on chromosomes 1, 2, 4–6, 8–10, 12, 14–20, 22–25, and 27–29.

Olsen et al. [20] performed a GWAS of the fat composition of cow’s milk, including C18:1t9, C18:1t10, C18:1t11, and FA percentage, with trans bonds in Norwegian red cows according to FTIR data. They found that rs29019625, located on BTA1, was strongly associated with C18:1t11, while other identified SNPs were less expressed. Ibeagha-Awemu et al. [21] found an association between FADS1 and FADS2 gene polymorphisms in Holstein cattle with the levels of trans fatty acids in milk: rs210169303 with C18:2t10 and C18:2t6 (p ≤ 0.05), rs42187261 with C18:2t6 (p ≤ 0.05), and rs109772589 with C14:1m (p ≤ 0.05). An association of the KK genotype for DGAT1_K232A with the amount of C18:1t11 (−0.082, p ≤ 0.05) was found in the Danish Jersey and Holstein breeds [22]. Strillacci et al. [23] identified SNPs rs42176310, rs110847444, rs41575963, rs42357017, rs41578757, and rs109178989 as significantly associated with C18:1t11 content in the Brown Swiss cattle population. Pegolo et al. [24,25], in 2 experiments on Brown Swiss cattle, first studied the associations of 38 different SNPs belonging to 30 genes with 57 fatty acid traits in milk. They revealed a relationship between rs110757796 and C16:1t9 content (effect = 0.006), between rs41255713, rs110757796, and rs109578101 (effect = 0.003, 0.004, and −0.005, respectively), between rs43705173, rs110937773, and C16:1t6–8 (effect = −0.006 and 0.005, respectively), between rs110445169 and C18:1t9 (effect = −0.005), and rs43703011 and rs109136815 were associated with C18:1t10 (effect = −0.014 and 0.11, respectively). An association was found between rs43705173, rs43706906, and rs43710288 and the content of C18:1t16 (effect = −0.008, −0.006, and −0.006, respectively). Additionally, three SNPs, rs29004170, rs43703013, and rs110684599, were associated with rumenic acid content (C18:2c9,t11) (effect = −0.020, −0.022, and 0.036, respectively). SNP rs29004170 was associated with C18:2t11,c15 (effect = −0.008), and rs211032652 with C18:2t9,t12 (effect = −0.014) content. Two SNPs showed an association with C18:3c9,t11,c15: rs41624917 and rs137182814 (effect = 0.003 and −0.043, respectively). In their next experiment, they performed a GWAS with 65 milk fatty acid traits in Brown Swiss cattle and found a total of 19 SNPs significantly associated with TFA content in cow’s milk: C18:1t6–8, C16:1t9, C18:1t11, C18:1t16, C18:1t4, C18:1t10, C18:1t4, C18:1t11, C18:1t16, C18:2c9,t11, C18:2t11,c15, and C18:3c9,t11,c15.

3. Candidate Genes for Trans Fat Content in Cow’s Milk

3.1. Candidate Genes with Predicted Functions

Studies to identify an association of the trans fatty acid content in cow’s milk with SNP were based on two approaches. In the first approach, an analysis of the polymorphism of genes involved in the synthesis of fatty acids, proteins, and in the functioning of the immune system, or located in genomic regions associated with quality and technological traits of milk, was performed [21,22,24]. In one case, 5 potentially functional SNPs (1 synonymous, 1 non-synonymous, 1 splicing site, and 2 3′UTR mutations) and 2 randomly selected intron mutations in the FADS1 and FADS2 genes were genotyped in 450 samples, and an association analysis was performed, including the content of fatty acids in milk [21]. In the second case, the effect of the DGAT_K232A mutation on the content of fatty acids in the milk of Holstein and Jersey cows was evaluated [22]. In the third case, the association of 51 SNPs, selected using functional and positional approaches, with 47 fatty acids, 9 fatty acid groups, and 5 Δ⁹-desaturation indices, was studied in milk samples from Brown Swiss cows [24].

STAT1 (signal transducer and activator of transcription 1-alpha/beta) is a member of the transcription factor family of signal transducers and transcription activators. STAT1 is involved in the upregulation of genes by interferon signals. Significant differences were found between estimates of the breeding value of milk productivity traits in cattle with different genotypes for the STAT1 gene [26,27].

Leptin (LEP) occupies the most important role in body weight control and energy balance. The association of leptin gene polymorphism with the increase in milk yield and the quality of milk during three lactations of Holstein cows was revealed. Animals with the TT genotype for the LEP gene (c.73T > C) showed the best result for three lactations during the experiments [28]. Polymorphism of the LEP-Sau3AI was associated with the somatic cell count (SCC) (p  ≤ 0.01), electrical conductivity (EC) (p  ≤  0.01), and pH (p ≤ 0.05) in Holstein cow milk. The BB genotype led to a higher SCC, EC, and pH compared to other genotypes [29]. Maletić et al. [30] studied the total somatic cell count (SCC), chemical composition of milk (fat, protein, lactose, total solids, and percent of solids-not-fat), and the evaluation of freezing point depression (FPD) in milk of Busha cows with different genotypes of LEP-Sau3AI. Animals with the AA genotype had an average SNF content (8.74%, p = 0.021) in milk, significantly lower compared to those with genotype AB (9.28%), while cows with genotype AA had significantly higher average FPD values (−0.54 °C, p = 0.004) than those with the AB genotype (−0.58 °C). The genotype of the LEP gene was significant for five individual saturated and unsaturated FAs and the branched-chain fatty acids (BCFA) group [31]. Haruna et al. [32] investigated the three variations (A3, B3, and C3) in Holstein Friesian × Jersey (HF × J) dairy cows in New Zealand and studied their effects on the milk fatty acid composition. The A3 variant was associated with a reduced level of C15:1, C18:1t11, C18:2t9,c12, C22:0, and C24:0, but with elevated levels of C12:1 and C13:0 iso (p ≤ 0.05). The B3 variant was associated with decreased levels of C6:0, C8:0, C11:0, C13:0, and C20:0, but with elevated C17:0 iso and C24:0 levels (p ≤ 0.05). The C3 variant was associated with reduced C17:0 iso levels but elevated C20:0 levels (p ≤ 0.05). The A3B3 genotype was found to be associated with reduced levels of C22:0 and C24:0 but elevated C8:0, C10:0, C11:0, C13:0, C15:0, and grouped medium-chain fatty acid (MCFA) levels (p ≤ 0.05). Genotype A3C3 was associated with decreased levels of C10:0, C11:0, C13:0, and aggregated MCFA (p ≤ 0.05).

The functions of the genes used in the first approach have been somewhat studied in detail. The surface properties of casein micelles are highly dependent on beta-casein (CSN2), and rs43703011 is associated with A2 milk [33,34]. The statistical analysis showed that polymorphism of the CSN2 gene had a significant effect on the protein content in the milk of the Slovak Holstein cattle. The percentage of fat in milk in cows with the AA genotype is increased compared to the A2A2 genotype. Amalfitano et al. [35] identified the influence of the CSN2 genotypes on the cattle milk protein profile of the Brown Swiss cows.

Diacylglycerol O-acyltransferase 1 (DGAT1) catalyzes triacylglycerol synthesis by using diacylglycerol and fatty acyl CoA as substrates [36]. It is involved in the esterification of exogenous fatty acids to glycerol in the liver, plays an important role in the synthesis of fat for storage, and is expressed in the female mammary glands, where it produces milk fat [36,37]. Polymorphism at position rs109326954 leads to p.A232K substitution, which is associated with protein and fat content in milk [38]. Elzaki et al. [39] found a significant effect of the DNA marker rs109234250 (DGAT1_K232A) on milk yield (p = 7.6 × 10⁻²⁰), fat yield (p = 2.2 × 10⁻¹⁷), protein yield (p = 2.0 × 10⁻¹⁹), and lactose yield (p = 4.0 × 10⁻¹⁸) in crossbred Butana × Holstein cattle. The breeding value for the amounts of milk (in kg) of animals with the AA genotype was significantly (p ≤ 0.0001) higher than in animals with the KK genotype [40]. The polymorphism of the DGAT1 gene in position p.A232K influenced the fatty acid composition: milk from AA cows had a more favorable fatty acid composition due to a lower total saturated fatty acids content and higher levels of oleic acid and total unsaturated fatty acids, a higher ratio of the saturated to unsaturated acids, and a higher atherogenic index [41].

AGPAT6 plays an important role in the process of synthesis of triglycerides (TG) in mammals. For further use in cattle breeding, the AGPAT6 gene is one of the potential candidates due to its ability to regulate the synthesis of milk fat. The SNP g.36,175,805C > T had a significant (p ≤ 0.05) influence on the EBV of fat percentage (EBV-FP) in Karan Fries Breeding Bulls. The KF bulls with the TT genotype had a comparatively lower EBV-FP than the bulls with the CC and CT genotypes. The substitution of the C allele with the T allele led to a decline of 0.0045% in the EBV-FP [42]. Wavenumbers studied by using Fourier transform infrared milk spectra and GWAS in Danish Jersey cows were associated with the AGPAT6 gene, which is involved in fatty acid synthesis in milk [43]. The most significant and favorable associations were observed between rs110454169 and rs109913786 polymorphisms located in the AGPAT6 gene, for fat yield (0.033 kg/day), fat percentage (0.093), and rennet coagulation time (−0.462 min) [44].

FABP4 is responsible for the lipid transport protein in adipocytes, which links long-chain fatty acids and retinoic acid. The gene delivers long-chain fatty acids and retinoic acid to related receptors in the nucleus. Viale et al. [44] revealed a trend (p ≤ 0.10) of the effect of the rs110757796 polymorphism in the FABP4 gene on milk yield, protein yield, and casein yield. After adjusting for the effect of the p.K232A amino acid substitution in diacylglycerol-O-acyltransferase 1 (DGAT1), which is associated with altered levels of many milk fatty acid components, the effect of FABP4 c.328A/G on milk FA levels was generally small. After analyses of five genotypes, AB cows produced more medium–long-chain fatty acids than CC cows (p ≤ 0.05) and more C14:0 acids than AC and AA cows (p ≤ 0.05). Cows with the AC genotype had more C24:0 fatty acids (p ≤ 0.05) than BC cows. Cows with the CC genotype produced more long-chain fatty acids than cows with the BC genotype (p ≤ 0.05). AA and AC cows produced less C22:0 FAs (p ≤ 0.01) than BC cows [45].

Cell survival depends on many factors, such as division, differentiation, and migration. Fibroblast growth factor 2 (FGF2) plays an important role in these functions. Genetic variants of FGF2 have been linked to the productive life, with significant dominance effects and overall dominance, milk fat content, and somatic cell scores [46]. Li et al. [47] have shown that the SNP rs210169303 was linked to the highest 305-day milk yield. Brzáková et al. [48] assessed the effect of SNP11646 in the FGF2 gene on the regressive evidence of the breeding value of sires in terms of reproductive qualities and milk production. The difference in milk production in animals with different genotypes was negligible. Milk production in sires with the AA genotype showed the lowest DRP value. The reproductive qualities of the same bulls were highly rated both in terms of the direct genetic effect (male fertility) and the maternal genetic effect (daughter conception). According to the conception rate of daughters, in some cases, the differences reached the threshold of significance.

The nucleotide-binding oligomerization domain-containing protein 2 (NOD2), also known as Caspase Recruitment Domain 15 (CARD15), pattern recognition receptor (PRR) detects bacterial peptidoglycan fragments and other danger signals and plays an important role in gastrointestinal immunity. It is a cytosolic protein capable of initiating inflammation. The CARD15 SNPs c.3020A > T and c.4500A > C were associated with EBVs for health and production traits in Canadian Holsteins. The SNP c.3020A > T was also associated with EBVs for SCS (p = 0.0097). Hap22 (TC) was associated with increased milk (p ≤ 0.0001) and protein (p = 0.0007) yield EBVs compared to the most frequent haplotype Hap12 (AC). The hap21 (TA) was significantly associated with elevated SCS EBVs (p = 0.0120) [49]. Wang et al. [50] showed that transitions (A→T) at position 114 bp were associated with the somatic cell score (p ≤ 0.01). The G→A at position 1594 bp plays a critical role in increasing 305-day milk yields in Chinese Holstein and Chinese Simmental breeds.

Signal transducer and activator of transcription 5A (STAT5A) performs a dual function: signal transduction and transcription activation, and regulates the expression of milk proteins during lactation. Significant differences between the genotypes of the STAT5A_MslI polymorphism were revealed: cows with the TT genotype produced a milk with a higher content of fat and protein compared to cows with the TC genotype [51]. Significant relationships were found between STAT5A_AvaI genotypes and milk electrical conductivity (p  =  0.007). The greatest EC values were observed in STAT5A-AvaI-TT-genotyped animals [52]. Association testing of SNP12195 (exon 8) and SNP14217 (intron 9) showed that allele G of SNP12195 was associated with a decrease in protein and fat percentages [53].

CC-motif chemokine ligand 2 (CCL2) is a small chemokine that belongs to the CC-type chemokine family and has the ability of chemoattractant activity to recruit monocytes to sites of inflammation. CCL2 induces proliferation of MAC-T cells, a bovine mammary epithelial cell line, and enhances cell cycle progression by increasing the expression of cyclin D1 [54]. Allele C increased the yield of milk and protein. After replacing the allele, the milk yield increased by 248 kg, and the protein yield by 7.4 kg. Several significant associations were identified: CCL2 c.-95T > C with udder depth (p = 0.008), and CCL2 c.1364A > G with milk yield (p = 0.03) and protein yield (p = 0.01) [55].

The growth hormone receptor (GHR) is involved in the regulation of postnatal growth of the body. Polymorphism in the GHR gene showed an association with milk yield traits and composition in Turkish Holstein, Turkish Jersey, and Chinese Holstein cattle [56,57]. The p.Phe279Tyr mutation in the GHR was associated with the protein percentage in the Chinese dairy population (p = 0.0014) [58]. A strong association of the F279Y polymorphism with milk yield, fat, protein, and casein content was confirmed in a population of 1370 dairy cows. The influence of the 279Y allele on the increase in lactose content was shown. Substitution effects of the Tyr allele in the GHR amounted to 320 kg of milk (305 days), 0.02 kg of casein per day, and 0.07 kg lactose yields per day. The Tyr allele was associated with a lower somatic cell score (SCS) (p ≤ 0.05) [59].

Prolactin (PRL) stimulates lactation by affecting the mammary gland. A significant relationship was shown between promoter genotypes (−1043A > G and −402A > G) and milk production characteristics in Chinese Holsteins [60]. A significant difference was also found between different genotypes of the PRL gene at position A103G in the average percentage of fat (p ≤ 0.05) [61]. A meta-analysis of various published studies of the relationship between PRL_Rsa I polymorphism and milk production showed that the overall effect of the gene on milk production is 0.533, and cows with the genotype AA have higher productivity than cows with the BB genotype (p ≤ 0.01), however this applies to animals of the non-Holstein breed [62]. According to the results of the analysis, in cattle with the AB genotype compared to BB (SMD = 0.289, 95% CI 0.005, 0.573), a statistically significantly higher protein yield was revealed compared to other genotypes [63].

PLCE1 encodes the phospholipase enzyme, which catalyzes the hydrolysis of phosphatidylinositol-4,5-bisphosphate to form two second messengers: inositol-1,4,5-triphosphate (IP3) and diacylglycerol (DAG). These second messengers regulate various processes that affect cell growth, differentiation, and gene expression. A GWAS for 22 milk fatty acids in 784 Chinese Holstein cows showed associations of the ARS-BFGL-NGS-110475 in gene PLCE1 with monounsaturated and polyunsaturated fatty acid traits (MUFA and PUFA) and indices of fatty acid traits [64], BovineHD2600004009 with milk yield by multibreed genome-wide association [65], rs42816577 with subcutaneous fat deposition traits in Holstein cattle [66], and rs41624917 with fat percentage [44].

Fatty acid desaturase 1 (FADS1) is involved in lipid metabolism and polyunsaturated fatty acid biosynthesis. Beak et al. [67] investigated the rs42187261 polymorphism and found its association with low-concentration C20:4 n-6 (p = 0.044) in Hanwoo beef. FADS1 is a potential genetic marker for indices of fatty acid traits [64].

Acyl-CoA 6-desaturase (FADS2) is involved in the biosynthesis of highly unsaturated fatty acids (HUFA). It can desaturate (11E)-octadecenoate (trans-vaccenoate, a metabolite in the biohydrogenation pathway of LA and the predominant trans fatty acid in cow milk) at carbon 6, generating (6Z,11E)-octadecadienoate. FADS2 is interesting as a candidate gene for selection to increase milk production and improve resistance against mastitis [47]. The SNP FADS2_c.1571G > A is a potential genetic marker in the breeding of cattle to elevate beneficial fatty acid content in milk [68].

3.2. Candidate Genes and Genomic Regions Identified by GWAS

It is interesting to consider the functions and participation in various biological processes of genes in which mutations were identified by the results of GWAS. GWAS was used as the second approach [20,23,25].

Of the 49 mutations identified by GWAS [20,23,25], 25 were localized in intergenic regions, and most of the mutations within the genes were localized in the intron part (Figure 1). Information about genes in which SNPs have been identified by GWAS, the proteins they encode, and their functions according to UNIPROT is provided in Table S1. SNPs were localized within or in close proximity to 22 genes. In Figure 2, we demonstrate protein–protein interactions based on genes in which SNPs were identified by using GWAS.

The network built using 22 identified genes showed a two-way relationship between CLS37A1 and CSF2RB (Figure 2a). With additional genes, the number of connections increased (Figure 2b).

We performed an analysis of the occurrence of SNPs in the literature data and present information about the traits with which they were associated.

The SNP rs29019625 (BTA1, close to SLC37A1) was associated with phosphorus content [69], with the infrared wavenumber WN414 [70]. SLC37A1 is responsible for the mineral composition of milk from cows, and such data were obtained through GWAS and post-GWAS analyses [71]. SLC37A1 is located in a region from 144.38 to 145.13 Mbp, simultaneously associated with milk yield and the somatic cell score [72]. It was identified as a candidate gene by single-trait analysis for three growth stages (6, 12, and 18 months after birth) in Simmental beef cattle [73].

The TBC1D23 gene, within which rs43232419 was localized, is included in the gene contents of cattle CNV regions [74].

SNP rs110614098 was located in the intron of the gene EPHB2, which was identified as a candidate gene for the maternal effect on calving traits in cattle [75,76].

CSF2RB is of interest as a candidate gene as it is responsible for increased expression in the mammary gland of cows [77,78,79]. A study of SNPs in the CSF2RB gene showed a correlation among the − log10 p-values for milk yield QTL and co-located eQTL for CSF2RB [79].

GWAS studies by Olsen et al. [20] identified four SNPs near the CSN3 gene. Marker rs29024681 located on BTA6 was significantly associated with the protein percent (p ≤ 0.0003) [80]. During the meta-analysis, the following data were obtained: lactation yield and fat percentage showed a statistical relationship between CSN3 genotypes and these traits in additive (p ≤ 0.05) and dominant (p ≤ 0.01) genetic models [81]. Alim et al. [82] found an association of SNPs g.10944A > G, g.12703G > T, g.10985G > A, g.10993T > A, g.10888T > C, and g.10924C > A with traits of milk production. Significant effects of the CSN3 polymorphism to milk infrared spectra were found in 5 regions, where the wavenumbers were from: 1238 to 1292 cm⁻¹, 1431 to 1477 cm⁻¹, 1504 to 1573 cm⁻¹, 2371 to 2607 cm⁻¹, and 3682 to 5008 cm⁻¹. The largest −log10(P) of 19.2 was found for wavenumber 3717 cm⁻¹ [83]. SNP rs29024684 in the CSN3 gene was significantly associated with κ-CN (p = 505,443 × 10⁻⁵⁹) [84]. This SNP was the highest SNP reliably identified for fat, solids, and energy in processed milk [85]; for protein percentage, rennet clotting time for samples coagulating within 45 min after enzyme addition, rennet clotting time for samples reaching 20 mm hardness within 45 min after enzyme addition, serum density 30 min after enzyme addition, rennet clotting time calculated from the clot compaction equation, potential asymptotic clot density, maximum clot density, clot compaction rate constant, and time to reach maximum clot density [86]; for α-LA [87], and for αS1-CN, α-LA, and milk protein content [88].

The SNP rs41653769 is located in the KDR gene. A strong selection signal was identified close to the KDR gene coding the coat color in the beef cattle [89] and in Sahiwal cattle [90], and KDR is also a commonly differentiated candidate gene associated with tropical adaptation in Ethiopian cattle populations [91].

Ha et al. [92] report that the BTB-01594395 mutation in the CEP162 gene was associated with Bovine viral diarrhea virus (BVDV) in Korean Holstein cattle.

The SNP rs41611219 is an intron variant of the ME1 gene. This SNP is associated with udder traits in Montbéliarde, Normande, and Holstein cows [93]. SNPs in the ME1 gene are associated with meat quality traits in Chinese Red cattle [94], and with meat and carcass quality traits in commercial Angus cattle [95].

Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2 (GNG2), within which the SNP rs41568929 was identified [20], was associated with the fertility traits of Bos indicus [96], with retained placenta (RETP) in Canadian Holstein dairy cows [97], with the regulation of metabolic processes of hormones involved in food intake in the Holstein breed [98], and with 305-day milk yield (MY), 305-day fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population [99].

Leal-Gutiérrez et al. [100] studied the meat quality traits from a multibreed Angus-Brahman population and found a signal for WBSF (Warner-Bratzler Shear Force) and tenderness in the ZC3H12C region.

The SNP rs29012314, located within the TMPRSS13 gene identified in Norwegian Red cattle [20], is the start SNP in large-effect QTL associated with maternal weaning weight in Red Angus cattle [101].

The SNP rs41578757 [23] is an intron mutation in the DISP1 gene. Dispatch homologue protein 1 (DISP1) has a function in hedgehog (Hh) signaling, regulates the release and extracellular accumulation of cholesterol-modified hedgehog proteins, and is, therefore, required for efficient production of the Hh signal, and it interacts with SCUBE2 to increase SHH secretion. Suggestively associated with growth in Reggiana breed haplotype was identified on BTA16:26.20–26.35 Mb (p = 1.40 × 10⁻⁷), in the DISP1 gene region [102]. DISP1 is within the window associated with the somatic cell score for Thai dairy cattle [103]. DISP1 is a candidate gene associated with the climatic covariable [104].

The RIMBP2 gene was included in the top 10 upregulated DEGs (differentially expressed genes) in Longissimus dorsi in Wagyu and Chinese Red Steppe cattle [105].

Soares [106] identified, for SCK1.1 (subclinical ketosis in the first lactation), a location on the chromosome 17 window, explaining the largest proportion of genetic variability. Genes involved in the regulation of gene expression were found in this region. Zinc finger proteins were mostly present in this region of the genome, including ZNF891.

The SNP rs41660449 is located in the intron of the gene NCOR2. NCOR2 is a possible functional candidate gene for bilateral convergent strabismus with exophthalmos [107], with candidate variants for perosomus elumbis [108]. NCOR2 is one of the overlapped genomic regions identified in at least two approaches in Valdostana Red Pied, Valdostana Black Pied, and Valdostana Chestnut populations [109].

Near the TRIM37 gene, rs110006082 is associated with lactation persistency [110]. Yu et al. [111] identified differentially expressed genes between preadipocytes and adipocytes and reported TRIM37 as a downregulated gene in differentiated adipocytes.

The SNP rs41589759 is localized within the gene JARID2. JARID2 is a gene located within the QTL which is associated with the development of the hind quarter [112], and it is located in the window from the GWAS explaining >1% of the genetic variation of the performance traits of angus cattle in high-altitude regions (elevation at 2170 m) [113]. Nyman et al. [114] point to JARID2 as a possible candidate gene for cow fertility traits.

The SNP rs29024014 in the NOL4 gene is associated with the content of C18:1t9 in milk [20]. It was shown that SNP rs109278135 near NOL4 was significantly associated with the MFP trait in Chinese Holstein [115], and with residual feed intake (RFI) in dairy cattle [116].

The SNP rs41567529, associated with the content of trans fatty acids in milk [20], is localized in the gene CUX1 (Homeobox protein cut-like 1). The role of CUX1 in hairline phenotypes makes it a strong adaptive candidate when animals are exposed to heat, cold, or toxic ergot alkaloids as a result of fescue stress [117]. This is a candidate gene identified in envGWAS multivariate analysis using continuous environmental attributes as dependent variables for Red Angus [118].

TENM3 is a candidate gene within the most significant QTL, which is associated with height or stature [119].

The SNP rs3423094014, associated with C18:2c9,t11 content in Brown Swiss [25], is localized in the DISC1 gene. Fonseca et al. [120] identified DISC1 as a prioritized candidate gene mapped within and close to (within a 200 kb interval) the haplotype associated with stillbirth events.

The gene CCDC15 (Coiled-coil domain-containing 15), in which rs42176310 was identified, is a protein-coding gene. Ryu and Lee [121] performed a genetic association of the marbling score with intragenic nucleotide variants at selection signals of the bovine genome and reported CCDC15 located at the probable selection signal. Ilska-Warner et al. [122] reported CCDC15 as a potential candidate gene for the telomere length and the association with animal fitness.

Thus, it has been shown that the genes in which SNPs associated with the content of trans fatty acids in milk were identified using GWAS were associated with various quantitative traits of dairy and beef cattle and can be used as candidate genes.

4. Conclusions

In recent years, more and more research has been carried out aimed at studying the genetic factors influencing the formation of quantitative and qualitative traits of farm animals. This article reviewed studies on the content of fatty acids in milk, primarily trans fatty acids, since their content in products is controversial. It should be noted that there have been few studies aimed at finding associations of genetic markers with the amount and composition of trans fatty acids in cow’s milk. We analyzed six articles on this topic. They were performed on Holstein, Red Norwegian, and Brown Swiss cattle breeds. Three studies were based on predetermined SNPs, an association of which with milk production had been previously identified. The remaining three experiments were performed using GWAS. The results of the studies show the presence of SNPs’ effects in the genes associated with the milk production traits in dairy cattle with the content of trans fatty acids and expand knowledge about new genomic regions of SNPs which can also affect the content of trans fatty acids in cow’s milk. At the same time, it should be noted that associations of SNPs identified by means of GWAS with various traits, including milk production, fertility, and adaptive qualities, were recorded; in some cases, such positions were under selection pressure. The material presented in this review can be used to create custom SNP panels, designed to assess their effect on phenotypic traits. Further research in this direction will provide new information that will serve as an impetus for the creation of modern breeding technologies and increase the efficiency of the production of high-quality dairy products.