Open Access

Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility

  • Xiao Wang1, 2,
  • Peipei Ma1, 2,
  • Jianfeng Liu1,
  • Qin Zhang1,
  • Yuan Zhang1,
  • Xiangdong Ding1,
  • Li Jiang1,
  • Yachun Wang1,
  • Yi Zhang1,
  • Dongxiao Sun1,
  • Shengli Zhang1,
  • Guosheng Su2 and
  • Ying Yu1Email author
BMC Genetics201516:111

https://doi.org/10.1186/s12863-015-0263-3

Received: 23 March 2015

Accepted: 13 August 2015

Published: 15 September 2015

Abstract

Backgrounds

Bovine mastitis is a typical inflammatory disease causing seriously economic loss. Genome-wide association study (GWAS) can be a powerful method to promote marker assistant selection of this kind of complex disease. The present study aimed to analyze and identify single nucleotide polymorphisms (SNPs) and candidate genes that associated with mastitis susceptibility traits in Chinese Holstein.

Results

Forty eight SNPs were identified significantly associated with mastitis resistance traits in Chinese Holstein cows, which are mainly located on the BTA 14. A total of 41 significant SNPs were linked to 31 annotated bovine genes. Gene Ontology and pathway enrichment revealed 5 genes involved in 32 pathways, in which, TRAPPC9 and ARHGAP39 genes participate cell differentiation and developmental pathway together. The six common genome-wide significant SNPs are found located within TRAPPC9 and flanking ARHGAP39 genes.

Conclusions

Our data identified the six SNPs significantly associated with SCS EBVs, which suggest that their linked two genes (TRAPPC9 and ARHGAP39) are novel candidate genes of mastitis susceptibility in Holsteins.

Keywords

Genome-wide association study EBVs of somatic cell scores Chinese Holstein cows Mixed model based single locus regression analysis Mastitis susceptibility

Background

Bovine mastitis is one of the most typical inflammatory diseases causing seriously economic loss in modern dairy farms and quality problems of dairy food worldwide [1]. Since the heritability of mastitis is low, genetic improvement on anti-mastitis by traditional selection is not very effective [2]. Moreover, it is not easy to measure mastitis in field scale. Somatic cell count (SCC) or log transformed SCC (somatic cell score, SCS) have relatively higher heritability compared to mastitis and are used as the first trait to improve mastitis resistance [3]. In addition, to avoid uncertain influences such as farms, seasons, sires and etc., estimated breeding values (EBVs) of somatic cell scores (SCSs) were normally used as pseudo-phenotypes of mastitis related traits in dairy cattle. Genome-wide association study (GWAS) is widely considered a potential method to promote marker assisted selection of mastitis related traits based on single nucleotide polymorphism (SNP) [4].

The previous GWAS for mastitis susceptibility showed multifarious results in different Holstein populations. Family-based association tests such as single locus regression analysis and transmission disequilibrium test have the robust advantage to population heterogeneity [5]. In 2011, Sodeland’s group detected QTLs for clinical mastitis on Bos taurus autosome (BTA) 2, 6, 14, and 20 in Norwegian red cattle [6]. In 2012, Meredith et al. reported that 9 SNPs located on BTA 6, 10, 15 and 20 were significantly associated with SCSs in Holstein sires and cows [7]. The same year, Wijga et al. [8] reported that SNPs relevant to log transformed lactation-average somatic cell scores or the standard deviation of test-day somatic cell score were mainly located on BTA 4, 6 and 18. In addition, strong associations of SNPs with clinical mastitis and SCS were reported on bovine BTA 6, 13, 14 and 20 in Nordic Holstein cattle by Sahana et al. [9]. Recently, GWAS performed in German Holstein cows identified significant SNPs on BTA 6, 13, 19 and X [10]. The studies in US Holstein dairy cows have shown that genetic variants on BTA 2, 14, 20 have impacts on clinical mastitis. The identified region on BTA 14 contains lymphocyte-antigen-6 complex (LY6) including LY6K, LY6D, LYNX1, LYPD2, SLURP1, PSCA genes in regulating the major histocompatibility complex [11]. The studies in Chinese population containing Chinese Holstein, Sanhe cattle and Chinese Simmental have analyzed that TLR4 gene (Toll-like receptor 4) and BRCA1 gene (Breast cancer 1) have the significant association with SCS [12, 13]. Even though many studies have identified significant SNPs, only one SNP (BTA-77077-no-rs, Position: 85527109) on BTA 6 was identical in the reports of Sahana et al. [9] and Abdel-Shafy et al [10]. These results implied that the significant SNPs associated with mastitis traits were not identified consistently and should be confirmed and validated in different Holstein populations.

In order to detect functional candidate genes for mastitis-related traits, GWAS was conducted with mixed model based single locus regression analysis (MMRA) in Chinese Holstein populations. Six common SNPs were identified by MMRA and two linked genes were disclosed with significant effects on mastitis-related traits in Chinese Holstein populations.

Results

Significant SNPs associated with SCSs EBVs

The –log10 P of all tested SNPs for SCS EBVs with MMRA is shown in Fig. 1. The significant SNPs associated with SCS EBVs were mainly located on BTA 14.
Fig. 1

Manhattan plots of genome-wide association for SCS EBVs

The genomic association SNPs detected by MMRA were presented in Table 1. In total, 48 significant SNPs on chromosome level were detected including 13 SNPs on genome level. As shown in Table 1, 41 out of 48 SNPs were located within or near 31 known genes.
Table 1

Chromosome-wide significant SNPs for SCS EBVs

SNP name

Chr.

Position(bp)

Nearest genesa

Distance(bp)

P-values

ARS-BFGL-NGS-32524

0b

NA

NA

NA

4.79E-07

ARS-BFGL-NGS-18858

0b

NA

NA

NA

7.00E-07

BFGL-NGS-114657

0b

NA

NA

NA

6.61E-07

ARS-BFGL-NGS-91137

0b

NA

NA

NA

1.48E-05

ARS-BFGL-NGS-60730

0b

NA

NA

NA

3.41E-05

ARS-BFGL-NGS-103637

1

59166287

SIDT1

within

4.13E-06

ARS-BFGL-NGS-2950

1

84528381

MAGEF1

152817

1.71E-05

Hapmap42708-BTA-86534

3

50852627

RWDD3

596180

1.15E-06

ARS-BFGL-NGS-55261

3

2281390

ILDR2

212007

1.91E-05

Hapmap32072-BTA-142491

4

106961853

TBXAS1

16314

1.13E-05

Hapmap51299-BTA-73473

5

47059558

RAB3A

86426

7.44E-06

ARS-BFGL-NGS-104108

5

71073538

IGF1

52675

1.48E-05

BTB-01491979

8

107025584

LOC534155

130161

2.22E-05

BTB-00391421

9

50410127

GRIK2

within

1.08E-05

Hapmap51481-BTA-67522

9

49607152

GRIK2

375591

1.51E-05

BTB-00391456

9

50434277

GRIK2

within

2.49E-05

ARS-BFGL-NGS-3540

11

68044963

C1D

359533

6.99E-07

Hapmap39693-BTA-85506

11

15115923

MEMO1

51033

1.09E-06

ARS-BFGL-BAC-14940

11

67828555

ETAA1

173499

1.42E-06

Hapmap31821-BTA-156670

13

4956832

NA

NA

1.14E-05

ARS-BFGL-NGS-100480

14

2607583

TRAPPC9

within

1.24E-10

ARS-BFGL-NGS-4939

14

443937

ARHGAP39

258178

9.97E-10

ARS-BFGL-NGS-107379

14

679600

ARHGAP39

460

1.63E-09

ARS-BFGL-NGS-57820

14

236532

ARHGAP39

50773

1.97E-09

ARS-BFGL-NGS-56327

14

2580414

TRAPPC9

within

3.29E-08

UA-IFASA-5306

14

2711615

TRAPPC9

within

3.64E-08

UA-IFASA-9288

14

2201870

PTK2

within

8.29E-08

ARS-BFGL-NGS-18365

14

741867

MAPK15

111034

2.77E-06

BFGL-NGS-113575

14

2484499

TRAPPC9

within

1.08E-05

BFGL-NGS-111902

14

65409003

TSPYL5

370903

1.86E-05

ARS-BFGL-NGS-104701

16

56834152

GLRX

191565

2.73E-05

ARS-BFGL-BAC-33744

19

34229778

NCOR1

within

4.14E-05

ARS-BFGL-NGS-44441

20

13114376

CD180

31009

3.59E-06

ARS-BFGL-NGS-106084

21

57855394

ITPK1

180441

5.48E-06

ARS-BFGL-NGS-61681

21

30197672

CHRNA7

499598

5.65E-06

ARS-BFGL-NGS-41216

21

25613731

BCL2A1

141178

1.12E-05

ARS-BFGL-NGS-7344

21

42702373

G2E3

521371

1.54E-05

ARS-BFGL-NGS-39846

27

36421058

PLEKHA2

12209

5.81E-06

ARS-BFGL-NGS-71055

27

37589834

IDO1

198717

8.77E-06

ARS-BFGL-NGS-29650

27

36946859

IDO1

431343

1.55E-05

ARS-BFGL-NGS-108861

27

37445592

IDO1

54475

4.96E-05

UA-IFASA-6255

28

41464821

BMPR1A

within

3.80E-05

BTB-01016631

29

28085086

SAA2

355019

5.76E-06

ARS-BFGL-NGS-12475

29

21777960

LUZP2

47926

9.44E-06

BTB-01337464

29

29072341

NA

NA

3.04E-05

Hapmap56639-rs29021780

X

2460976

GRIA3

within

1.34E-07

Hapmap57012-rs29019338

X

12135331

F9

821885

2.85E-06

ARS-BFGL-NGS-94205

X

2348904

GRIA3

within

8.47E-05

NA: not available

aDerived from UCSC Genome Bioinformatics (http://genome.ucsc.edu/cgi-bin/hgBlat?command=start)

bThese SNPs are not assigned to any chromosomes and noted as “0”

In the thirteen genome-wide significant SNPs, ARS-BFGL-NGS-100480 was located within TRAPPC9 gene (trafficking protein particle complex 9) on BTA 14 and showed lowest P-values of 1.24E-10. Two other significant SNPs, ARS-BFGL-NGS-56327 and UA-IFASA-5306 located within TRAPPC9 gene, were detected with P-values of 3.29E-08, and 3.64E-08, respectively. In addition, three other significant SNPs were identified linked with ARHGAP39 gene (Rho GTPase activating protein 39) (Table 2).
Table 2

Genome-wide significant SNPs with genome annotations

SNP name

Chr.

Nearest genesa

P-values

Name

Distance(bp)

Full name

ARS-BFGL-NGS-32524

0b

NA

NA

NA

4.79E-07

ARS-BFGL-NGS-18858

0b

NA

NA

NA

7.00E-07

BFGL-NGS-114657

0b

NA

NA

NA

6.61E-07

ARS-BFGL-NGS-3540

11

C1D

359533

C1D nuclear receptor corepressor

6.99E-07

Hapmap39693-BTA-85506

11

MEMO1

51033

mediator of cell motility 1

1.09E-06

ARS-BFGL-NGS-100480

14

TRAPPC9

within

trafficking protein particle complex 9

1.24E-10

ARS-BFGL-NGS-4939

14

ARHGAP39

258178

Rho GTPase activating protein 39

9.97E-10

ARS-BFGL-NGS-107379

14

ARHGAP39

460

Rho GTPase activating protein 39

1.63E-09

ARS-BFGL-NGS-57820

14

ARHGAP39

50773

Rho GTPase activating protein 39

1.97E-09

ARS-BFGL-NGS-56327

14

TRAPPC9

within

trafficking protein particle complex 9

3.29E-08

UA-IFASA-5306

14

TRAPPC9

within

trafficking protein particle complex 9

3.64E-08

UA-IFASA-9288

14

PTK2

within

PTK2 protein tyrosine kinase 2

8.29E-08

Hapmap56639-rs29021780

X

GRIA3

within

glutamate receptor, ionotrophic, AMPA 3

1.34E-07

NA not available

aDerived from UCSC Genome Bioinformatics (http://genome.ucsc.edu/cgi-bin/hgBlat?command=start)

bThese SNPs are not assigned to any chromosomes and noted as “0”

Linkage disequilibrium (LD) blocks of the significant SNPs on BTA 14

Linkage disequilibrium analysis for the total ten significant SNPs on BTA 14 showed two LD blocks (Fig. 2). Two significant SNPs (ARS-BFGL-NGS-57820 and ARS-BFGL-NGS-4939) in the block 1 were located on the upstream of ARHGAP39 gene, and three significant SNPs (BFGL-NGS-113575, ARS-BFGL-NGS-56327 and ARS-BFGL-NGS-100480) in the block 2 were located within TRAPPC9 gene.
Fig. 2

Linkage disequilibrium (LD) pattern for 10 significant SNPs on BTA 14. Solid line triangles refer to linkage disequilibrium (LD). One square refers to LD level (r2) between two SNPs and the squares are colored by D’/LOD standard scheme (LOD is the logarithm of likelihood odds ratio and the reliable index to measure D’). D’/LOD standard scheme is that red refers to LOD > 2, D’ = 1; pink refers to LOD > 2, D’ < 1; blue refers to LOD < 2, D’ = 1; white refers to LOD < 2, D’ < 1

Two candidate genes for mastitis-related traits

TRAPPC9 and ARHGAP39 genes (each contains three significant SNPs on genome level) identified by MMRA can be considered potential candidate genes for mastitis-related traits. To decipher the effect of each genotype in each potential candidate gene on mastitis-related traits, the SCS EBVs of the cows with three genotypes were compared. As shown in the left panel of the Fig. 3, the cows with genotype AA in the two genes all owned significant higher SCS EBVs compared to the other genotypes (P < 0.001). These results appropriately confirmed the two genes (TRAPPC9 and ARHGAP39) as potential candidate genes for SCS EBVs. The right panel of the Fig. 3 showed the average original phenotypic SCC of the cows with three genotypes for each gene fluctuated with the days in milk (DIM). It was displayed that the cows with genotype AA had a tendency of higher SCC along DIM than the other two genotypes for the two genes especially for TRAPPC9 gene (Fig. 3).
Fig. 3

The SCS EBVs and curves of SCC in different genotypes of TRAPPC9 and ARHGAP39 genes. **refers to P < 0.001

Gene ontology and pathway enrichment for the significant SNPs on genome level

Through the Gene Ontology (GO) analysis of GenCLiP 2.0 (http://ci.smu.edu.cn/GenCLiP2.0/analysis.php?random=new), we found that 5 genes perform mainly functions in 32 pathway terms presented in Table 3 and Fig. 4. Through enrichment of five genes, ARHGAP39 gene can totally participate 24 pathway terms including two pathway terms combined with TRAPPC9 gene (GO:0030154 and GO:0048869), which influence cell differentiation or cellular developmental process.
Table 3

Results of GO analysisa

Pathway

Hit

Total

P-Value

Q-Value

Gene

List

axon guidance

2

360

0.004

0.357

ARHGAP39;PTK2

GO:0007411

Taxis

2

608

0.012

0.165

ARHGAP39;PTK2

GO:0042330

regulation of small GTPase mediated signal transduction

2

425

0.006

0.247

ARHGAP39;PTK2

GO:0051056

Axonogenesis

2

517

0.009

0.241

ARHGAP39;PTK2

GO:0007409

cell morphogenesis involved in neuron differentiation

2

568

0.010

0.217

ARHGAP39;PTK2

GO:0048667

neuron projection morphogenesis

2

576

0.011

0.179

ARHGAP39;PTK2

GO:0048812

neuron projection development

2

703

0.016

0.101

ARHGAP39;PTK2

GO:0031175

Chemotaxis

2

608

0.012

0.142

ARHGAP39;PTK2

GO:0006935

small GTPase mediated signal transduction

2

676

0.015

0.135

ARHGAP39;PTK2

GO:0007264

cell projection morphogenesis

2

689

0.015

0.126

ARHGAP39;PTK2

GO:0048858

cell part morphogenesis

2

701

0.016

0.118

ARHGAP39;PTK2

GO:0032990

cell morphogenesis involved in differentiation

2

709

0.016

0.095

ARHGAP39;PTK2

GO:0000904

neuron development

2

813

0.021

0.096

ARHGAP39;PTK2

GO:0048666

cell projection organization

2

949

0.028

0.116

ARHGAP39;PTK2

GO:0030030

cell morphogenesis

2

968

0.029

0.115

ARHGAP39;PTK2

GO:0000902

neuron differentiation

2

1008

0.031

0.118

ARHGAP39;PTK2

GO:0030182

cellular component morphogenesis

2

1026

0.032

0.117

ARHGAP39;PTK2

GO:0032989

generation of neurons

2

1088

0.036

0.120

ARHGAP39;PTK2

GO:0048699

Neurogenesis

2

1156

0.040

0.120

ARHGAP39;PTK2

GO:0022008

Locomotion

2

1282

0.049

0.127

ARHGAP39;PTK2

GO:0040011

synaptic transmission

2

702

0.016

0.109

GRIA3;PTK2

GO:0007268

multicellular organismal signaling

2

812

0.021

0.101

GRIA3;PTK2

GO:0035637

cell junction

2

771

0.019

0.104

GRIA3;PTK2

GO:0030054

transmission of nerve impulse

2

791

0.020

0.103

GRIA3;PTK2

GO:0019226

cell-cell signaling

2

1135

0.039

0.120

GRIA3;PTK2

GO:0007267

Nucleolus

2

628

0.013

0.132

C1D;PTK2

GO:0005730

nucleoplasm part

2

862

0.023

0.102

C1D;PTK2

GO:0044451

receptor binding

2

1206

0.044

0.125

C1D;PTK2

GO:0005102

cell differentiation

3

2754

0.033

0.114

ARHGAP39;PTK2;TRAPPC9

GO:0030154

cellular developmental process

3

2928

0.039

0.125

ARHGAP39;PTK2;TRAPPC9

GO:0048869

intracellular non-membrane-bounded organelle

3

3104

0.046

0.126

ARHGAP39;C1D;PTK2

GO:0043232

non-membrane-bounded organelle

3

3104

0.046

0.122

ARHGAP39;C1D;PTK2

GO:0043228

Fig. 4

The cluster result of GO analysis

Discussion

The present study identified significant SNPs and novel candidate genes associated with mastitis-related traits in Chinese Holstein population with mixed model based single marker regression analysis (MMRA). Two genes (TRAPPC9 and ARHGAP39) identified by significant SNPs indicate that they are important candidate genes associated with mastitis-related traits. To our knowledge, this is the first study to decompose the genetic background of mastitis-related traits in Chinese dairy cattle using MMRA assay.

With regards to TRAPPC9 gene, it was reported that its product NIBP (NIK and IKKβ-binding protein) can enhance cytokine-induced NF-κB signaling pathway through interaction with NIK (NF-κB-inducing kinase) and IKKβ (IκB kinase-β) [14, 15]. In recent studies, TRAPPC9 gene was considered as candidate gene for autosomal recessive non-syndromic mental retardation [16, 17]. In the present study, the SCS EBVs (2.99) of the cows with AA genotype of SNP (ARS-BFGL-NGS-100480) in TRAPPC9 gene is significantly higher than the other two genotypes (P < 0.001). The similar tendency of the three genotypes was independently proved in a completely different Chinese Holstein population (n = 314, our unpublished data). As for ARHGAP39 gene, it was proved to be function to activate Rho GTPase which is known as new targets in cancer therapy [18]. Therefore, it is clear that the present study screened functional closely related genes to bovine mastitis resistance.

From the reported GWAS based on single locus regression analysis, it is not easy to identify the certain SNPs associated with SCS or mastitis-related traits. As shown in Table 1, 7 significant SNPs located on BTA 14 on whole genomic level (P < 1.14E-06) by MMRA in Chinese Holsteins were completely different from all the reported significant SNPs [7, 8], whereas significant SNPs on BTA 14 are consistent with other studies [6, 911, 19, 20]. In comparison, one significant SNP UA-IFASA-9288 (BTA 14, Position: 2201870) in Chinese Holstein was close to (147413 bp) the SNP ARS-BFGL-NGS-107379 (Position: 2054457) which was identified in Nordic Holstein [9]. However, Tiezz et al. [11] identified a region associated with clinical mastitis from 2,574,909 to 3,137,184 bp on BTA 14 which contains three genome-wide significant SNPs (ARS-BFGL-NGS-100480, ARS-BFGL-NGS-56327 and UA-IFASA-5306) covered by TRAPPC9 gene in this study. These GWAS studies suggest that mastitis-related traits as low heritable polygenetic traits are mainly controlled by multiple loci which distributed across the whole genome and each with relatively small genetic effect.

Although SCS is continuous trait which normally used as important indicator of mastitis, it is usually unstable and easily influenced by environment [21, 22]. Therefore, to disease indicator trait, current strategy has changed to performing association studies in cases and controls test [23], because of mastitis resistance or susceptibility can be considered as threshold traits [2]. In the current another study, we defined that the left and right parts of the population with half/one standard deviation of SCS EBVs were mastitis susceptibility group (case) and healthy group (control), respectively, and analyzed the two groups with ROADTRIPs (Robust Association-Detection Test for Related Individuals with Population Substructure) (version 1.2) (http://faculty.washington.edu/tathornt/software/ROADTRIPS2/) using bovine 54 k SNPs information. Although the decreased population size and increasing bias affect the testing power of the case-control association assay, we also have found two significant SNPs linked to two genes (TRAPPC9 and ARHGAP39) by ROADTRIPs of case-control test compared with MMRA results, which strongly suggest that these genes are novel candidate genes for mastitis traits.

The genes closed to or covered significant SNPs were further subjected to bioinformatics analysis. Results from Gene Ontology (GO) analysis (Table 3) indicated that TRAPPC9, ARHGAP39 and PTK2 genes play a role in regulation of cell differentiation (GO: 0030154, P = 0.033) or developmental process (GO: 0048869, P = 0.039). From the cluster result of GO analysis (Fig. 4), we found that ARHGAP39 and PTK2 genes are mostly close genes, which participate 24 pathway terms. However, TRAPPC9 gene has less result in GO analysis, thus the related pathways are needed to do further functional analysis.

Conclusions

Although lower detecting power exists in SCS EBVs and other mastitis resistance traits, results consistently support that the significant SNPs are mainly located on the BTA 14 in the Chinese Holstein cows. TRAPPC9 and ARHGAP39 genes reveal the two novel candidate genes associated with mastitis resistant traits in dairy cattle.

Methods

Ethics statement

All protocols for collection of the blood sample of experimental cows were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at China Agricultural University.

Animals and phenotype

A total of 2,093 cows from 14 sires were collected to construct the study population. The number of daughters of 14 sires range from 83 to 358 with an average of 150. Although the 14 sires were genotyped, they were not used in the association study in order to avoid double use of daughters’ information. These daughters were from 15 Holstein cattle farms in Beijing, China. No specific permissions were required for these locations/activities.

As closely following normal distribution, somatic cell scores (SCSs) are calculated from SCCs as (log2 (SCC/100,000) + 3). To avoid environment influence, EBVs of SCSs were provided as the phenotypes in the GWAS. These EBVs were obtained based on a multiple trait random regression test-day model [24] using the software RUNGE provided by Canadian Dairy Network (CDN) (http://www.cdn.ca).

DNA extraction and genotypes

Genomic DNA of the whole blood was extracted using the TIANamp Blood Genomic DNA Purification kit (Tiangen inc. Beijing, China). The criteria of DNA quality control were DNA concentration should be larger than 50 ng/μL, the ratio of OD260/OD280 in the range of 1.7–1.9 and the ratio of OD260/OD230 in the range of 1.5–2.1.

The cows were genotyped using Illumina Bovine SNP50 BeadChip [25]. The genotypes were edited according to the criteria: (1) call rate > = 90 %; (2) SNPs did not deviated extremely from Hardy-Weinberg equilibrium (P >10−6); (3) minor allele frequency > = 3 %). After quality control, a total of 43,885 SNPs were available for MMRA. Distribution of SNPs on each chromosome after quality control and the average distances between adjacent SNPs are shown in Additional file 1: Table S1.

Association analysis

Mixed model based single locus regression analysis (MMRA) applied to perform GWAS in our studies is as follows:

MMRA:
$$ \mathbf{y}=\upmu + b\mathrm{x} + \mathrm{Z}\mathrm{a} + \mathrm{e} $$

Where y is the vector of phenotypes (SCS EBVs), μ is the overall mean, b is the vector of coefficients of the regression on SNP genotypes, x is the vector of SNP genotypes, a ~ (0, Aσ a 2 ) and e ~ (0, Wσ e 2 ) are the vectors of the polygenic effects and residuals, where A is the additive genetic relationship matrix and W is a diagonal matrix with diagonal elements of 1/RELi to weight residuals variance for heterogeneity [26]. RELi is the reliability of EBV for the ith individual. σ a 2  and σ e 2 is the additive variance and residual error variance respectively. For each SNP, the estimated b and \( Var\left(\widehat{b}\right) \) are obtained via mixed model equations (MME). In addition, an approximate Wald chi-squared statistic \( {\widehat{b}}^2/Var\left(\widehat{b}\right) \) with df =1 is estimated for the SNPs significantly associated with phenotypes. This association analysis was conducted using a program written in FORTRAN language by our group [26].

Statistical inference

To decrease the false positive rate of multiple tests and screen more available SNPs as well as find more functional related genes, Bonferroni multiple testing (P < 0.05) was adopted to adjust for number of SNPs on genome and chromosome level. The results of Bonferroni threshold for genome and each chromosome divided by 0.05 were listed in Additional file 2: Table S2.

Linkage disequilibrium analysis for the significant SNPs on BTA 14 was performed using Haploview software (version 4.2) [27].

Student t-tests were conducted to compare the difference of cows SCS EBVs with different genotypes in each candidate gene.

Abbreviations

GWAS: 

Genome-wide association study

SNP: 

Single nucleotide polymorphism

SCC: 

Somatic cell count

SCSs: 

Somatic cell scores

EBVs: 

Estimated breeding values

BTA: 

Bos taurus autosome

MMRA: 

Mixed model based single locus regression analysis

LY6

Lymphocyte-antigen-6 complex

TLR4

Toll-like receptor 4

BRCA1

Breast cancer 1

TRAPPC9: 

Trafficking protein particle complex 9

ARHGAP39

Rho GTPase activating protein 39

LD: 

Linkage disequilibrium

DIM: 

Days in milk

GO: 

Gene Ontology

NIBP: 

NIK and IKKβ-binding protein

NIK: 

NF-κB-inducing kinase

IKKβ: 

IκB kinase-β

ROADTRIPs: 

Robust Association-Detection Test for Related Individuals with Population Substructure

IACUC: 

Institutional Animal Care and Use Committee

CDN: 

Canadian Dairy Network

MME: 

Mixed model equations

Declarations

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (31272420), State High-Tech Development Plan of China (2008AA101002), Basic Research from the Ministry of Education of the People’s Republic of China (2011JS006), Modern Agro-industry Technology Research System (CARS-37), the Twelfth Five-Year plan of National Science and Technology Project in Rural Areas (2011BAD28B02) and the Program for Changjiang Scholar and Innovation Research Team in University (IRT1191). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University
(2)
Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University

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© Wang et al. 2015