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BMC Genomics

Open Access

Genome wide association studies for body conformation traits in the Chinese Holstein cattle population

  • Xiaoping Wu1,
  • Ming Fang1, 3,
  • Lin Liu2,
  • Sheng Wang1,
  • Jianfeng Liu1,
  • Xiangdong Ding1,
  • Shengli Zhang1,
  • Qin Zhang1,
  • Yuan Zhang1,
  • Lv Qiao2,
  • Mogens Sandø Lund4,
  • Guosheng Su4 and
  • Dongxiao Sun1Email author
Contributed equally
BMC Genomics201314:897

https://doi.org/10.1186/1471-2164-14-897

Received: 12 March 2013

Accepted: 10 December 2013

Published: 17 December 2013

Abstract

Background

Genome-wide association study (GWAS) is a powerful tool for revealing the genetic basis of quantitative traits. However, studies using GWAS for conformation traits of cattle is comparatively less. This study aims to use GWAS to find the candidates genes for body conformation traits.

Results

The Illumina BovineSNP50 BeadChip was used to identify single nucleotide polymorphisms (SNPs) that are associated with body conformation traits. A least absolute shrinkage and selection operator (LASSO) was applied to detect multiple SNPs simultaneously for 29 body conformation traits with 1,314 Chinese Holstein cattle and 52,166 SNPs. Totally, 59 genome-wide significant SNPs associated with 26 conformation traits were detected by genome-wide association analysis; five SNPs were within previously reported QTL regions (Animal Quantitative Trait Loci (QTL) database) and 11 were very close to the reported SNPs. Twenty-two SNPs were located within annotated gene regions, while the remainder were 0.6–826 kb away from known genes. Some of the genes had clear biological functions related to conformation traits. By combining information about the previously reported QTL regions and the biological functions of the genes, we identified DARC, GAS1, MTPN, HTR2A, ZNF521, PDIA6, and TMEM130 as the most promising candidate genes for capacity and body depth, chest width, foot angle, angularity, rear leg side view, teat length, and animal size traits, respectively. We also found four SNPs that affected four pairs of traits, and the genetic correlation between each pair of traits ranged from 0.35 to 0.86, suggesting that these SNPs may have a pleiotropic effect on each pair of traits.

Conclusions

A total of 59 significant SNPs associated with 26 conformation traits were identified in the Chinese Holstein population. Six promising candidate genes were suggested, and four SNPs showed genetic correlation for four pairs of traits.

Keywords

Dairy cattleGWASBody conformation traitsSNPHolsteinQTL

Background

Since the 1990s, body conformation traits have been used in dairy cattle breeding programs in many countries. Although these traits themselves are not of economic interest to breeders, they are closely related to many economic traits, such as the health, productivity, and lifetime of cattle. Vollema et al. [1] reported that some conformation traits such as body depth, rump angle, rump width, and udder depth were useful predictors of lifetime and longevity in Dutch dairy bull populations because of the genetic correlation between them. Lund et al. [2] showed that genetic correlations between health and type traits were generally moderate (-0.32 to 0.37) and that selection for improved udder conformation reduced the somatic cell count and the occurrence of clinical mastitis. Short and Lawlor [3] found that genetic correlations between linear type traits and first lactation yield ranged from 0.48 to 0.54. Pozveh et al. reported that body depth had genetic correlations with many other economic traits, such as the days from calving to first-insemination (0.79), calving interval (0.35), and gestation length (0.34). Stature was also genetically correlated with gestation length (0.49) [4]. Therefore, quantitative trait loci (QTLs) associated with body conformation traits are economically as important as other economic traits.

With the availability of a high-density chip with single nucleotide polymorphisms (SNPs) for bovine, genome-wide association study (GWAS) has become a useful tool for fine-scale QTL mapping. This approach has been widely applied to causative mutation detection in human [5, 6], mouse [7] and cattle [8, 9]. By using very large numbers of SNPs researcher can easily detect statistical associations between SNPs and phenotypes, and thus biologically meaningful candidate genes close to the significant SNPs are identified for further study. This procedure greatly narrows down the regions of the genome that contain the causative mutations. The associations can provide direct and necessary evidence for the function of a gene.

Recently, many GWASs have been focused on the economic traits in dairy cattle, including production traits [8, 1015], fertility traits [8, 1618], disease resistance [9, 19, 20], and somatic cell score [13], and many statistically significant SNPs and biologically meaningful genes have been reported. However, comparatively few studies about body conformation traits have been published [8, 21]. Linkage analysis has been used by some researchers to detect QTLs associated with conformation traits [2224]. Schrooten et al. [22] used microsatellite markers in a whole genome scan for QTLs affecting 18 conformation traits. Ashwell et al. [23] detected QTLs affecting 22 conformation traits, including body, udder, feet, legs, and dairy conformation, and found 41 chromosome-wise significant QTLs. Cole et al. [8] used a single-locus model to analyze 18 body conformation traits , which included six trait groups, body size, body shape, udder, teats, teats, feet and legs, and final score and reported the top 100 effects for each trait. Their results showed that traits within a phenotype group had a tendency of sharing common SNP effects.

In this research, we performed a genome wide association study for 29 conformation traits in a Chinese Holstein population, which included 1314 Chinese Holstein cattle and 52,166 SNPs. A LASSO-like multiple-SNP method was applied to identify multiple SNPs simultaneously. The genes closest to the significant SNPs (within a 1 Mb region) were annotated.

Methods

Blood samples were collected from Chinese Holstein cattle when the regular quarantine inspection of the farms was conducted. The procedure for collecting the blood samples was carried out in strict accordance with the protocol approved by the Animal Welfare Committee of China Agricultural University (Permit Number: DK996).

Phenotype and genotype data

The Chinese Holstein population in this study comprised 1314 Chinese Holstein cows, the daughters of 22 sires. All the cows were from 22 dairy cattle farms in the Beijing Dairy Cattle Center and the Beijing Sanyuan Lvhe Dairy Farming Center where regular and standard performance tests, including measurement of conformation traits, have been carried out since 1999 as part of the Dairy Herd Improvement (DHI) system. According to the linear classification system defined by Dairy Data Center of China, Dairy Association of China (DAC) [25], 21 linear type traits were scored from 1 to 9, and eight composite traits were measured using an index with values and scored from 0 to 100. The 21 traits were animal size, stature, height at front end, chest width, body depth, loin strength, rump width, rump angle, bone quality, foot angle, rear legs side view, udder depth, udder texture, median suspensory, fore udder attachment, front teat placement, teat length, rear attachment height, rear attachment width, rear teat placement, and angularity. The eight function score traits were conformation (final score), dairy character, capacity, rump, feet and legs, fore udder, rear udder, and mammary system. Calculation of the scores for the eight composite traits was based on linear score, weights, and defective traits. The phenotypic values of the 21 conformation traits in the first lactation of the cows were measured by the Beijing Dairy Cattle Center [26] and then the genetic parameters of all 29 traits were estimated [27]. The estimated breeding values (EBVs) were calculated with a multiple-trait random regression test-day model using the RunGE software purchased from Canadian Dairy Network [28] by the Dairy Data Center of China. The descriptive statistics of the EBVs for the 29 traits in the 1314 individuals are listed in Table 1. The genetic correlations between each pair of traits were also calculated (see Additional file 1 for details). To conveniently generalize the results, the correlation coefficients (a total of 29*28/2 = 406 pair-wise values) were classified into five levels, -1 to -0.66, -0.66 to -0.33, -0.33 to 0.33, 0.33 to 0.66, and 0.66 to 1, and were defined as high-level negative correlation, medium-level negative correlation, weak-level correlation, medium-level positive correlation, high-level positive correlation, respectively (Table 2). The results show that 15 of 406 pairs of traits (7.2%) have high-level positive correlations, while most of the pairs have weak correlations (75.2%).
Table 1

Statistics of the estimated breeding values for the 29 conformation traits used in the GWAS

Traits

N

Mean

Variance

SD

Min

Max

Heritability

Conformation (final score)

1314

-1.58

9.73

3.12

-14

8

0.21

Capacity

1314

-1.92

14.61

3.82

-14

10

0.29

Stature

1314

-1.74

21.88

4.68

-25

13

0.37

Height at front end

1314

-0.96

8.42

2.90

-11

10

0.14

Animal size

1314

-1.50

15.27

3.91

-17

12

0.37

Chest width

1314

-2.59

14.30

3.78

-14

7

0.09

Body depth

1314

-0.84

13.76

3.71

-15

8

0.19

Rump

1314

-1.24

11.68

3.42

-12

11

0.07

Rump angle

1314

-0.52

18.87

4.34

-16

14

0.26

Rump width

1314

-0.87

24.09

4.91

-18

21

0.07

Loin strength

1314

-1.41

17.42

4.17

-19

11

0.17

Feet and legs

1314

-1.48

7.17

2.68

-11

7

0.09

Foot angle

1314

-1.03

12.11

3.48

-16

11

0.13

Bone quality

1314

-0.11

13.05

3.61

-14

10

0.10

Rear legs side view

1314

0.01

14.63

3.82

-17

13

0.24

Mammary system

1314

-0.81

13.06

3.61

-16

10

0.19

Udder depth

1314

-1.60

9.59

3.10

-15

8

0.22

Udder texture

1314

-1.17

8.39

2.90

-12

7

0.08

Median suspensory

1314

-0.44

15.10

3.89

-13

13

0.17

Fore udder

1314

-0.59

15.19

3.90

-16

12

0.17

Fore attachment

1314

-0.20

19.12

4.37

-15

11

0.27

Fore teat placement

1314

-0.64

13.37

3.66

-13

12

0.10

Teat length

1314

0.22

12.60

3.55

-16

11

0.18

Rear udder

1314

-0.70

13.16

3.63

-16

11

0.21

Rear attachment height

1314

-0.30

8.95

2.99

-11

8

0.15

Rear attachment width

1314

-1.23

11.34

3.37

-13

10

0.19

Rear teat placement

1314

-1.63

9.97

3.16

-12

9

0.11

Dairy character

1314

-1.50

16.40

4.05

-19

11

0.34

Angularity

1314

-1.39

12.22

3.50

-18

10

0.18

Note: N, SD, Min and Max means observations, standard deviation, minimum and maximum, respectively.

Table 2

Summary of the frequencies of pair-wise genetic correlations among 29 conformation traits

Range

(-1 to -0.66)

(-0.66 to -0.33)

(-0.33 to 0.33)

(0.33 to 0.66)

(0.66 to 1)

Total

Number

0

3

306

82

15

406

Frequency

0

0.007

0.752

0.201

0.072

1

The animals were genotyped using the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA, USA). Some individuals were genotyped using the Illumina 54 K chip version1 containing 54,001 SNPs, while others were genotyped using the 54 K chip version 2 containing 54,609 SNPs. Genotype imputation was conducted for all the genotyped individuals using the Beagle software, version 3.1.0 [29, 30]. After imputation, there were 56,270 SNPs in the marker data. SNPs were excluded from the analysis if the minor allele frequency (MAF) was less than 1%, the call rate was less than 90%, or the genotype frequency deviated from Hardy-Weinberg Equilibrium (HWE) with a P-value lower than 10-6. After quality control, 1314 individuals with 52,166 SNPs remained for the GWAS. After editing, the average distance between adjacent SNPs on the genome was 59.59 kb, and the median distance was 49.00 kb. Finally, the association analysis was conducted between each trait and 52,166 SNPs on 29 autosomes and X chromosome in the bovine genome.

Statistical analyses

Statistical tests of SNP effects were conducted using the expectation maximization algorithm based on an improved least absolute shrinkage and selection operator (LASSO) [31] method. This method simultaneously estimates multiple SNP effects and shrinks the effects of zero-effect SNPs towards zero, and thus avoids complex model selection (Fang et al. 2013, unpublished).

The GWAS was carried out in two steps. First, single trait mixed model analysis (SMMA) was applied to estimate the effect of each SNP. Then, the first 500 markers (why 500 markers were used is explained in the Discussion section) with the lowest P-values were selected for the multiple-SNP analysis.

The linear model that was used to estimate the effect of the jth SNP can be expressed as:
y = 1 μ + x j β j + Zg + e
(1)

where y is the vector of EBVs, 1 is the vector with its elements of 1, μ is the population mean; x j is the vector of the genotype of the jth SNP marker, which is assigned to -1, 0, and 1 for genotypes AA, AB and BB, respectively, and β j is the SNP effect; g is the vector of polygenic effects, and Z is the design matrix related to the polygenic effect; e is the vector of random residuals. It is assumed that g ~ N 0 , A σ g 2 and e ~ N 0 , I σ e 2 , where A is the additive genetic relationship matrix based on pedigree, σ g 2 is the variance of polygenic effect, I is an identity matrix, and σ e 2 is the residual variance. When a single-SNP mixed model was applied, the computational time was extremely large because of the iterative calculation of variance components (Best Linear Unbiased Prediction). Therefore, we first approximately calculated the variance components without considering a QTL effect and then imposed the estimates of variance components on the mixed model equation, which does not need an iterative calculation and thus reduces computational time.

For the SMMA analysis, the significance of the SNP effect was tested using a t-test with null hypothesis of β= 0, and the Bonferroni correction was applied to control the false positives. So, the threshold for significant associations was –log10 (0.05/N), where N is the number of SNP loci tested in the analysis.

The model to estimated effects of the SNPs selected from the first step can be expressed as:
y = 1 μ + + Zg + e
(2)

where X is the matrix of genotype covariables of the 500 SNPs, and β is the vector of SNP effects. An expectation-maximization algorithm was adopted to estimate the model parameters. The method assigned an improved LASSO prior π β j = λ j 2 e - λ j β j to SNP effect β j [32], where the hyper-parameter λ j 2 / 2 is assigned a conjugate gamma prior with gamma (a,b), where a and b are very small values, and both a and b are taken as 10-6. The prior of the residual polygenic effect follows the normal distribution g σ g 2 ~ N 0 , A σ g 2 , where σ g 2 is the residual polygenic variance and A is the additive genetic relationship matrix. The expectation-maximization algorithm was applied to estimate SNP effects β j by finding the maximum posterior mode, which treats the polygenic effect (g) as a missing variable (see Additional file 2 for details).

The threshold value for declaring the significance of the SNP was determined from the empirical distribution of the heritability of SNP j (the SNP with the largest heritability across the genome for each permutation), h j 2 = σ j 2 / j = 1 p σ j 2 + σ g 2 + σ e 2 , derived from 1,000 permutations, where σ j 2 = 2 p j 1 - p j β j 2 is the variance of the jth SNP, and p j is the allele frequency of the SNP. Here, heritability was used to measure the strength of each SNP, which is fairer than using the SNP effect, because the allele frequency of each SNP is different.

Identification of SNP locations and gene annotation

The locations of significant SNPs were reported based on the UMD3.1 assembly of bovine genome sequence (assembled by the Center for Bioinformatics and Computational Biology (CBCB) at University of Maryland). The genes that were closest to the significant SNPs (within 1 Mb) were determined by the National Animal Genome Research Program [33] and the National Center for Biotechnology Information [34]).

Results

A total of 59 genome-wise significant SNPs associated with 26 out of the 29 conformation traits were found by our improved LASSO method. Twenty-two of the SNPs were located within 22 known genes regions. We identified the 26 conformation traits into six trait group, and investigated the significant SNPs associated with each of these traits as described below.

Dairy character traits

Three and two SNPs were associated with dairy character and angularity respectively (Table 3). Among them, dairy character and angularity shared one common SNP, which was located 45 kb away from SLC25A24 on Bos taurus chromosome 3 (BTA3). For dairy character, one SNP was located within SCEL on BTA12 and the other SNP was 14 kb away from SPATA17. For angularity, the other SNP was 261 kb away from HTR2A.
Table 3

Genome-wide significant SNPs for final conformation score and dairy character traits

Trait

SNP name

Chr.

Position (bp)

Nearest gene

Distance (bp)

Heritability

Threshold

Conformation (final score)

ARS-BFGL-NGS-109711

5

110149999

ANKRD54

within

0.00980

0.00942

Dairy character

ARS-BFGL-NGS-14022

3

35255950

SLC25A24

45,501

0.01490

0.00929

BTB-01238380

12

53100776

SCEL

within

0.00936

0.00929

ARS-BFGL-NGS-55380

16

21821449

SPATA17

14,623

0.00990

0.00929

Angularity

ARS-BFGL-NGS-14022

3

35255950

SLC25A24

45,501

0.01100

0.00969

 

ARS-BFGL-NGS-113826

12

17150394

HTR2A

261,113

0.01320

0.00969

Note: Heritability and threshold were obtained using the LASSO method. Nearest gene are symbols of gene full name in the NCBI database (http://www.ncbi.nlm.nih.gov/).

Capacity traits

For body depth, height at front end, and animal size, each trait was associated with one significant SNP; for stature and loin strength, each trait was associated with two SNPs; and for chest width and capacity, each trait was associated with five SNPs (Table 4). Among them, the SNP on BTA3 was 7 kb away from DARC and was associated with both body depth and capacity; and the SNP on BTA25 was 9 kb away from TMEM130, and was associated with both body depth and animal size. The SNPs at 39 Mb on BTA9, 115 Mb on BTA6, 35 Mb on BTA15, 53 Mb on BTA12, and 10 Mb on BTA 18 were associated with capacity, stature, loin strength, height at front end, and chest width, respectively, and all of them were located in regions of the chromosomes that contained known genes. The remaining SNPs were at distances of 3 kb to 19 kb from the nearest known genes.
Table 4

Genome-wide significant SNPs for capacity and the component traits

Trait

SNP name

Chr.

Position (bp)

Nearest gene

Distance (bp)

Heritability

Threshold

Capacity

Hapmap40339-BTA-117016

3

10640386

DARC

7,094

0.01100

0.00894

ARS-BFGL-NGS-114456

7

30964539

LOC789456

97,615

0.01100

0.00894

ARS-BFGL-NGS-44162

9

39626344

LOC539486

within

0.01160

0.00894

ARS-BFGL-NGS-26589

18

4852600

NUDT7

137,863

0.01080

0.00894

ARS-BFGL-NGS-115067

25

37927752

TMEM130

8,967

0.01280

0.00894

Stature

Hapmap60794-rs29022851

6

115008971

CPEB2

within

0.01110

0.00953

BTA-72885-no-rs

29

19560064

LOC782090

81,135

0.01370

0.00953

Body depth

Hapmap40339-BTA-117016

3

10640386

DARC

7,094

0.00884

0.00872

Loin strength

ARS-BFGL-NGS-70552

15

35177124

SERGEF

within

0.01340

0.00895

BTB-00938945

26

32943986

GPAM

19,414

0.00908

0.00895

Height at front end

BTB-01238380

12

53100776

SCEL

within

0.00875

0.00811

Animal size

ARS-BFGL-NGS-115067

25

37927752

TMEM130

8,967

0.01130

0.00953

Chest width

BTA-110160-no-rs

8

81389800

GAS1

121,119

0.01870

0.00969

ARS-BFGL-NGS-115466

18

10002426

CDH13

within

0.01410

0.00969

BTA-45515-no-rs

19

43170256

PTRF

8,091

0.01220

0.00969

BTB-00922140

4

82550244

POU6F2

54,944

0.01070

0.00969

 

ARS-BFGL-NGS-57462

25

8086468

LOC538487

131,274

0.01030

0.00969

Note: Heritability and threshold were obtained using the LASSO method. Nearest gene are symbols of gene full name in the NCBI database (http://www.ncbi.nlm.nih.gov/).

Rump traits

Eleven significant SNPs on different chromosomes were associated with rump traits (Table 5). Two and three of these SNPs were associated with rump and rump angle, respectively, and all of them were located within regions of the chromosomes that contained known genes. The remaining significant SNPs were at distances of 48 kb to 826 kb from the nearest known genes.
Table 5

Genome-wide significant SNPs for rump and the component traits

Trait

SNP name

Chr.

Position (bp)

Nearest gene

Distance (bp)

Heritability/-log10(P)b

Threshold

Rump

BTB-01660659

1

145986598

KRTAP10-12

688

0.01280

0.00916

ARS-BFGL-NGS-12856

4

8155616

CDK14

within

0.01100

0.00916

BTB-00323505

7

82338362

ODZ2

within

0.00966

0.00916

Rump width

BTB-00168895

4

20788689

LOC781728

166,306

0.01260

0.00917

Hapmap40061-BTA-28737

9

1775187

LOC616304

826,933

0.00924

0.00917

BTB-02035532a

7

58436123

LOC100138639

348,605

6.07b

6.02c

ARS-BFGL-NGS-14128a

10

36665562

ACYP2

within

7.21b

6.02c

ARS-BFGL-NGS-86147a

10

49856100

ACYP2

44,441

6.89b

6.02c

ARS-BFGL-NGS-53281a

15

66603229

SLC1A2

within

8.35b

6.02c

BTB-00611649a

15

67429625

LDLRAD3

within

6.05b

6.02c

ARS-BFGL-NGS-97658a

15

68069900

C15H11orf74

158,748

7.27b

6.02c

BTA-30189-no-rsa

X

60101130

MAGED2

42,513

6.34b

6.02c

ARS-BFGL-NGS-80859a

X

61237718

NXF3

338,723

7.01b

6.02c

Rump angle

BTA-94299-no-rs

5

93940507

MGST1

within

0.01500

0.00906

Hapmap48553-BTA-10000

7

59019641

LOC788619

36,977

0.01610

0.00906

BTB-01219012

7

65799159

LOC100296765

48,625

0.01020

0.00906

ARS-BFGL-NGS-31810

11

105631144

LOC536255

within

0.00960

0.00906

ARS-BFGL-NGS-54462

25

13405791

MIR365

61,471

0.01190

0.00906

 

ARS-BFGL-NGS-102900

27

4720968

AGPAT5

within

0.01300

0.00906

Note: Heritability and threshold were obtained using the LASSO method; -log10(P) was calculated using SMMA. aSNP detected by SMMA only; b-log10(P) obtained from SMMA; cthreshold of SMMA. Nearest gene are symbols of gene full name in the NCBI database (http://www.ncbi.nlm.nih.gov/).

Feet and legs traits

Twelve significant SNPs were detected for feet and legs traits (Table 6). Three of these SNPs, for feet and legs, foot angle, and rear leg side view, were located within DHX35 on BTA13, PLEKHB2 on BTA2, and DOCK10 on BTA2, respectively. Two SNPs on BTA3 and BTA27 for feet and legs, two SNPs on BTA1 and BTA15 for bone quality, three SNPs on BTA3, BTA4, and BTA22 for foot angle, two SNPs on BTA14 and BTA 24 for rear leg side view were located at distances of 3 kb to 420 kb from the nearest known genes.
Table 6

Genome-wide significant SNPs for feet and legs and the component traits

Trait

SNP name

Chr.

Position (bp)

Nearest gene

Distance (bp)

Heritability/-log10(P)b

Threshold

Feet and legs

Hapmap48847-BTA-67772

3

48281407

RWDD3

116,751

0.01530

0.00948

ARS-BFGL-NGS-76581

27

39783292

OXSM

78,430

0.01290

0.00948

Hapmap53251-rs29027216

13

68437003

DHX35

within

0.01050

0.00948

Hapmap49594-BTA-39447a

1

20165566

LOC101905904

within

6.54b

6.02c

Bone quality

BTA-87372-no-rs

1

30724028

LOC100337296

420,082

0.00967

0.00949

BTA-117758-no-rs

15

72591774

C8H9orf30

112,905

0.00964

0.00949

Foot angle

ARS-BFGL-NGS-18261

2

1896078

PLEKHB2

within

0.01010

0.00929

ARS-BFGL-NGS-73625

3

14218748

NES

3,146

0.01060

0.00929

Hapmap48448-BTA-71823

4

100663967

MTPN

37,399

0.00943

0.00929

ARS-BFGL-NGS-113718

22

2655659

CMC1

29,461

0.01120

0.00929

Rear leg side view

ARS-BFGL-NGS-97763

2

113852386

DOCK10

within

0.01020

0.00942

Hapmap29973-BTA-129162

14

46264806

PAG1

71,476

0.00978

0.00942

UA-IFASA-4800

24

31524371

ZNF521

151,162

0.01230

0.00942

 

Hapmap52451-rs29021142a

1

138784934

KCNH8

106,181

6.25b

6.02c

Note: Heritability and threshold were obtained using the LASSO method; -log10(P) was calculated using SMMA. aSNP detected by SMMA only; b-log10(P) obtained from SMMA; cthreshold of SMMA. Nearest gene are symbols of gene full name in the NCBI database (http://www.ncbi.nlm.nih.gov/).

Mammary system traits

A total of 17 significant SNPs were detected for mammary system traits (Table 7). Of these SNPs, one associated with rear udder was located within LOC100337279 on BTA14; two associated with udder texture were within LOC100295233 and DRG1 on BTA3 and BTA7, respectively; two associated with median suspensory fell were within LRP2 and MACROD2 on BTA2 and BTA13, respectively; one associated with fore teat placement was located within SLC39A11 on BTA19; and one associated with rear teat placement was located within SH3RF3 on BTA11. The other 10 SNPs were located at distances of 960 bp to 448 kb from the nearest known genes.
Table 7

Genome-wide significant SNPs for mammary system traits

Trait

SNP name

Chr.

Position (bp)

Nearest gene

Distance (bp)

Heritability/-log10(P)b

Threshold

Rear udder

ARS-BFGL-NGS-111920

14

44029634

LOC100337279

within

0.01330

0.00891

Hapmap50827-BTA-94026

24

2166631

LOC100336384

39,890

0.01130

0.00891

Udder texture

ARS-BFGL-NGS-104839

3

88712390

LOC100295233

within

0.00873

0.00872

BTA-41935-no-rs

17

72284836

DRG1

within

0.01670

0.00872

BTB-01236227

20

15824409

HTR1A

264,560

0.00941

0.00872

Median suspensory

BTB-00089278

2

26942975

LRP2

within

0.01080

0.00874

BTB-01007411

4

37145925

SEMA3E

960

0.00995

0.00874

ARS-BFGL-NGS-35982

5

5693439

NAP1L1

81,318

0.00941

0.00874

ARS-BFGL-NGS-29118

13

8497369

MACROD2

within

0.01490

0.00874

ARS-BFGL-NGS-52278a

12

89182471

RAB20

within

7.85b

6.02c

Fore attachment

ARS-BFGL-NGS-114960

29

36024434

NTM

448,744

0.01050

0.00965

Fore teat Placement

ARS-BFGL-NGS-113245

19

59068269

SLC39A11

within

0.01290

0.00892

Teat length

BTB-01255458

10

99270875

PDIA6

80,295

0.01030

0.00911

Rear attach height

ARS-BFGL-NGS-20052

2

107616903

CDK5R2

3,609

0.00988

0.00904

Hapmap43038-BTA-76203

6

50316616

LOC100298058

12,846

0.00997

0.00904

Rear attach Width

BTB-01478363

20

17370437

BAG1

210,690

0.00938

0.00924

Rear teat Placement

ARS-BFGL-NGS-31730

11

44265651

SH3RF3

within

0.00927

0.00864

 

BTB-01230622

15

62600934

DCDC5

61,622

0.01200

0.00864

Note: Heritability and threshold were obtained using the LASSO method; -log10(P) was calculated using SMMA. aSNP detected by SMMA only; b-log10(P) obtained from SMMA; cthreshold of SMMA. Nearest gene are symbols of gene full name in the NCBI database (http://www.ncbi.nlm.nih.gov/).

Final conformation score

A SNPs on BTA5 (Table 3) was found to be associated with final conformation score, and this SNP was harbored within ANKRD54, which encodes an ankyrin repeat domain-containing protein.

The estimated heritability for 29 conformation traits obtained using improved LASSO was plotted and the figures are available in Additional file 3.

The results obtained using SMMA are also listed in Tables 6, 7 and 8. Only 11 significant SNPs were detected and eight of them were significantly associated with rump width. The other three SNPs were associated with rear legs side view, median suspensory, and feet and legs.
Table 8

Genome-wide significant SNPs compared with the SNPs reported by Cole et al.[8]

Chr.

Position (bp)a

Traita

Position (bp)b

Distance (bp)

Traitb

12

53100776

Dairy character

52240216

860,560

Teat length, Rear leg side view

16

21821449

Dairy character

21741980

79,469

Somatic cell score

16

21821449

Dairy character

22179897

358,448

Rear teat placement

16

21821449

Dairy character

22272329

450,880

Somatic cell score, Rear teat placement

16

21821449

Dairy character

22406467

585,018

Somatic cell score

18

4852600

Capacity

5655435

802,835

Foot angle

5

110149999

Conformation (final score)

110886859

736,860

Fore udder attachment, Rear udder height, Udder depth

5

110149999

Conformation (final score)

110910712

760,713

Fore udder attachment, Udder depth

7

30964539

Capacity

31136178

171,639

Somatic cell score

7

30964539

Capacity

31217950

253,411

Somatic cell score

7

30964539

Capacity

31655835

691,296

Teat length

aResults from our study; bresults reported by Cole et al. [8] Distance, the distance on the corresponding chromosome between the positions of the two SNPs (ours and the corresponding SNP from Cole et al.).

When we compared our results with those of Cole et al. [8] and Bolormaa et al. [21], we found that none of our significant SNPs were the same as the SNPs reported by Cole et al. [8] or Bolormaa et al. [21]. However, some of our SNPs were close to the significant SNPs reported by Cole et al. [8] that were associated with different traits (Table 8).

Discussion

In this study, we performed a GWAS for 29 conformation traits in a population of Chinese Holstein cows. A two-step strategy was applied to estimate SNP effect, and first we selected 500 SNPs using SMMA. We originally planned to select SNPs with P-values < 0.01 (-log10(P) > 2), and we found that about 500 SNPs met this condition for the 29 traits (the -log10(P) values at the 500th marker were sorted into descending order for the 29 traits and ranged from 2.089 to 2.421). Therefore, we decided to use the top 500 SNPs for the multiple QTL analysis. In other words, the selected 500 SNPs include nearly all the SNPs with P-values < 0.01.

We found five SNPs located within previously reported QTL regions that were associated with conformation-related traits. The SNP on BTA12 associated with angularity is 261 kb away from HTR2A and is located within a QTL region that has been reported by Schrooten et al. [22] to be associated with angularity. The SNP on BTA29 associated with stature is 81 kb away from LOC782090 and is within a large QTL region that has been found to significantly affect Angus body height at maturity [35]. The SNP on BTA24 associated with rear leg side view is near ZNF521 and is within a QTL region that has been reported to have a significant effect on dairy cattle rear leg set [22]. The SNP on BTA10 associated with teat length is near PDIA6 and is located within a QTL region that has been shown to have a significant effect on teat length [36]. And, the SNP on BTA25 associated with animal size is near TMEM130 and is within a QTL region that has been reported to affecting calf size in Danish Holstein cattle [37]. Besides, most of significant SNPs that we detected in this study are located within QTL regions that have been reported previously to affect production, longevity, and reproduction traits in dairy cattle [21, 35, 36, 38, 39].

We also found several SNPs located within genes that are known to have functions related to the development and metabolism of animal tissues. The SNP (Hapmap40339-BTA-117016; Table 4) on BTA3 which was associated with both capacity and body depth is 7 kb away from the gene, Duffy blood group, chemokine receptor (DARC). Hai et al. [40] performed a bivariate GWAS in human to identify the SNPs associated with lean body mass and age at menarche and reported that DARC may play an important role in regulating the metabolisms of both these traits. The SNP (BTA-110160-no-rs; Table 4) on BTA8 associated with chest width is 121 kb away from the growth arrest specific 1 (GAS1) gene. GAS1 is highly expressed in quiescent mammalian cells and its over-expression in normal and some cancer cell lines was reported to inhibit G0/G1 transition [41]. It was found that GAS1 was expressed by chondrocytes after the cartilage started to differentiate [41]. The SNP on BTA4 associated with foot angle is 37 kb away from the myotrophin (MTPN) gene, which plays an important role in cell and skeletal muscle growth [42]. These genes are suggested as functional candidate genes for body conformation traits.

Generally, different SNPs are associated with different traits, but some SNPs have been found to affect more than one trait. In our study, SNP Hapmap40339-BTA-117016 (Table 4) was associated with both capacity and body depth, SNP ARS-BFGL-NGS-115067 (Table 4) was associated with both capacity and animal size, SNP ARS-BFGL-NGS-14022 (Table 3) was associated with both dairy character and angularity, and SNP BTB-01238380 (Tables 3 and 4) was associated with both dairy character and height at front end. The genetic correlation between each of these pairs of genes was 0.51, 0.77, 0.86, and 0.35, which suggested that these four SNPs likely contribute to genetic correlation and perhaps have a pleiotropic effect on each pair of traits.

Conclusions

The present genome-wide association study identified 59 significant SNPs associated with 26 conformation traits in a Chinese Holstein cattle population. Some of these SNPs were located within or near previously reported genes and QTL regions, while some of the SNPs were new discoveries. We found that DARC, GAS1, MTPN, HTR2A, ZNF521, PDIA6, and TMEM130 were the most promising candidate genes for capacity and body depth, chest width, foot angle, angularity, rear leg side view, teat length, and animal size traits, respectively.

Notes

Abbreviations

GWAS: 

Genome-wide association study

EBV: 

Estimate breeding value

SNP: 

Single-nucleotide polymorphism

QTL: 

Quantitative trait locus

BTA: 

Bos taurus automosome

LASSO: 

Least absolute shrinkage and selection operator

DAC: 

Dairy Association of China

DHI: 

Dairy Herd Improvement system

MAF: 

Minor allele frequency

SMMA: 

Single trait mixed model analysis

GAS1: 

Growth arrest specific 1

HTR2A: 

5-hydroxytryptamine (serotonin) receptor 2A

ANKRD54: 

Ankyrin repeat domain 54

DHX35: 

DEAH (Asp-Glu-Ala-His) box polypeptide 35

DOCK10: 

Dedicator of cytokinesis 10

DRG1: 

Developmentally regulated GTP binding protein 1

DARC: 

Duffy blood group, chemokine receptor

LRP2: 

Low density lipoprotein receptor-related protein 2

MACROD2: 

MACRO domain containing 2

MTPN: 

Myotrophin

PDIA6: 

Protein disulfide isomerase family A, member 6

SCEL: 

Sciellin

SH3RF3: 

SH3 domain containing ring finger 3

SLC25A24: 

Solute carrier family 25 (mitochondrial carrier

phosphate carrier): 

Member 24

SLC39A11: 

Solute carrier family 39 (metal ion transporter), member 11

SPATA17: 

Spermatogenesis associated 17

TMEM130: 

Transmembrane protein 130

ZNF521: 

Zinc finger protein 52.

Declarations

Acknowledgments

This work was supported by the National Science and Technology Program of China (2013AA102504, 2011BAD28B02, 2012BAD12B01), the Beijing Innovation Team of Technology System in the National Dairy Industry, 948 Program (2011-G2A), the Beijing Research and Technology program (D121100003312001), the Program for Changjiang Scholar and Innovation Research Team in University (IRT1191), the National Natural Science Foundation of China (Grant No. 31001001), and the Program for New Century Excellent Talents In Heilongjiang Provincial University (Grant No. 1253--NCET--001).

Authors’ Affiliations

(1)
Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University
(2)
Beijing Dairy Cattle Center
(3)
Life Science College, Heilongjiang Bayi Agricultural University
(4)
Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University

References

  1. Vollema AR, Van der Beek S, Harbers AGF, De Jong G: Genetic evaluation for longevity of dutch dairy bulls. J Dairy Sci. 2000, 83 (11): 2629-2639. 10.3168/jds.S0022-0302(00)75156-3.View ArticlePubMedGoogle Scholar
  2. Lund T, Miglior F, Dekkers JCM, Burnside EB: Genetic-relationships between clinical mastitis, somatic-cell count, and udder conformation in Danish Holsteins. Livest Prod Sci. 1994, 39 (3): 243-251. 10.1016/0301-6226(94)90203-8.View ArticleGoogle Scholar
  3. Short TH, Lawlor TJ: Genetic-parameters of conformation traits, milk-yield, and herd life in Holsteins. J Dairy Sci. 1992, 75 (7): 1987-1998. 10.3168/jds.S0022-0302(92)77958-2.View ArticlePubMedGoogle Scholar
  4. Pozveh ST, Shadparvar AA, Shahrbabak MM, Taromsari MD: Genetic analysis of reproduction traits and their relationship with conformation traits in Holstein cows. Livest Sci. 2009, 125 (1): 84-87. 10.1016/j.livsci.2009.02.015.View ArticleGoogle Scholar
  5. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, et al: Complement factor H polymorphism in age-related macular degeneration. Science. 2005, 308 (5720): 385-389. 10.1126/science.1109557.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Johnson AD, O'Donnell CJ: An open access database of genome-wide association results. Bmc Med Genet. 2009, 10: 6-10.1186/1471-2350-10-6.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Flint J, Eskin E: Genome-wide association studies in mice. Nat Rev Genet. 2012, 13 (11): 807-817. 10.1038/nrg3335.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Cole JB, Wiggans GR, Ma L, Sonstegard TS, Lawlor TJ, Crooker BA, Van Tassell CP, Yang J, Wang S, Matukumalli LK, et al: Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics. 2011, 12: 408-10.1186/1471-2164-12-408.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Zanella R, Settles ML, McKay SD, Schnabel R, Taylor J, Whitlock RH, Schukken Y, Van Kessel JS, Smith JM, Neibergs HL: Identification of loci associated with tolerance to Johne's disease in Holstein cattle. Anim Genet. 2011, 42 (1): 28-38. 10.1111/j.1365-2052.2010.02076.x.View ArticlePubMedGoogle Scholar
  10. Jiang L, Liu JF, Sun DX, Ma PP, Ding XD, Yu Y, Zhang Q: Genome wide association studies for milk production traits in Chinese Holstein population. Plos One. 2010, 5 (10): e13661-10.1371/journal.pone.0013661.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Daetwyler HD, Schenkel FS, Sargolzaei M, Robinson JA: A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map. J Dairy Sci. 2008, 91 (8): 3225-3236. 10.3168/jds.2007-0333.View ArticlePubMedGoogle Scholar
  12. Schennink A, Stoop WM, Visker MH, van der Poel JJ, Bovenhuis H, van Arendonk JA: Short communication: genome-wide scan for bovine milk-fat composition. II. Quantitative trait loci for long-chain fatty acids. J Dairy Sci. 2009, 92 (9): 4676-4682. 10.3168/jds.2008-1965.View ArticlePubMedGoogle Scholar
  13. Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, Marques E, Stothard P, Moore SS: A whole genome scan to map QTL for milk production traits and somatic cell score in Canadian Holstein bulls. J Anim Breed Genet. 2009, 126 (3): 216-227. 10.1111/j.1439-0388.2008.00793.x.View ArticlePubMedGoogle Scholar
  14. Olsen HG, Hayes BJ, Kent MP, Nome T, Svendsen M, Larsgard AG, Lien S: Genome-wide association mapping in Norwegian Red cattle identifies quantitative trait loci for fertility and milk production on BTA12. Anim Genet. 2011, 42 (5): 466-474. 10.1111/j.1365-2052.2011.02179.x.View ArticlePubMedGoogle Scholar
  15. Bouwman AC, Bovenhuis H, Visker MHPW, van Arendonk JAM: Genome-wide association of milk fatty acids in Dutch dairy cattle. Bmc Genet. 2011, 12: 43-10.1186/1471-2156-12-43.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Sahana G, Guldbrandtsen B, Lund MS: Genome-wide association study for calving traits in Danish and Swedish Holstein cattle. J Dairy Sci. 2011, 94 (1): 479-486. 10.3168/jds.2010-3381.View ArticlePubMedGoogle Scholar
  17. Kim ES, Berger PJ, Kirkpatrick BW: Genome-wide scan for bovine twinning rate QTL using linkage disequilibrium. Anim Genet. 2009, 40 (3): 300-307. 10.1111/j.1365-2052.2008.01832.x.View ArticlePubMedGoogle Scholar
  18. Feugang JM, Kaya A, Page GP, Chen L, Mehta T, Hirani K, Nazareth L, Topper E, Gibbs R, Memili E: Two-stage genome-wide association study identifies integrin beta 5 as having potential role in bull fertility. BMC Genomics. 2009, 10: 176-10.1186/1471-2164-10-176.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Pant SD, Schenkel FS, Verschoor CP, You QM, Kelton DF, Moore SS, Karrow NA: A principal component regression based genome wide analysis approach reveals the presence of a novel QTL on BTA7 for MAP resistance in holstein cattle. Genomics. 2010, 95 (3): 176-182. 10.1016/j.ygeno.2010.01.001.View ArticlePubMedGoogle Scholar
  20. Kirkpatrick BW, Shi X, Shook GE, Collins MT: Whole-genome association analysis of susceptibility to paratuberculosis in Holstein cattle. Anim Genet. 2011, 42 (2): 149-160. 10.1111/j.1365-2052.2010.02097.x.View ArticlePubMedGoogle Scholar
  21. Bolormaa S, Hayes BJ, Savin K, Hawken R, Barendse W, Arthur PF, Herd RM, Goddard ME: Genome-wide association studies for feedlot and growth traits in cattle. J Anim Sci. 2011, 89 (6): 1684-1697. 10.2527/jas.2010-3079.View ArticlePubMedGoogle Scholar
  22. Schrooten C, Bovenhuis H, Coppieters W, Van Arendonk JAM: Whole genome scan to detect quantitative trait loci for conformation and functional traits in dairy cattle. J Dairy Sci. 2000, 83 (4): 795-806. 10.3168/jds.S0022-0302(00)74942-3.View ArticlePubMedGoogle Scholar
  23. Ashwell MS, Heyen DW, Weller JI, Ron M, Sonstegard TS, Van Tassell CP, Lewin HA: Detection of quantitative trait loci influencing conformation traits and calving ease in Holstein-Friesian cattle. J Dairy Sci. 2005, 88 (11): 4111-4119. 10.3168/jds.S0022-0302(05)73095-2.View ArticlePubMedGoogle Scholar
  24. Schrooten C, Bink MCAM, Bovenhuis H: Whole genome scan to detect chromosomal regions affecting multiple traits in dairy cattle. J Dairy Sci. 2004, 87 (10): 3550-3560. 10.3168/jds.S0022-0302(04)73492-X.View ArticlePubMedGoogle Scholar
  25. Dairy Data Center of China. [http://www.holstein.org.cn/newsDetail.jsp?lanm=04&wenzid=28]
  26. Beijing Dairy Cattle Center. [http://www.bdcc.com.cn/]
  27. Weijia G: Study on Population Genetic Analysis of Chinese Holstein (Chinese). 2010, China: China Agriculture UniversityGoogle Scholar
  28. Canadian Dairy Network. [http://www.cdn.ca]
  29. Browning BL, Browning SR: A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009, 84 (2): 210-223. 10.1016/j.ajhg.2009.01.005.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Browning BL, Browning SR: Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. Genet Epidemiol. 2007, 31 (6): 606-606.Google Scholar
  31. Tibshirani R: Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met. 1996, 58 (1): 267-288.Google Scholar
  32. Fang M, Jiang D, Li DD, Yang RQ, Fu WX, Pu LJ, Gao HJ, Wang GH, Yu LY: Improved LASSO priors for shrinkage quantitative trait loci mapping. Theor Appl Genet. 2012, 124 (7): 1315-1324. 10.1007/s00122-012-1789-7.View ArticlePubMedGoogle Scholar
  33. National Animal Genome Research Program. [http://www.animalgenome.org/]
  34. National Center for Biotechnology Information. [http://www.ncbi.nlm.nih.gov/]
  35. McClure MC, Morsci NS, Schnabel RD, Kim JW, Yao P, Rolf MM, McKay SD, Gregg SJ, Chapple RH, Northcutt SL, et al: A genome scan for quantitative trait loci influencing carcass, post-natal growth and reproductive traits in commercial Angus cattle. Anim Genet. 2010, 41 (6): 597-607. 10.1111/j.1365-2052.2010.02063.x.View ArticlePubMedGoogle Scholar
  36. Schnabel RD, Sonstegard TS, Taylor JF, Ashwell MS: Whole-genome scan to detect QTL for milk production, conformation, fertility and functional traits in two US Holstein families. Anim Genet. 2005, 36 (5): 408-416. 10.1111/j.1365-2052.2005.01337.x.View ArticlePubMedGoogle Scholar
  37. Thomasen JR, Guldbrandtsen B, Sorensen P, Thomsen B, Lund MS: Quantitative trait loci affecting calving traits in Danish Holstein cattle. J Dairy Sci. 2008, 91 (5): 2098-2105. 10.3168/jds.2007-0602.View ArticlePubMedGoogle Scholar
  38. Boichard D, Grohs C, Bourgeois F, Cerqueira F, Faugeras R, Neau A, Rupp R, Amigues Y, Boscher MY, Leveziel H: Detection of genes influencing economic traits in three French dairy cattle breeds. Genet Sel Evol. 2003, 35 (1): 77-101. 10.1186/1297-9686-35-1-77.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Seidenspinner T, Bennewitz J, Reinhardt F, Thaller G: Need for sharp phenotypes in QTL detection for calving traits in dairy cattle. J Anim Breed Genet. 2009, 126 (6): 455-462. 10.1111/j.1439-0388.2009.00804.x.View ArticlePubMedGoogle Scholar
  40. Hai R, Zhang L, Pei YF, Zhao LJ, Ran S, Han YY, Zhu XZ, Shen H, Tian Q, Deng HW: Bivariate genome-wide association study suggests that the DARC gene influences lean body mass and age at menarche. Sci China Life Sci. 2012, 55 (6): 516-520. 10.1007/s11427-012-4327-6.View ArticlePubMedGoogle Scholar
  41. Lee KK, Leung AK, Tang MK, Cai DQ, Schneider C, Brancolini C, Chow PH: Functions of the growth arrest specific 1 gene in the development of the mouse embryo. Dev Biol. 2001, 234 (1): 188-203. 10.1006/dbio.2001.0249.View ArticlePubMedGoogle Scholar
  42. Wang L, Wang Y: Molecular characterization, expression patterns and subcellular localization of Myotrophin (MTPN) gene in porcine skeletal muscle. Mol Biol Rep. 2012, 39 (3): 2733-2738. 10.1007/s11033-011-1028-3.View ArticlePubMedGoogle Scholar

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