A post-GWAS confirming the genetic effects and functional polymorphisms of AGPAT3 gene on milk fatty acids in dairy cattle

Lijun Shi,Xin Wu,Yuze Yang,Zhu Ma,Xiaoqing Lv,Lin Liu,Yanhua Li,Feng Zhao,Bo Han and Dongxiao Sun*

Abstract Background:People are paying more attention to the healthy and balanced diet with the improvement of their living standards.Milk fatty acids(FAs)have been reported that they were related to some atherosclerosis and coronary heart diseases in human.In our previous genome-wide association study(GWAS)on milk FAs in dairy cattle,83 genomewide significant single nucleotide polymorphisms(SNPs)were detected.Among them,two SNPs,ARS-BFGL-NGS-109493 and BTA-56389-no-rs associated with C18index(P=0.0459),were located in the upstream of 1-acylglycerol-3-phosphate O-acyltransferase 3(AGPAT3)gene.AGPAT3 is involved in glycerol-lipid,glycerol-phospholipid metabolism and phospholipase D signaling pathways.Hence,it was inferred as a candidate gene for milk FAs.The aim of this study was to further confirm the genetic effects of the AGPAT3 gene on milk FA traits in dairy cattle.Results:Through re-sequencing the complete coding region,and 3000 bp of 5′and 3′regulatory regions of the AGPA T3 gene,a total of 17 SNPs were identified,including four in 5′regulatory region,one in 5′untranslated region(UTR),three in introns,one in 3′UTR,and eight in 3′regulatory region.By the linkage disequilibrium(LD)analysis with Haploview4.1 software,two haplotype blocks were observed that were formed by four and 12 identified SNPs,respectively.Using SAS9.2,we performed single locus-based and haplotype-based association analysis on 24 milk FAs in 1065 Chinese Holstein cows,and discovered that all the SNPs and the haplotype blocks were significantly associated with C6:0,C8:0 and C10:0(P＜0.0001-0.0384).Further,with Genomatix,we predicted that four SNPs in 5′regulatory region(g.146702957G＞A,g.146704373A＞G,g.146704618A＞G and g.146704699G＞A)changed the transcription factor binding sites(TFBSs)for transcription factors SMARCA3,REX1,VMYB,BRACH,NKX26,ZBED4,SP1,USF1,ARNT and FOXA1.Out of them,two SNPs were validated to impact transcriptional activity by performing luciferase assay that the alleles A of both SNPs,g.146704373A＞G and g.146704618A＞G,increased the transcriptional activities of AGPAT3 promoter compared with alleles G(P=0.0004).Conclusions:In conclusion,our findings first demonstrated the significant genetic associations of the AGPAT3 gene with milk FAs in dairy cattle,and two potential causal mutations were detected.

Keywords:AGPAT3,Chinese Holstein,Genetic effects,Milk fatty acids,Potential causal mutation

Introduction

Milk fat is one of critical breeding objectives in dairy cattle.It is comprised of triglyceride(＞95%),diglyceride(2%),phospholipids(1%),cholesterol(0.05%)and small amount of free fatty acids(FAs)(～0.1%)[1].The main components of triglyceride are glycerin and FAs,in which,the FAs act as precursors for the formation of other aroma components,such as esters and alcohols[2].For the various milk fatty acid traits in Holstein cows,the estimated heritability values have been reported to be 0.14-0.33 for saturated fatty acids(SFAs)and 0.08-0.29 for unsaturated fatty acids(UFAs)[3-7].Genome-wide association study(GWAS)is a commonly used strategy to identify potential genetic variants underlying important complex traits in human and domestic animals.So far,some candidate genes and QTL regions for milk production traits have been detected with GWA studies in dairy cattle,such asDLGAP1,AP2B1,SCD,BTA11(1.59-3.37 Mb),and BTA3(70.34-73.69 Mb)[8-13].In our previous GWAS for milk FAs in Chinese Holstein cows,83 genome-wide significant single nucleotide polymorphisms(SNPs)were detected in total[12],in which,two SNPs(ARS-BFGL-NGS-109493 and BTA-56389-no-rs)associated with C18index(P=0.0459),were located in the upstream of 1-acylglycerol-3-phosphate Oacyltransferase 3(AGPAT3)gene.In addition,we performed a joint GWAS for milk FAs in combined Chinese and Danish Holstein populations and found that a chromosome-wide significant QTL region of 146.29-146.31 Mb on BTA1 was associated with C18:0[13].TheAGPAT3gene was nearby this region with approximately 400 kb.1-acylglycerol-sn-glycero 3-phosphate acyltransferase(AGPAT),encoded by theAGPAT3gene,is one of the isoforms of AGPATs[14]and is involved in the glycerolipid(ko00561)and glycerophospholipid metabolisms(ko00564),and phospholipase D signaling pathway(ko04072).Mammalian AGPAT catalyzed the acylation of lysophosphatidic acid to form the phosphatidic acid that was the precursor of all glycerplipids[14].Therefore,it was implied that theAGPAT3gene was a promising candidate gene for milk FA traits in dairy cattle.The purpose of the present study was to further detect whether theAGPAT3gene had significant genetic effects on milk FAs in a Chinese Holstein cow population.

Materials and methods

Animals and phenotypic data

In this study,a total of 1065 Chinese Holstein cows were used as descripted in a previous research[15],which milk samples were collected in Beijing Dairy Cattle Center(www.bdcc.com.cn)to measure milk FA contents.With the gas chromatography method,a total of 16 milk FAs(C6:0,C8:0,C10:0,C11:0,C12:0,C13:0,C14:0,C14:1,C15:0,C16:0,C16:1,C17:0,C17:1,C18:0,C18:1cis-9 and C20:0)were measured as the weight proportion of total fat weight[12].With the phenotypes,we calculated five indices based on the formula[16]:C14index=C17indexC18indexandIn addition,the summarized SFA and UFA,and SFA/UFA were obtained as well.

SNP identification and genotyping

Based on the genomic sequence of bovineAGPAT3gene(Gene ID:506607),14 pairs of primers(Table S1)were designed by the Primer 3 version 4.0(http://bioinfo.ut.ee/primer3-0.4.0/)and were synthesized in the Beijing Genomics Institute(Beijing,China)to amplify all the exons with partial adjacent intron region,and 3000 bp of 5′and 3′regulatory regions.As previously descripted[15],two DNA pools were constructed and the polymerase chain reaction(PCR)amplifications were performed with each DNA pool as template.To identify potential polymorphisms,the PCR amplification products were bidirectionally sequenced with an ABI3730XL DNA analyzer(Applied Biosystems,Foster,CA,USA).Then,the identified SNPs were genotyped for the 1065 cows by the matrix-assisted laser-desorption/ionization time of flight mass spectrometry(MALDI-TOF MS,Sequenom MassARRAY,Bioyong Technologies Inc.,HK).

Linkage disequilibrium(LD)and association analyses

We estimated the LD among the identified SNPs ofAGPAT3gene with Haploview 4.1(Broad Institute,Cambridge,MA,USA).

For association analysis,the 1065 cows were traced back to three-generation pedigrees to construct the kinship matrix(A-matrix)by SAS 9.2(SAS institute,Cary,NC,USA),so that 3335 individuals were totally included.Single-locus and haplotype-based associations with 24 kind of milk FAs were performed by the following mixed animal model with SAS 9.2:

Here,Yijklmis the phenotypic value of each milk fatty acid trait;μis the overall mean;Giis the fixed effect corresponding to the genotype or haplotype combination of individuali;hj(j=1-23)andlk(k=1-4)were the fixed effect of farmjand stage of lactation ll,respectively;alis the random polygenic effect;Mm(m=1-293)is the fixed effect of age at calvingm;bis the regression coefficient of covariateM;andeijklmis the random residual.Further,we calculated the additive effect(a),dominant effect(d),and allele substitution effect(α)according toaHere,AA,AB and BB were the least square means of milk FAs corresponding to the genotypes,andpandqwere the frequencies of allele A and B,respectively.

Prediction of changes of transcription factor binding sites(TFBSs)caused by the SNPs in 5′regulatory region

We used the Genomatix software suite v3.9(http://www.genomatix.de/cgi-bin/welcome/welcome.pl?s=d1b5 c9a9015b02bb3b1a806f9c03293f)[18]to predict whether the four SNPs in 5′regulatory region ofAGPAT3(g.146702957G＞A,g.146704373A＞G,g.146704618A＞G,and g.146704699G＞A)changed the TFBSs.

Recombinant plasmid construction and luciferase assay

To detect the allele-specific effects of the SNPs g.146702957G＞A,g.146704373A＞G,g.146704618A＞G,and g.146704699G＞A on the transcriptional activity ofAGPAT3gene,eight luciferase reporter gene fragments(G and A of g.146702957G＞A;A and G of g.146704373A＞G;A and G of g.146704618A＞G;and G and A of g.146704699G＞A)corresponding to the eight alleles of the four SNPs(Fig.1a)were designed and synthesized(Genewiz,Suzhou,China).The eight fragments with the KpnI and Nhel restriction sites at the 5′and 3′termini,respectively,were cloned into the pGL4.14 luciferase assay vector(Promega,Madison,USA).In addition,all the plasmids were purified by the Endo-free Plasmid DNA Mini Kit II(OMEGA,omega bio-tek,Norcross,Georgia,USA),and were sequenced to confirm the integrity of each construct’s insertion.

The human embryonic kidney(HEK)293 T cells were cultured with Dulbecco′s modified Eagle′s medium(Gibco,Life Technologies)and 10% fetal bovine serum(Gibco)at 37°C in a humidified incubator containing 5%CO2.Before transfection,about 2×105cells were seeded in each 24-well plate.For eight luciferase reporter gene fragments of g.146702957G＞A,g.146704373A＞G,g.146704618A＞G and g.146704699G＞A,500 ng constructed plasmid was co-transfected along with 10 ng pRLTK Renilla luciferase reporter vector(Promega)into each well.All the experiments were performed in three replicates for each construct.Approximate 48 h after transfection,the cells were harvested and the activity of both firefly and Renilla luciferases were measured with a Dual-Luciferase Reporter Assay System(Promega)on a Modulus microplate multimode reader(Turner Biosystems,CA,USA).The average statistic of three replicates were calculated as the normalized luciferase data(Firefly/Renilla).

Results

Identification of SNPs

A total of 17 SNPs of theAGPAT3gene was detected in this study(Table 1),which consisted of four(g.146702957G＞A,g.146704373A＞G,g.146704618A＞G and g.146704699G＞A)in 5′flanking region,one(g.146705692G＞A)in 5′untranslated region(UTR),three(g.146725085 T＞C,g.146726096A＞G and g.146729107A＞C)in introns,one(g.146735090G＞T)in 3′ UTR, and eight (g.146737188C＞T,g.146737545G＞A,g.146737748 T＞C,g.146737849C＞T, g.146737879 T＞G, g.146737916 T＞C,g.146737946C＞T and g.146738055G＞A)in 3′flanking region.The genotype and allele frequencies of the identified SNPs were presented in Table 1.

Estimation of LD among the identified SNPs of AGPAT3

Fig.1 Luciferase assay.a Sketches of recombinant plasmids with g.146702957G＞A,g.146704373A＞G,g.146704618A＞G,and g.146704699G＞A in the 5′flanking region of AGPAT3gene.The nucleotides in red highlight refer to the SNP.b Luciferase assay analysis of the recombinant plasmids in HEK293 cells.Blank:Blank cells.PGL4.14:Empty vector.**:P＜0.01

Table 1 Information of 17 SNPs of AGPAT3 gene with genotypic and allelic frequencies

Table 1 Information of 17 SNPs of AGPAT3 gene with genotypic and allelic frequencies(Continued)

We used the haploview 4.1 to estimate the LD among these 17 SNPs,and observed two haplotype blocks(Fig.2)that was formed by four and 12 SNPs,respectively.The haplotype block 1 included four haplotype combinations,namely,H1:GAAG(38%),H2:GAAA(32.2%),H3:AGGG(26.6%),and H4:GAGG(3%),and the haplotype block 2 had six haplotype combinations:H1=GTAAGCGTCTTC, H2=GCACGTACTGCT,H3=GCAATCGTCTTC, H4=ACACGCGTCTTC,H5=GTGATCGTCTTC,and H6=GCAAGCGTCTTC with their frequencies of 20%,39.8%,13.4%,13.4%,7.9%and 4.1%.

Associations between AGPAT3 and milk FAs

The associations of the 17 SNPs with 24 milk FAs were summarized in Table 2.Among these SNPs,17 were strongly associated with C6:0(P＜0.0001-0.0004)and C8:0(P＜0.0001-0.0384);14 were significantly associated with total index(P＜0.0001-0.0318);ten were significantly associated with C10:0(P=0.0016-0.0151);nine were strongly associated with C17:1(P＜0.0001-0.0149);seven were significantly associated with C20:0(P＜0.0001-0.0072);five had significant associations with C14:0(P＜0.0001-0.0477);five were strongly associated with C17index(P=0.0006-0.0389);five had strong associations with C18:1cis-9(P＜0.0001-0.0258);three had significant associations with C18:0(P=0.0020-0.0246);three had strong associations with SFA(P＜0.0001-0.0434);two were significantly associated with C17:0(P=0.0212-0.0413);two were significantly associated with UFA(P＜0.0001 andP=0.0386);one was strongly associated with C18index(P=0.0249);and one had significant association with SFA/UFA(P=0.0005).However,no significant association was found with C11:0,C12:0,C13:0,C14:1,C15:0,C16:0,C16:1,C14index and C16index(P＞0.05).

Fig.2 Linkage disequilibrium(LD)among the 17 SNPs of AGPAT3 gene.The blocks indicate haplotype blocks,and the text above the horizontal number is the SNP names.The values in boxes are pairwise SNP correlations(D′),while the bright red boxes without numbers inffer complete LD(D′=1).The boxes have the greater LD with the brigther red

C17:0,%C16:1,%C16:0,%C15:0,%0.5631±0.0055 0.5693±0.0031 0.5653±0.0032 0.2642 0.5696±0.0031 0.5643±0.0055 0.5641±0.0033 0.1370 0.5698±0.0031a 0.5638±0.0052 0.5621±0.0032b 0.0212*0.5629±0.0048 0.5682±0.0032 0.5647±0.0032 0.3310 0.5820±0.0133 0.5665±0.0029 0.5663±0.0036 0.4920 0.5675±0.0031 0.5690±1.3640±0.0417 1.3327±0.0247 1.3238±0.0255 0.5783 1.3326±0.0246 1.3626±0.0423 1.3192±0.0260 0.5151 1.3183±0.0250 1.3507±0.0398 1.3120±0.0251 0.5678 1.2832±0.0362 1.3158±0.0253 1.3390±0.0252 0.2175 1.2628±0.0991 1.3316±0.0233 1.3301±0.0284 0.7785 1.3135±0.0250 1.3526±34.7663±0.3048 34.8480±0.1750 34.8295±0.1822 0.9581 34.8165±0.1762 34.8297±0.2999 34.8408±0.1846 0.9871 34.7496±0.1778 34.6202±0.2865 34.8113±0.1797 0.7525 34.6861±0.2593 34.8492±0.1810 34.7333±0.1806 0.6735 35.1757±0.7126 34.8014±0.1658 34.8758±0.2043 0.7941 34.9341±0.1775 34.7645±0.9937±0.0219 0.9909±0.0120 0.9968±0.0128 0.8721 0.9906±0.0121 0.9887±0.0222 0.9959±0.0129 0.8781 0.9925±0.0122 0.9926±0.0207 0.9956±0.0125 0.9604 0.9839±0.0189 0.9898±0.0125 1.0021±0.0126 0.4571 0.9965±0.0536 0.9888±0.0113 1.0037±0.0144 0.4783 0.9924±0.0123 0.9954±C14:1,%0.6665±0.0323 0.6475±0.0178 0.6730±0.0189 0.6411±0.0179 0.6567±0.0324 0.6651±0.0190 0.3318 0.6421±0.0181 0.6692±0.0300 0.6706±0.0185 0.1903 0.6572±0.0273 0.6434±0.0185 0.6607±0.0185 0.5672 0.6254±0.0768 0.6495±0.0168 0.6425±0.0211 0.6482±0.0180 0.6559±Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)C13:0,% C14:0,%10.1091±0.1114a 10.2460±0.0695 10.3670±0.0713b 0.0095**0.2843 10.2422±0.0699 10.1221±0.1115 10.3515±0.0717 0.0261*10.2405±0.0701 10.2407±0.1056 10.3059±0.0708 0.4364 10.3492±0.0976 10.2789±0.0714 10.3046±0.0707 0.6869 9.8944±0.2514 10.1890±0.0666A 10.4710±0.0777B＜.0001**0.8836 10.2790±0.0705 10.1990±0.0977±0.0055 0.1003±0.0029 0.0985±0.0030 0.7895 0.0997±0.0028 0.0967±0.0055 0.0975±0.0030 0.7032 0.0998±0.0029 0.0982±0.0052 0.0997±0.0030 0.9495 0.0979±0.0047 0.0972±0.0030 0.1006±0.0030 0.5248 0.1107±0.0136 0.0975±0.0026 0.1021±0.0035 0.2420 0.0991±0.0029 0.0981±C12:0,%3.0058±0.0652 3.0015±0.0410 3.0068±0.0425 0.9854 3.0240±0.0410 3.0067±0.0652 3.0271±0.0429 0.9398 3.0223±0.0414 3.0483±0.0612 3.0496±0.0421 0.6596 3.0193±0.0569 3.0216±0.0421 3.0363±0.0417 0.8808 3.0099±0.1491 3.0048±0.0393 3.0349±0.0459 0.6684 2.9767±0.0417 2.9839±C11:0,%0.0583±0.0048 0.0584±0.0034 0.0586±0.0035 0.9962 0.0579±0.0035 0.0571±0.0047 0.0574±0.0036 0.9561 0.0579±0.0035 0.0582±0.0045 0.0588±0.0035 0.0590±0.0043 0.0574±0.0035 0.0591±0.0035 0.0544±0.0099 0.0577±0.0034 0.0596±0.0037 0.6338 0.0587±0.0035 0.0576±C10:0,%2.8618±0.0492 2.8278±0.0319 2.8646±0.0330 2.8096±0.0318 2.8117±0.0499 2.8525±0.0334 2.8322±0.0323A 2.8859±0.0473 2.9155±0.0324B 2.8484±0.0435 2.8053±0.0325A 2.8843±0.0330B 2.7974±0.1115 2.7966±0.0307 2.8012±0.0361 0.9829 2.8094±0.0324 2.8207±C8:0,%0.9946±0.0175A 0.9187±0.0106B 0.9914±0.0111A 0.8829±0.0106A 0.9368±0.0173B 0.9533±0.0111 0.9070±0.0107A 0.9726±0.0164B 1.0007±0.0109B 0.9396±0.0151A 0.9274±0.0108A 0.9886±0.0110B 0.8508±0.0393 0.9413±0.0103 0.9309±0.0120 0.9110±0.0108A 0.9664±C6:0,%0.0204A 0.4264±0.0119B 0.5226±0.0125C＜.0001**＜.0001**0.2626 0.4101±0.0120A 0.0205B 0.5002±0.0127C＜.0001**＜.0001**0.1676 0.4167±0.0121A 0.0193Ba 0.5155±0.0123Bb＜.0001**＜.0001**0.0016**0.8984 0.0173A 0.4614±0.0125A 0.5119±0.0124B＜.0001**＜.0001**0.0040**0.6878 0.2969±0.0462A 0.4844±0.0114B 0.4644±0.0140B＜.0001**0.0384*0.4525±0.0121A 0.5018±Genotype (No.)AA(66-73)0.5840±GG(473-509)GA(337-367)P AA(470-505)GG(66-73)0.5641±GA(332-361)P AA(437-474)GG(76-84)0.5594±GA(368-396)P AA(97-102)0.4391±AG(366-402)GG(412-443)P AA(9)GG(638-690)GA(230-250)P CC(449-486)CT(347-SNP g.146702957G＞A g.146704373A＞G g.146704618A＞G g.146704699G＞A g.146705692G＞A g.146725085 T＞C

C17:0,%C16:1,%C16:0,%C15:0,%C14:1,%0.0033 0.5651±0.0054 0.7081 0.5646±0.0028 0.5732±0.0042 0.5561±0.0178 0.0541 0.5650±0.0039 0.5718±0.0036a 0.5640±0.0031b 0.0413*0.5697±0.0030 0.5673±0.0033 0.5726±0.0078 0.5951 0.5678±0.0034 0.5697±0.0031 0.5753±0.0042 0.1793 0.5706±0.0042 0.5654±0.0033 0.5665±0.0031 0.0260 1.2804±0.0416 0.0673 1.3251±0.0230 1.3088±0.0324 1.2342±0.1318 0.6686 1.3271±0.0304 1.3319±0.0282 1.3357±0.0247 0.9431 1.3414±0.0240 1.3009±0.0260 1.3066±0.0590 0.1367 1.3351±0.0264 1.3330±0.0248 1.3487±0.0321 0.8567 1.3322±0.0322 1.3318±0.0263 1.3239±0.0247 0.1858 34.7673±0.2953 0.5001 34.9294±0.1634 34.4974±0.2316 34.9285±0.9476 0.0865 34.7045±0.2154 34.7739±0.2011 34.8408±0.1767 0.7516 34.8202±0.1713 34.7381±0.1858 34.5408±0.4264 0.7122 34.7063±0.1890 34.8170±0.1770 34.6458±0.2300 0.6289 34.5818±0.2305 34.6776±0.1877 34.8114±0.1754 0.0129 1.0009±0.0216 0.9063 0.9947±0.0111 0.9798±0.0166 0.9762±0.0715 0.5894 0.9933±0.0156 0.9904±0.0142 0.9991±0.0121 0.7714 0.9965±0.0118 0.9898±0.0129 0.9771±0.0317 0.7195 0.9988±0.0132 0.9955±0.0122 0.9790±0.0166 0.4650 0.9801±0.0166 1.0007±0.0132 0.9951±0.0122 0.0189 0.6607±0.0313 0.8512 0.6553±0.0166 0.6513±0.0242 0.5784±0.1024 0.7439 0.6707±0.0228 0.6559±0.0207 0.6552±0.0180 0.7282 0.6514±0.0176 0.6592±0.0190 0.6209±0.0446 0.6492 0.6581±0.0194 0.6511±0.0179 0.6586±0.0242 0.8950 0.6582±0.0241 0.6558±0.0194 0.6492±0.0179 Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C13:0,% C14:0,%0.0720 10.2449±0.1100 0.3190 10.3084±0.0657a 10.1355±0.0879b 10.5609±0.3391 0.0314*10.1650±0.0823 10.2943±0.0782 10.2937±0.0696 0.1253 10.3081±0.0686 10.1970±0.0723 10.0790±0.1526 0.0477*10.1935±0.0741 10.2310±0.0697 10.0919±0.0876 0.1655 10.1601±0.0876 10.2857±0.0729 10.3073±0.0697 0.0030 0.1018±0.0054 0.7757 0.0996±0.0026 0.0974±0.0041 0.0981±0.0180 0.8364 0.0988±0.0037 0.0988±0.0034 0.0984±0.0029 0.9905 0.1001±0.0027 0.0974±0.0031 0.0929±0.0078 0.4694 0.0999±0.0031 0.0981±0.0029 0.0968±0.0040 0.7234 0.0975±0.0040 0.1007±0.0031 0.0982±0.0029 C12:0,%0.0430 3.0082±0.0641 0.8546 3.0394±0.0390 3.0084±0.0521 3.1630±0.1962 0.5976 2.9831±0.0487 2.9740±0.0458 3.0187±0.0413 0.3680 3.0735±0.0404 3.0090±0.0430 3.0448±0.0882 0.1107 3.0228±0.0438 3.0423±0.0413 2.9847±0.0514 0.3982 2.9638±0.0514 3.0218±0.0435 3.0188±0.0412 C11:0,%0.0036 0.0610±0.0047 0.6288 0.0578±0.0034 0.0557±0.0040 0.0701±0.0129 0.4269 0.0565±0.0039 0.0564±0.0037 0.0587±0.0035 0.0589±0.0034 0.0576±0.0036 0.0537±0.0061 0.5402 0.0589±0.0036 0.0585±0.0035 0.0579±0.0040 0.0565±0.0040 0.0584±0.0036 0.0576±0.0035 C10:0,%0.0336 2.8365±0.0487 2.8758±0.0304 2.8526±0.0395 3.0515±0.1473 2.8547±0.0375b 2.7656±0.0356Aa 2.8525±0.0323B 2.8328±0.0315 2.8230±0.0334 2.9134±0.0675 2.8918±＜.0001**＜.0001**0.7754 0.0004**0.0015**0.3415＜.0001**＜.0001**0.0020**0.5029＜.0001**0.0069**0.3563 0.0339Aa 2.8306±0.0323b 2.7818±0.0397B 2.8094±0.0396a 2.9008±0.0339b 2.8496±0.0319 C8:0,%0.0111B 0.9116±0.0170A 0.9597±0.0101A 0.9216±0.0136B 0.9142±0.0513 0.9758±0.0127A 0.8846±0.0119B 0.9650±0.0108A 0.9428±0.0105 0.9288±0.0113A 0.9944±0.0233B 0.9949±0.0113A 0.9489±0.0106B 0.9270±0.0135B 0.9200±0.0135Aa 0.9867±0.0113B 0.9491±0.0107Ab C6:0,%0.0126B 0.4274±0.0202A 0.4677±0.0113A 0.4266±0.0157Bb 0.6162±0.0686a 0.5256±0.0149Aa 0.4039±0.0138B 0.4901±0.0121Ab 0.4596±0.0118A 0.4480±0.0127A 0.7225±0.0295B 0.5244±0.0129A 0.4912±0.0123B 0.4013±0.0157C＜.0001**＜.0001**0.0020**0.9407 0.4117±0.0157A 0.5261±0.0130B 0.5097±0.0121B Genotype (No.)376)TT(65-70)P AA(740-804)AG(136-144)GG(4-5)P AA(166-183)CC(240-260)CA(461-492)P GG(524-568)GT(326-349)TT(26-31)P CC(317-344)CT(413-448)TT(137-148)P AA(137-148)GG(316-343)GA(419-455)SNP g.146726096A＞G g.146729107A＞C g.146735090G＞T g.146737188C＞T g.146737545G＞A

C17:0,%C16:1,%C16:0,%C15:0,%C14:1,%0.4321 0.5735±0.0041 0.5674±0.0031 0.5663±0.0034 0.1867 0.5673±0.0034 0.5693±0.0031 0.5746±0.0042 0.2053 0.5739±0.0042 0.5663±0.0031 0.5655±0.0034 0.0919 0.5741±0.0042 0.5672±0.0033 0.5683±0.0031 0.2206 0.5630±0.0034 0.5682±0.0042 0.5650±0.0031 0.4202 0.5674±0.0122 0.9184 1.3348±0.0317 1.3342±0.0246 1.3359±0.0267 0.9969 1.3386±0.0265 1.3318±0.0246 1.3495±0.0323 0.8199 1.3232±0.0319 1.3154±0.0246 1.3229±0.0266 0.9269 1.3256±0.0320 1.3300±0.0266 1.3212±0.0246 0.9201 1.3334±0.0264 1.3427±0.0324 1.3304±0.0247 0.9099 1.3472±0.0940 0.4569 34.6990±0.2295 34.8666±0.1762 34.7461±0.1888 0.6140 34.6572±0.1886 34.8335±0.1766 34.6245±0.2293 0.4155 34.7225±0.2283 34.8734±0.1770 34.7756±0.1892 0.6984 34.6490±0.2278 34.7363±0.1881 34.7903±0.1759 0.7790 34.7602±0.1890 34.6590±0.2300 34.8888±0.1760 0.4662 35.1841±0.6782 0.4545 0.9773±0.0165 0.9927±0.0122 0.9975±0.0131 0.4587 0.9977±0.0133 0.9947±0.0122 0.9804±0.0166 0.5650 0.9809±0.0167 0.9975±0.0122 1.0019±0.0132 0.4325 0.9769±0.0165 0.9981±0.0131 0.9921±0.0122 0.4280 1.0016±0.0134 0.9796±0.0165 0.9973±0.0121 0.3945 0.9913±0.0486 0.8841 0.6542±0.0240 0.6476±0.0179 0.6574±0.0194 0.8346 0.6588±0.0194 0.6471±0.0178 0.6600±0.0243 0.7288 0.6588±0.0239 0.6513±0.0178 0.6594±0.0194 0.8731 0.6547±0.0239 0.6549±0.0194 0.6464±0.0180 0.8589 0.6597±0.0193 0.6572±0.0242 0.6447±0.0179 0.6382 0.6569±0.0695 Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C13:0,% C14:0,%0.1234 10.1822±0.0867 10.3018±0.0699 10.2819±0.0737 0.2573 10.2313±0.0740 10.2479±0.06960 10.1267±0.0874 0.2572 10.1224±0.0876 10.2698±0.0696 10.2327±0.0735 0.1295 10.2239±0.0870 10.2947±0.0734 10.3252±0.0696 0.3730 10.2241±0.0738 10.1050±0.0876 10.2450±0.0689 0.1510 10.3654±0.2293 0.6416 0.0977±0.0040 0.0994±0.0028 0.1015±0.0031 0.6124 0.1005±0.0031 0.0980±0.0029 0.0968±0.0040 0.6079 0.0962±0.0040 0.0977±0.0029 0.0998±0.0031 0.6531 0.0969±0.0040 0.1009±0.0031 0.0983±0.0029 0.5637 0.1004±0.0032 0.0968±0.0040 0.0981±0.0029 0.6268 0.0976±0.0122 C12:0,%0.3853 2.9404±0.0514 3.0032±0.0410 2.9957±0.0439 0.3252 2.9970±0.0438 3.0009±0.0410 2.9274±0.0518 0.2126 2.9852±0.0514 3.0364±0.0412 3.0380±0.0437 0.4448 2.9481±0.0512 3.0094±0.0435 3.0156±0.0411 0.2742 2.9994±0.0439 2.9507±0.0512 3.0044±0.0411 0.4414 3.0416±0.1363 C11:0,%0.7932 0.0575±0.0040 0.0583±0.0035 0.0585±0.0036 0.9369 0.0593±0.0036 0.0586±0.0035 0.0579±0.0040 0.0575±0.0040 0.0589±0.0035 0.0596±0.0036 0.0572±0.0040 0.0582±0.0036 0.0580±0.0035 0.0588±0.0036 0.0575±0.0040 0.0582±0.0035 0.8961 0.0479±0.0090 C10:0,%2.8149±0.0393A 2.8760±0.0323 2.9154±0.0336B 2.8975±＜.0001**＜.0001**0.0134*＜.0001**＜.0001**0.0106*0.0338Aa 2.8322±0.0324b 2.7934±0.0392B 2.7827±0.0399A 2.8248±0.0323a 2.8914±0.0337Bb 2.8087±0.0392a 2.9066±0.0338b 2.8552±0.0320 2.8350±0.0341a 2.7486±0.0392b 2.7811±0.0319 2.8684±0.1023 C8:0,%0.9086±0.0134Aa 0.9386±0.0107Ab 0.9793±0.0113B 0.9965±0.0114A 0.9498±0.0106B 0.9285±0.0134B 0.9053±0.0135Aa 0.9359±0.0107Ab 0.9831±0.0113B 0.9080±0.0133A 0.9891±0.0113B 0.9291±0.0107A 1.0049±0.0113A 0.9113±0.0135B 0.9464±0.0106C 1.0161±0.0354 C6:0,%0.3955±0.0156A 0.4892±0.0121Ba 0.5161±0.0130Bb 0.5257±0.0130Aa 0.4959±0.0121Ab 0.4106±0.0157B＜.0001**＜.0001**0.0022**0.8856 0.4117±0.0158A 0.5013±0.0121B 0.5384±0.0130C＜.0001**＜.0001**0.0017**0.7680 0.3909±0.0155A 0.5185±0.0129B 0.4963±0.0122B＜.0001**＜.0001**0.0088**0.9406 0.5219±0.0130A 0.4155±0.0156B 0.4999±0.0122A＜.0001**＜.0001**0.0151*0.0459A Genotype (No.)P CC(139-151)CT(420-455)TT(314-341)P CC(313-340)CT(421-457)TT(136-147)P GG(137-148)GT(416-452)TT(314-341)P CC(139-151)TT(315-342)CT(419-454)P CC(309-336)TT(138-149)CT(424-460)P AA(10-12)0.6010±SNP g.146737748 T＞C g.146737849C＞T g.146737879 T＞G g.146737916 T＞C g.146737946C＞T g.146738055G＞

C17:0,%0.5638±0.0036 0.5711±0.0029 0.0622 Total index,%27.6986±0.2400 27.6651±0.1408a 27.3392±0.1492b 0.0137*27.5503±0.1427A 27.5179±0.2397 27.1837±0.1520B 0.0065**27.6612±0.1445a 27.7215±0.2257 27.3551±0.1486b 0.0173*27.7433±0.2080 27.3897±0.1458 27.6124±0.1479 0.0692 27.5978±0.5625 27.5684±C16:1,%C16:0,%1.3007±0.0284 1.3298±0.0234 0.4293 SFA/UFA 34.7921±0.2040 34.7389±0.1667 0.7698 UFA,%2.2673±0.0383 2.2689±0.0209 2.2946±0.0221 0.3992 2.2695±0.0210 2.2679±0.0387 2.2966±0.0224 0.3715 2.2704±0.0212 2.2769±0.0362 2.3049±0.0217 0.2066 2.2693±0.0330 2.2872±0.0217 2.2789±0.0219 0.8274 2.2889±0.0942 2.2644±30.4810±0.2651 30.4841±0.1442 30.1838±0.1525 0.0767 30.4771±0.1448 30.5383±0.2663 30.1883±0.1544 0.0790 30.5624±0.1480 30.6622±0.2488 30.2507±0.1491 0.0386*30.3455±0.2268 30.3321±0.1507 30.4898±0.1492 0.5095 30.5992±0.6477 30.4947±C15:0,%0.9892±0.0144 0.9931±0.0115 0.9508 67.7650±0.2901 67.8726±0.1595 68.0908±0.1672 0.2479 67.8406±0.1588 67.7508±0.2936 68.1000±0.1698 0.1635 67.9068±0.1614 67.7817±0.2744 68.1314±0.1646 0.2011 67.8410±0.2477 68.0257±0.1649 67.8451±0.1655 68.1513±0.7081 67.9010±C14:1,%0.6599±0.0210 0.6563±0.0169 0.9801 25.6833±0.3643 24.9133±0.2143 24.9476±0.2258 0.0648 24.8788±0.2148 25.6137±0.3613 24.8785±0.2281 0.0732 24.6723±0.2156 25.3871±0.3399 24.6942±0.2223 0.0623 24.3088±0.3144A 24.5937±0.2213A 25.1974±0.2243B 0.0006**0.4504 23.4125±0.8489 25.0231±Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C13:0,% C14:0,%10.1430±0.0783 10.2445±0.0669 0.1855 C16index,% C17index,% SFA,%3.7684±0.1055 3.6753±0.0635 3.6562±0.0658 0.5211 3.6613±0.0633 3.7324±0.1072 3.6303±0.0667 0.5611 3.6288±0.0640 3.6981±0.1002 3.6029±0.0647 0.5736 3.5700±0.0924 3.6496±0.0646 3.7083±0.0653 0.2267 3.4819±0.2506 3.6825±0.0968±0.0035 0.1003±0.0026 0.5412 C14index,%6.4077±0.2370 6.1345±0.1378 6.3010±0.1449 6.0812±0.1397 6.3322±0.2387 6.2187±0.1449 6.1572±0.1406 6.3577±0.2214 6.3184±0.1421 6.2255±0.2057 6.1203±0.1428 6.2892±0.1427 0.3721 6.0520±0.5535 6.3397±C12:0,%2.9691±0.0461 3.0454±0.0394 0.0792 0.1666±0.0031 0.1722±0.0017A 0.1656±0.0018B 0.0001**0.2470 0.1730±0.0017A 0.1683±0.0030 0.1660±0.0018B＜.0001**0.3517 0.1721±0.0017A 0.1661±0.0029 0.1668±0.0018B 0.0024**0.3292 0.1715±0.0025 0.1695±0.0017 0.1683±0.0017 0.4108 0.1951±0.0077A 0.1706±C11:0,%0.0584±0.0037 0.0592±0.0034 0.3930 C18index,% C20:0,%57.6323±0.4830 57.3457±0.2687 56.9727±0.2812 0.1783 57.5363±0.2686 57.9715±0.4858 57.1583±0.2841 0.1186 57.3032±0.2720 57.7299±0.4541 57.2219±0.2775 0.4975 57.8611±0.4127 57.1301±0.2779a 57.7454±0.2775b 0.0249*57.2586±1.1637 57.3378±C10:0,%2.7914±0.0360 2.8074±0.0310 C18:1cis-9.%19.1043±0.2147 19.1790±0.1155 18.9544±0.1220 0.1290 19.2191±0.1150 19.1654±0.2165 18.9787±0.1240 0.0995 19.3545±0.1167a 19.2130±0.2015 19.0297±0.1199b 0.0137*19.3065±0.1844 19.1440±0.1202 19.2363±0.1189 19.6663±0.5313 19.2113±C8:0,%0.9751±0.0122A 0.9454±0.0102B C18:0,%13.8892±0.1615 14.1758±0.0849 14.0807±0.0916 0.1580 14.1259±0.0853 13.8793±0.1644 13.9943±0.0926 0.1494 14.1934±0.0867a 13.8955±0.1515 13.9823±0.0885b 0.0182*13.8701±0.1379b 14.2179±0.0895Aa 13.9484±0.0895B 0.0020**0.5640 14.3208±0.3965 14.1146±C6:0,%0.5257±0.0138A 0.4368±0.0115B＜.0001**0.0010**0.6593 C17:1,%0.0042 0.1895±0.0024 0.1894±0.0026 0.4386 0.1913±0.0024 0.0042 0.1909±0.0026 0.3096 0.1929±0.0025 0.0039 0.1916±0.0025 0.2123 0.0036 0.1886±0.0025 0.1935±0.0025 0.0659 0.1733±0.0099 0.1888±Genotype (No.)AG(218-237)GG(650-700)P Genotype(No.)AA(62-73)0.1943±GG(424-511)GA(367-395)P AA(420-507)GG(62-73)0.1968±GA(290-361)P AA(392-475)GG(69-84)0.1980±GA(324-396)P AA(92-102)0.1903±AG(324-443)GG(365-443)P AA(7-9)GG(575-SNP A SNP g.146702957G＞A g.146704373A＞G g.146704618A＞G g.146704699G＞A g.146705692G＞A

C17:0,%C16:1,%C16:0,%C15:0,%C14:1,%0.1365A 27.1387±0.1658B 0.0034**27.3956±0.1473a 27.6832±0.1486b 27.8078±0.2388 0.0207*27.4019±0.1349A 28.0468±0.1811Bb 25.9007±0.7434a 27.7110±0.1753 27.5823±0.1642 27.5050±0.1431 0.3631 27.5196±0.1422 27.4330±0.1490 27.2316±0.3293 0.5447 27.4624±0.1555 27.4299±0.1436a 27.8433±0.1864b 0.0197 2.2906±0.0251 0.4718 2.2931±0.0213 2.2615±0.0222 2.2696±0.0381 0.2723 2.2925±0.0193A 2.2118±0.0288Bb 2.5679±0.1243a 2.2755±0.0266 2.2672±0.0248 2.2879±0.0213 0.6297 2.2819±0.0204 2.2809±0.0226 2.3215±0.0547 0.7477 2.2880±0.0228 2.2958±0.0214 2.2482±0.0291 0.1354 30.1774±0.1714 0.0946 30.2848±0.1468 30.5957±0.1552 30.6268±0.2630 0.0542 30.2985±0.1327Aa 31.0017±0.1996B 28.1258±0.8622Ab 30.4868±0.185 30.5267±0.1705 30.3797±0.1454 0.5871 30.3923±0.1411 30.3837±0.1551 30.2155±0.3762 0.8890 30.3617±0.1578 30.2470±0.1460 30.6818±0.1987 0.1488 68.2087±0.1901 0.159 68.0279±0.1619 67.7874±0.1695 67.6933±0.2875 0.1896 68.0865±0.1467A 67.3379±0.2192Bb 70.1075±0.9410a 67.8911±0.2031 67.7963±0.1872 67.9785±0.1603 0.5431 68.0759±0.1559 67.9829±0.1706 68.2537±0.4099 0.7056 68.0396±0.1746 68.0867±0.1608 67.6431±0.2173 0.2052 25.0126±0.2497 0.1576 24.8455±0.2162 25.2682±0.2286 25.4392±0.3615 0.0294*24.7419±0.1997A 25.5638±0.2780B 23.7060±1.1232 0.0011**＜.0001**＜.0001**0.0005**＜.0001**25.1709±0.2648 24.7129±0.2420 24.9844±0.2181 0.1738 24.8158±0.2105 24.8500±0.2284 24.9340±0.5123 0.9587 25.3794±0.2296 24.9450±0.2165 25.2577±0.2793 Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C13:0,% C14:0,%0.0560 3.6635±0.0723 0.6902 3.6277±0.0641 3.7375±0.0663 3.5625±0.1057 0.0520 3.6343±0.0594 3.6312±0.0822 3.3763±0.3321 0.7344 3.6593±0.0779 3.6834±0.0720 3.6817±0.0634 0.9350 3.6985±0.0616 3.6004±0.0664 3.6534±0.1493 0.1695 3.6909±0.0677 3.6688±0.0638 3.7299±0.0820 0.1305 6.1097±0.1620 6.1641±0.1391 6.2935±0.1457 6.5092±0.2316 6.1731±0.1295 6.3518±0.1821 5.1590±0.7400 6.3828±0.1703 6.0951±0.1568 6.0679±0.1391 6.0957±0.1366 6.2102±0.146 6.0045±0.3251 0.5571 6.1741±0.1492 6.1248±0.1392 6.1684±0.1817 C12:0,%0.0016B 0.1645±0.0020C＜.0001**0.1835 0.1677±0.0017A 0.1712±0.0018 0.1768±0.0030B 0.0026**0.2133 0.169±0.0015A 0.1673±0.0024A 0.1989±0.0100B 0.0069**0.1794 0.1745±0.0021A 0.1676±0.0020C 0.1703±0.0017 0.0072**0.0883 0.1712±0.0016 0.1697±0.0018 0.1786±0.0049 0.1494 0.1716±0.0018 0.1689±0.0017 0.1687±0.0022 C11:0,%0.2523 56.9865±0.3164 57.2455±0.2729 57.6639±0.2843 57.8594±0.4752 0.1449 57.3355±0.2478 57.6605±0.3664 55.4830±1.5514 57.4468±0.3385 57.4101±0.3144 57.5354±0.2712 0.8887 57.3698±0.2622 57.0423±0.2871 56.3973±0.6774 0.1741 57.2315±0.2934 57.0650±0.2710 57.5732±0.3627 C10:0,%0.1066A 18.8330±0.1399B 0.0042**0.4179 19.0157±0.1164 19.2591±0.1234 19.4598±0.2118 0.0227*19.0281±0.1066A 19.6764±0.1610B 18.4707±0.7012＜.0001**0.2737 19.2419±0.1482 19.1574±0.1368 19.1133±0.1161 0.6456 19.0282±0.1124 19.0764±0.1241 18.8872±0.3054 0.7827 19.0166±0.1268 18.9851±0.1163 19.3227±0.1595 C8:0,%0.0794 14.0965±0.1040 0.8464 14.0468±0.0870 14.0184±0.0911 14.0374±0.1585 0.9442 14.0391±0.0782a 14.3151±0.1199b 14.6475±0.5272 14.0091±0.1119 14.1061±0.1012 13.9829±0.0864 0.4157 14.0441±0.0833 14.1213±0.0926 14.3172±0.2303 0.3646 14.0802±0.0941 14.1547±0.0872 14.1171±0.1204 C6:0,%0.0023 0.1887±0.0028 0.2854 0.1909±0.0025 0.1942±0.0026 0.1995±0.0042 0.0502 0.1894±0.0023A 0.2020±0.0032B 0.1718±0.0133＜.0001**0.0246*0.1904±0.0031 0.1883±0.0028 0.1894±0.0025 0.7788 0.1922±0.0024 0.1934±0.0026 0.1984±0.0058 0.4841 0.1937±0.0026b 0.1877±0.0025Aa 0.1961±0.0032B Genotype (No.)694)GA(200-250)P CC(397-486)CT(311-378)TT(58-70)P AA(672-805)AG(107-145)GG(4-5)P AA(157-183)CC(219-259)CA(395-494)P GG(476-569)GT(284-351)TT(20-31)P CC(284-344)CT(361-448)TT(127-148)SNP g.146725085 T＞C g.146726096A＞G g.146729107A＞C g.146735090G＞T g.146737188C＞T

C17:0,%C16:1,%C16:0,%C15:0,%C14:1,%0.0318*27.9350±0.1870a 27.5125±0.1534b 27.5136±0.1426b 0.0232*27.8452±0.1856a 27.4570±0.1426b 27.4242±0.1548b 0.0294*27.3959±0.1549 27.3368±0.1429a 27.7909±0.1867b 0.0181*27.8407±0.1859a 27.4284±0.1438b 27.4250±0.1552b 0.0258*27.8821±0.1855a 27.4459±0.1536b 27.4913±0.1430b 0.0260*27.4462±0.1556b 0.2209 2.2208±0.0288 2.2747±0.0230 2.2817±0.0210 0.0756 2.2365±0.0285 2.2852±0.0211 2.2865±0.0230 0.1604 2.2749±0.0231 2.2852±0.0211 2.2359±0.0289 0.2010 2.2448±0.0290 2.2917±0.0212 2.2935±0.0228 0.1878 2.2388±0.0284 2.2897±0.0229 2.2877±0.0211 0.1542 2.2781±0.0231 0.0682 30.7602±0.1992 30.3993±0.1579 30.3193±0.1449 0.0622 30.6795±0.1968 30.2417±0.1456 30.3295±0.1583 0.0634 30.2563±0.1585 30.1612±0.1454 30.5937±0.1985 0.0724 30.6096±0.1983 30.2380±0.1464 30.2822±0.1578 0.1374 30.7478±0.1974 30.3957±0.1581 30.3340±0.1453 0.0831 30.4044±0.1588 0.0888 67.4893±0.2187a 67.9662±0.1728 68.0117±0.1589b 0.0334*67.5538±0.2170 68.0054±0.1596 68.0076±0.1746 0.0646 68.0036±0.1752 68.1156±0.1595a 67.599±0.2189b 0.0434*67.5294±0.2172 67.9798±0.1605 67.9566±0.1740 0.0763 67.5382±0.2176 67.9870±0.1741 67.9939±0.1593 0.0673 67.9820±0.1743 0.0529 24.9182±0.2810 25.0728±0.2302 24.6877±0.2175 0.1064 24.9868±0.2789 24.8028±0.2166 25.1846±0.2305 0.1187 25.2872±0.2299a 24.8370±0.2170b 25.1958±0.2806 0.0389*25.1214±0.2807 24.9443±0.2164 25.3307±0.2299 0.1116 25.1146±0.2768 25.3034±0.2313 24.9351±0.2172 0.1358 25.2621±0.2307 Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C13:0,% C14:0,%0.6926 3.6997±0.0817 3.6694±0.0677 3.6470±0.0635 0.7494 3.6841±0.0805 3.6787±0.0634 3.6871±0.0681 0.9876 3.6784±0.0679 3.6433±0.0634 3.7417±0.0821 0.3874 3.6782±0.0813 3.6252±0.0634 3.6573±0.0683 0.7092 3.675±0.0810 3.6778±0.0683 3.6574±0.0634 0.9227 3.6822±0.0677 0.9103 6.2266±0.1817 6.2523±0.1485 6.1186±0.1391 0.5090 6.2361±0.1823 6.1569±0.1387 6.2670±0.1472 0.6416 6.2705±0.1497 6.1599±0.1381 6.2904±0.1813 0.5645 6.1986±0.1806 6.0816±0.1386 6.2251±0.1478 0.4621 6.2201±0.1802 6.2657±0.1487 6.1427±0.1386 0.5851 6.2878±0.1493 C12:0,%0.1887 0.1696±0.0023 0.1711±0.0018 0.1686±0.0017 0.3128 0.1711±0.0022 0.1698±0.0017 0.1725±0.0018 0.2563 0.1715±0.0018 0.1692±0.0017 0.1698±0.0023 0.3580 0.1668±0.0023 0.1662±0.0017 0.1694±0.0018 0.1390 0.1702±0.0023 0.1720±0.0018 0.1692±0.0017 0.2229 0.1703±0.0018 C11:0,%0.3152 57.9106±0.3655 57.4943±0.2900 57.3574±0.268 0.2603 57.7773±0.3626 57.2339±0.2677 57.2531±0.2940 0.2416 57.2827±0.2941 57.1311±0.2686 57.6388±0.3647 0.3216 57.5884±0.3617 57.1175±0.2712 57.2233±0.2916 0.3760 57.8855±0.3621 57.3779±0.2917 57.3551±0.2685 0.2644 57.3541±0.2931 C10:0,%0.0814 19.4701±0.1601 19.1472±0.1258 19.1421±0.1158 0.0816 19.3639±0.1597 19.0535±0.1157 19.0741±0.1267 0.1110 19.0461±0.1275 19.0311±0.1157 19.3218±0.1608 0.1496 19.2864±0.1606 18.9996±0.1170 18.9898±0.1260 0.1380 19.4421±0.1594 19.1122±0.1261 19.1531±0.1157 0.1021 19.0648±0.1269 C8:0,%14.0514±0.1204 14.0176±0.0944 14.0964±0.0863 14.0913±0.1188 14.0907±0.0871 14.0418±0.0941 14.0479±0.0944 14.1343±0.0867 14.1515±0.1209 14.1331±0.1199 14.0937±0.0864 14.0683±0.0950 14.0216±0.1184 13.9730±0.0943 14.0312±0.0863 14.0356±0.0944 C6:0,%0.0017**0.6908 0.1945±0.0032a 0.1931±0.0026A 0.1866±0.0025Bb 0.0017**0.6564 0.1979±0.0032a 0.1899±0.0025b 0.1954±0.0026a 0.0043**0.8370 0.1942±0.0026b 0.1884±0.0025Aa 0.1968±0.0032B 0.0022**0.5467 0.1950±0.0032A 0.1860±0.0025B 0.1926±0.0026A 0.0007**0.8640 0.1958±0.0031a 0.1941±0.0026A 0.1876±0.0025Bb 0.0014**0.7903 0.1934±0.0026a Genotype (No.)P AA(128-148)GG(283-343)GA(368-455)P CC(130-151)CT(366-455)TT(281-341)P CC(280-340)CT(369-458)TT(126-147)P GG(127-148)GT(363-452)TT(281-341)P CC(130-151)TT(282-342)CT(366-454)P CC(276-353)SNP g.146737545G＞A g.146737748 T＞C g.146737849C＞T g.146737879 T＞G g.146737916 T＞C g.146737946C＞

C17:0,%27.9048±0.1867Aa 27.4151±0.1428B 0.0073**26.4994±0.5087a 27.2869±0.1638b 27.5622±0.1357 0.0146*C16:1,%2.2291±0.0290 2.2787±0.0210 0.1645 2.3942±0.0853 2.2993±0.0251 2.2729±0.0197 0.1927 C16:0,%30.7658±0.1999 30.3468±0.1450 0.0783 29.7689±0.5866 30.2599±0.1723 30.5244±0.1358 0.1078 C15:0,%67.5357±0.2182 68.0108±0.1592 0.0567 69.0285±0.6442 68.2252±0.1883 68.0300±0.1500 0.1638 C14:1,%24.8679±0.2786 24.8255±0.2163 0.0533 24.8286±0.8041 24.4635±0.2501a 24.9766±0.2047b 0.0353*Table 2 Association between 17 SNPs and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C13:0,% C14:0,%3.7333±0.0830 3.6631±0.0633 0.6201 3.6632±0.2374 3.5945±0.0726 3.6781±0.0602 0.3532 6.2543±0.1810 6.1445±0.1379 0.4699 5.8460±0.5058 6.2627±0.1586 6.1662±0.1317 0.5865 C12:0,%0.1687±0.0022 0.1683±0.0017 0.4732 0.1754±0.0077 0.1674±0.0020 0.1702±0.0016 0.2047 C11:0,%57.7406±0.3642 57.2223±0.2683 0.3013 55.7325±1.0632 56.8884±0.3155 57.3907±0.2534 0.0632 C10:0,%19.3950±0.1605 19.0634±0.1159 0.0742 18.0083±0.4790 18.9628±0.1381 19.1422±0.1083 0.0258*C8:0,%14.0929±0.1198 14.1087±0.0864 13.9011±0.3614 14.1406±0.1041 14.1562±0.0803 0.7729 C6:0,%0.1956±0.0032A 0.1872±0.0025Bb 0.0015**0.6954 0.2017±0.0090 0.1868±0.0029a 0.1929±0.0023b 0.0149*Genotype (No.)TT(129-149)CT(372-460)P AA(8-12)AG(195-237)GG(578-702)P SNP T g.146738055G＞A Note:LSM least square mean.SE standard error.P indicates the significances of the association analysis between each SNP and milk fatty acid traits.P is the raw value.*:P＜0.05.**:P＜0.01.Different letter(small letters:P＜0.05;capital letters:P＜0.01)superscripts indicate significant differences among the genotypes.The number in the brackets represents the number of cows for the corresponding genotypes

Further,the additive(a),dominant(d),and allele substitution effects(α)of the 17 SNPs on each kind of fatty acid were calculated.Results showed that the 17 SNPs exhibited significant additive,dominant,and allele substitution effects on C6:0,C8:0,C10:0,C14:0,C16:0,C16:1,C17:0,C18:0,C18:1cis-9,C18index,C20:0,C14index,C16index,C17index,SFA,UFA,and total index(Table S2;P＜0.05).For C11:0,C12:0,C13:0,C14:1 and C15:0,none of significant additive,dominant,and allele substitution effects was found(P＞0.05).

Also,association analysis on two haplotype blocks with 24 milk FAs was performed(Table 3).The haplotype blcok1 was significantly associated with C6:0,C8:0,C10:0,C14:0,C18:0,C20:0,C17index and total index(P＜0.0001-0.0245),and the block 2 was strongly associated with C6:0,C8:0,C10:0,C14:0,C18:0,C17:1,C18:1cis-9,C18index,C20:0,C16index,C17index,SFA,UFA and total index(P＜0.0001-0.0498;Table 3).While,no significant association was found for C11:0,C12:0,C13:0,C14:1,C15:0,C16:0,C16:1,C14index and SFA/UFA(P＞0.05).

Prediction of TFBSs changing caused by the SNPs in 5′regulatory region

By performing Genomatix software suite v3.9,it was predicted that that four SNPs in the 5′regulatory region ofAGPAT3gene,g.146702957G＞A,g.146704373A＞G,g.146704618A＞G and g.146704699G＞A altered the binding sites of some transcription factors(Table 4).The allele A of g.146702957G＞A created a TFBS for SMAR CA3(SWI/SNF related,matrix associated,actin dependent regulator of chromatin,subfamily a,member 3)and REX1(REX1 transcription factor;zinc finger protein 42),respectively,and the allele G created a TFBS for VMYB(v-Myb,variant of AMV v-myb).The alleles A and G of g.146704373A＞G created a TFBS for BRACH(Brachyury)and NKX26(NK2 homeobox 6,Csx2),respectively.The allele G of g.146704618A＞G created two TFBSs for ZBED4(Zinc finger,BED-type containing 4;GC-box binding sites)and SP1(Stimulating protein 1,ubiquitous zinc finger transcription factor).The allele G of g.146704699G＞A created two TFBSs for USF1(Upstream stimulating factor 1)and ARNT(AhR nuclear translocator homodimers),and the allele A created a TFBS for FOXA1(Forkhead box protein A1,hepatocyte nuclear factor 3-alpha(HNF-3-alpha)).

Exploring for luciferase activity altered by the SNPs in 5′regulatory region

To validate the TFBS prediction results,the luciferase assay was further performed for the four SNPs(g.146702957G＞A,g.146704373A＞G,g.146704618A＞G and g.146704699G＞A)(Fig.1b).We observed that the luciferase activities of six constructs containing g.146704373A＞G,g.146704618A＞G,and g.146704699G＞A,were significantly higher than that of the pGL4.14 empty vector(P＜0.0007)and blank control(P＜0.0008),while g.146702957G＞A did not(P＞0.05).Further,the luciferase activity of alleles A of g.146704373A＞G and g.146704618A＞G were significantly higher than that of their alleles G(P=0.0004;Fig.1b).The luciferase activity of allele G of g.146704699G＞A was higher than that of allele A,while not significant(P＞0.05).These results indicated that the transcriptional activity of theAGPAT3gene significantly altered by g.146704373A＞G and g.146704618A＞G might be the reasons strongly impacted on FAs.

Discussion

This study was a follow-up investigation for our previous GWAS on milk FAs in Chinese Holstein[12].AGPAT3is involved in pathways related to lipid metabolism(ko00561,ko00564 and ko04072).In human,docosapentaenoic acid as the substrate ofAGPAT3protein transfers a fatty acid insn-2 position of lysophosphatic acid,a step in the phospholipid biosynthesis pathway[19].Here,we detected that theAGPAT3gene mainly impacted the medium-chain milk FAs in dairy cattle.

Mammalian AGPAT catalyzed the acylation of lysophosphatidic acid to form the phosphatidic acid,which was the precursor of all glycerplipids[14].For the AGPAT families,AGPAT1had significant association with milk FA CLA[20],andAGPAT6was strongly associated with C14:0,C16:0,C10:1,C12:1,C14:1 and C16:1[21].In our previous GWA studies[12,13],AGPAT3gene was identified as a candidate for two milk FAs,C18index and C18:0.In this study,using an independent Chinese Holstein population that was different from the previous GWA studies,we also observed thatAGPAT3showed a significant genetic effect on C18index and C18:0.In addition,our results revealed that theAGPAT3had strong associations with C6:0,C8:0 and C10:0.Overall,the previous GWASs and this study suggested thatAGPAT3gene had significantly genetic effects on milk FAs.

C16:1,% C17:0,%0.5739±0.0044 0.5767±0.0037 0.5656±0.0039 0.5686±0.0048 0.5694±0.0042 0.5689±0.0056 0.0962 0.5703±0.0045 0.5671±0.0058 0.5652±0.0062 0.5744±0.0043 0.5593±0.0049 0.5769±0.0048 0.5783±0.0056 0.0193*%27.6878±0.1931a 27.5081±0.1649 27.4224±0.1743 27.6407±0.2079 1.3602±0.0336 1.3269±0.0292 1.3176±0.0303 1.2820±0.0363 1.3081±0.0326 1.3749±0.0428 0.3084 1.3447±0.0344 1.3754±0.0439 1.4103±0.0465 1.3455±0.0335 1.3358±0.0376 1.3292±0.0373 1.4043±0.0426 0.4760 SFA/UFA Total index,2.2565±0.0304 2.2675±0.0255 2.2838±0.0269 2.2608±0.0330 C16:0,%C15:0,%34.9164±0.2400 34.8598±0.2082 34.7483±0.2175 34.7264±0.2591 35.0655±0.2324 34.8421±0.3116 0.8036 34.9810±0.2481 34.7801±0.3163 34.2739±0.3353 34.7218±0.2397 35.2877±0.2714 34.9866±0.2709 34.6563±0.3081 0.1205 UFA,%0.9978±0.0175 0.9938±0.0149 1.0068±0.0153 0.9853±0.0190 0.9835±0.0166 0.9895±0.0227 0.8195 0.9918±0.0176 0.9893±0.0231 1.0077±0.0243 0.9760±0.0171 0.9821±0.0192 0.9990±0.0193 0.9807±0.0223 0.8845 SFA,%30.6829±0.2089 30.4159±0.1766 30.1895±0.1868 30.3912±0.2271 67.7674±0.2297 67.9734±0.1925 68.0604±0.2014 67.9786±0.2486 Table 3 Association between haplotype blocks in AGPAT3 and milk fatty acid traits in Chinese Holstein cows(LSM±SE)C14:1,%0.6359±0.0253 0.6296±0.0213 0.6786±0.0225 0.6575±0.0274 0.6483±0.0243 0.6629±0.0329 0.3696 0.6361±0.0253 0.6793±0.0335 0.6664±0.0353 0.6586±0.0250 0.6550±0.0283 0.6403±0.0284 0.6521±0.0319 0.9124 C17index,%25.0181±0.2938 24.6437±0.2483 24.9275±0.2620 24.2275±0.3182a C13:0,% C14:0,%10.1573±0.0917 10.2724±0.0804 10.3997±0.0839a 10.3001±0.0986 10.1961±0.0869 10.0632±0.1132b 0.0105*10.1137±0.0946 9.9868±0.1182 10.3528±0.1232 10.0997±0.0927 10.0970±0.1008 10.3642±0.1024 10.0688±0.1129 0.0127*C16index,%3.7792±0.0860 3.6878±0.0747 3.6750±0.0774 3.5740±0.0928 0.1020±0.0043 0.0983±0.0035 0.1005±0.0037 0.0987±0.0046 0.0956±0.0041 0.0978±0.0056 0.8544 0.0962±0.0043 0.0984±0.0057 0.1029±0.0061 0.0967±0.0042 0.0950±0.0048 0.1005±0.0048 0.0982±0.0056 0.9225 C14index,%6.0996±0.1885 5.9684±0.1634 6.3161±0.1697 6.1465±0.2072 C11:0,% C12:0,%2.9643±0.0538 2.9761±0.0470 3.0334±0.0487 2.9978±0.0575 2.9299±0.0521 3.0030±0.0657 0.4138 3.0485±0.0549 2.9637±0.0688 3.0676±0.0718 2.9456±0.0546 2.9215±0.0594 2.9780±0.0594 2.9783±0.0671 0.2555 C20:0,%0.1791±0.0025Aad 0.1710±0.0020ABc 0.1635±0.0022Bb 0.1731±0.0025Aac 0.0579±0.0041 0.0569±0.0038 0.0596±0.0039 0.0594±0.0043 0.0575±0.0040 0.0597±0.0048 0.9269 0.0580±0.0042 0.0575±0.0050 0.0631±0.0053 0.0579±0.0042 0.0611±0.0045 0.0580±0.0045 0.0575±0.0049 0.8887 C18index,%57.4699±0.3816 56.9474±0.3225 57.3701±0.3377 57.5431±0.4143 C10:0,%2.7932±0.0413a 2.7610±0.0367A 2.9065±0.0373Bb 2.8120±0.0436 2.7551±0.0400A 2.8116±0.0513 0.0001**2.8297±0.0425a 2.8139±0.0519 2.8422±0.0552 2.7289±0.0417 2.6966±0.0455 2.6801±0.0452b 2.7312±0.0510 0.0013**C18:1cis-9,%19.3463±0.1683 19.1954±0.1414 19.0385±0.1501 19.2225±0.1833 C8:0,%0.8989±0.0140ACa 0.8923±0.0122Aa 1.0162±0.0128Bb 0.9044±0.0152ACDac 0.9441±0.0134CDcd 0.9642±0.0177BDd＜.0001**0.9932±0.0145Aa 1.0604±0.0180B 0.9280±0.0192ACd 0.8735±0.0140Cde 0.8930±0.0155Cbd 0.8165±0.0155Dc 0.8526±0.0179Cbce＜.0001**C18:0,%14.1901±0.1259 14.3563±0.1050a 13.9560±0.1103b 14.0036±0.1391 C6:0,%0.3692±0.0166A 0.4422±0.0142B 0.5563±0.0146C 0.4381±0.0175B 0.4610±0.0157B 0.5532±0.0206C＜.0001**0.5694±0.0167Aa 0.6103±0.0213Da 0.4357±0.0224Bbc 0.3787±0.0166Cbe 0.5078±0.0181Ac 0.4629±0.0187Fc 0.3384±0.0204Ede＜.0001**C17:1,%0.1992±0.0034 0.1904±0.0029 0.1926±0.0030 0.1912±0.0036 Haplotype combination(No.)H1H1(121-130)H1H2(218-240)H1H3(191-204)H2H2(98-103)H2H3(136-150)H3H3(66-72)P H1H2(126-139)H1H3(56-62)H1H4(52-55)H2H2(138-149)H2H3(95-102)H2H4(96-107)H2H5(63-68)P Haplotype combination(No.)H1H1(105-130)H1H2(193-241)H1H3(167-204)H2H2(93-103)Haplotype block Block 1 Block 2 Haplotype block Block 1

C16:1,% C17:0,%27.0443±0.1860b 27.5612±0.2436 0.0245*27.5466±0.1948 27.5735±0.2497 27.8206±0.2639 27.8762±0.1924A 27.0508±0.2145B 27.0983±0.2132Ca 28.0083±0.2341ACb 0.0001**2.2977±0.0289 2.2496±0.0391 0.7634 2.2848±0.0307 2.2858±0.0404 2.2085±0.0432 2.2254±0.0299 2.3174±0.0344 2.3011±0.0339 2.2230±0.0391 0.0574 C16:0,%30.1933±0.1991 30.6459±0.2693 0.1757 30.3523±0.2134 30.2962±0.2735 30.8744±0.2977 30.7484±0.2092a 29.8952±0.2352b 30.0572±0.2341 30.8806±0.2665a 0.0015**Table 3 Association between haplotype blocks in AGPAT3 and milk fatty acid traits in Chinese Holstein cows(LSM±SE)(Continued)C15:0,%68.2035±0.2194 67.7284±0.2962 0.5462 67.9941±0.2332 67.9988±0.3013 67.3613±0.3257 67.4990±0.2285 68.2658±0.2556 68.1996±0.2544 67.4171±0.2947 0.0096**C14:1,%24.3598±0.2842a 25.6261±0.3649b 0.0053**24.8359±0.2983a 25.4165±0.3779 25.5598±0.4005 25.0985±0.2912 24.7605±0.3257a 24.6392±0.3234a 25.9925±0.368b 0.0075**C13:0,% C14:0,%3.6158±0.0830 3.8032±0.1081 0.2434 3.6796±0.0878 3.7758±0.1121 3.9414±0.1180 3.6936±0.0856 3.6030±0.0958 3.6211±0.0957 3.8763±0.1080 0.0498*6.0705±0.1814 6.3488±0.2396 0.3560 6.1665±0.1940 6.6035±0.2487 6.2159±0.2621 6.2908±0.1912 6.3426±0.2113 6.0775±0.2131 6.1397±0.2402 0.5789 C11:0,% C12:0,%0.1697±0.0023Abc 0.1669±0.0031Bbc＜.0001**0.1705±0.0024 0.1698±0.0030 0.1771±0.0035A 0.1679±0.0024 0.1714±0.0028 0.1622±0.0028B 0.1644±0.0033 0.0020**56.5368±0.3665 57.8332±0.4902 0.0649 57.4628±0.3893 57.1552±0.5004 58.2375±0.5405 57.7791±0.3817 56.6882±0.4257 56.6694±0.4235 57.5823±0.4896 0.0345*C10:0,%18.8881±0.1604 19.1521±0.2201 0.214 19.0344±0.1692 19.0365±0.2254 19.3446±0.2403 19.4270±0.166a 18.8617±0.1897 18.7635±0.1887b 19.5612±0.2164a 0.0036**C8:0,%14.2354±0.1206 13.8767±0.1663 0.0051**14.1418±0.1267 14.0962±0.1695 13.7531±0.1790 14.1252±0.1253 14.0980±0.1432 14.3357±0.1411 14.3662±0.1625 0.0798 C6:0,%0.1922±0.0032 0.2002±0.0043 0.0513 0.1873±0.0034A 0.1908±0.0043 0.1991±0.0046 0.1973±0.0034 0.1877±0.0037A 0.1877±0.0038a 0.2051±0.0042Bb＜.0001**Haplotype combination(No.)H2H3(119-150)H3H3(61-72)P H1H2(117-139)H1H3(59-62)H1H4(44-55)H2H2(128-149)H2H3(80-102)H2H4(88-107)H2H5(50-69)P Haplotype block Block 2 Note:LSM least square mean.SE standard error.P indicates the significances of the association analysis between the haplotype block and milk fatty acid traits.P is the raw value.*:P＜0.05.**:P＜0.01.Different letter(small letters:P＜0.05;capital letters:P＜0.01)superscripts indicate significant differences among the haplotype combinations.The number in the brackets represents the number of cows for the corresponding haplotype combination

Table 4 Changes of transcription factor binding site(TFBS)caused by the SNP in the 5′untranslated(UTR)and flanking regions of AGPAT3

Sequences-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that affects gene expression and hence heritable phenotypic variation[22,23].Eukaryotic regulatory sequences,including enhancers and promoters,are typically between a hundred and several thousand base pairs in length,and can harbor many TFBSs[24].It is essential to understand the evolution dynamics of transcription factor binding for understanding the evolution of gene regulation[25].In this study,by prediction,g.146704373A＞G changed the bindings of transcription factors(TFs)BRACH and NKX26,and g.146704618A＞G altered the bindings of TFs ZBED4 and SP1.Further,we used the luciferase assay to verify that the alleles A of g.146704373A＞G and g.146704618A＞G strongly increased the transcription activity of theAGPAT3gene than the alleles G.Previous studies showed that BRACH as a regulatory factor directly activated downstream mesoderm-specific genes to exert its mesoderminducing effects[26],and NKX26 restrained the transcription activity ofCx40through the F151L missense mutation to impact the heart development[27].ZBED4 could act as a co-repressor of nuclear hormone receptors(NHRs)by its LXXLL motifys in cones[28].Through interfering with the recruitment of SP1 toZNF132promoter region,methylation of SP1-binding site can inhibitZNF132transcriptional expression to impact the tumor in the development of esophageal squamous cell carcinoma[29].These reports have indicated that the TFs BRACH,NKX26,ZBED4 and SP1 could activate or repress the expression of their target genes.Based on our association analysis,the cows with the AA genotypes of g.146704373A＞G and g.146704618A＞G ofAGPAT3,yielded significantly lower contents of C6:0 and C8:0 than those with GG genotypes.According to above,we deduced that the BRACH as a TF might activateAGPAT3gene transcription activity by binding to the allele A of g.146704373A＞G thereby reducing the contents of C6:0 and C8:0,while,the transcription factors NKX26,ZBED4 and SP1 might have the contrary effects.

Nowadays,genomic selection is the main implication for dairy cattle breeding,where the genomic chips are used.Among the SNP markers in these chips,most of them were collected from the current SNP database and almost evenly distributed across the whole genome.Hence,g.146704373A＞G and g.146704618A＞G ofAGPAT3as the potentially causal mutations could be put into the SNP chip instead of used in marker selection to increase selection efficiency in some specific dairy cattle populations to improve the contents of milk FAs.

Conclusion

In conclusion,through a post-GWAS approach,our study firstly indicated there were significant genetic associations between theAGPAT3gene and milk FAs in dairy cattle.Further,we found that two SNPs in 5′regulatory region(g.146704373A＞G and g.146704618A＞G)changed the transcriptional activity ofAGPAT3implying their potential causal function.These findings provided important molecular information for dairy cattle breeding.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s40104-020-00540-4.

Additional file 1:Table S1.PCR primers information of AGPAT3 gene

Additional file 2:Table S2.Additive(a),dominant(d)and allele substitution(α)effects of 17 SNPs on milk fatty acid traits of AGPAT3 gene in Chinese Holstein cows

Abbreviations

a:Additive;AGPAT:1-Acylglycerol-sn-glycero 3-phosphate acyltransferase;AGPAT3:1-Acylglycerol-3-phosphate O-acyltransferase 3;ARNT:AhR nuclear translocator homodimers;BRACH:Brachyury;d:Dominant;FA:Fatty acid;FOXA1:Forkhead box protein A1,hepatocyte nuclear factor 3-alpha(HNF-3-alpha);GWAS:Genome-wide association study;HEK:Human embryonic kidney;LD:Linkage disequilibrium;NKX26:NK2 homeobox 6,Csx2;PCR:Polymerase chain reaction;REX1:REX1 transcription factor;zinc finger protein 42;SFA:Saturated fatty acids;SMARCA3:SWI/SNF related,matrix associated,actin dependent regulator of chromatin,subfamily a,member 3;SNP:Single nucleotide polymorphism;SP1:Stimulating protein 1,ubiquitous zinc finger transcription factor;TFBS:Transcription factor binding site;UFA:Unsaturated fatty acid;USF1:Upstream stimulating factor 1;UTR:Untranslated region;VMYB:v-Myb,variant of AMV v-myb;ZBED4:Zinc finger,BED-type containing 4,GC-box binding sites;α:Substitution

Acknowledgements

We appreciate Beijing Dairy Cattle Center and Beijing Sanyuanlvhe Dairy Farming Center for providing the milk,blood,and semen samples of Chinese Holstein.

Authors’contributions

DS and YY conceived and designed the experiments,LL prepared the DNA samples for SNP identification and genotyping with the help of XW,ZM,XL,YL,and FZ,XL measured the phenotypes of milk fatty acids,LS and XW analyzed the data,and LS,BH and DS prepared the manuscript.All authors read and approved the final manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China(31872330,31802041),Beijing Science and Technology Program(20200105,D171100002417001),earmarked fund for Modern Agroindustry Technology Research System(CARS-36),and the Program for Changjiang Scholar and Innovation Research Team in University(IRT_15R62).

Availability of data and materials

All relevant data are available within the article and its supplementary information.

Ethics approval and consent to participate

All protocols for collection of the samples of experimental individuals and phenotypic observations were reviewed and approved by the Institutional Animal Care and Use Committee(IACUC)at China Agricultural University(Permit Number:DK996).Milk,blood and semen samples were collected specifically for this study following standard procedures with the full agreement of the Beijing Sanyuanlvhe Dairy Farming Center who owned the Holstein cows and bulls,respectively.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

1Department of Animal Genetics,Breeding and Reproduction,College of Animal Science and Technology,Key Laboratory of Animal Genetics,Breeding and Reproduction of Ministry of Agriculture and Rural Affairs,National Engineering Laboratory for Animal Breeding,China Agricultural University,No.2 Yuanmingyuan West Road,Haidian District,Beijing 100193,China.2Institute of Animal Science,Chinese Academy of Agricultural Sciences,Beijing 100193,China.3Beijing General Station of Animal Husbandry,Beijing 100101,China.4Beijing Dairy Cattle Center,Beijing 100192,China.

Received:25 June 2020 Accepted:14 December 2020