Open Access

Genetic variants in mammary development, prolactin signalling and involution pathways explain considerable variation in bovine milk production and milk composition

  • Lesley-Ann Raven1, 2, 3Email author,
  • Benjamin G Cocks1, 2, 3,
  • Michael E Goddard1, 3, 4,
  • Jennie E Pryce1, 3 and
  • Ben J Hayes1, 2, 3
Genetics Selection Evolution201446:29

https://doi.org/10.1186/1297-9686-46-29

Received: 25 August 2013

Accepted: 28 February 2014

Published: 29 April 2014

Abstract

Background

The maintenance of lactation in mammals is the result of a balance between competing signals from mammary development, prolactin signalling and involution pathways. Dairy cattle are an interesting case study to investigate the effect of polymorphisms that affect the function of genes in these pathways. In dairy cattle, lactation yields and milk composition (for example protein percentage and fat percentage) are routinely recorded, and these vary greatly between individuals. In this study, we test 8058 single nucleotide polymorphisms in or close to genes in these pathways for association with milk production traits and determine the proportion of variance explained by each pathway, using data on 16 812 dairy cattle, including Holstein-Friesian and Jersey bulls and cows.

Results

Single nucleotide polymorphisms close to genes in the mammary development, prolactin signalling and involution pathways were significantly associated with milk production traits. The involution pathway explained the largest proportion of genetic variation for production traits. The mammary development pathway also explained additional genetic variation for milk volume, fat percentage and protein percentage.

Conclusions

Genetic variants in the involution pathway explained considerably more genetic variation in milk production traits than expected by chance. Many of the associations for single nucleotide polymorphisms in genes in this pathway have not been detected in conventional genome-wide association studies. The pathway approach used here allowed us to identify some novel candidates for further studies that will be aimed at refining the location of associated genomic regions and identifying polymorphisms contributing to variation in lactation volume and milk composition.

Background

There have been many attempts to identify the genes that control milk production and functional traits in dairy cattle since they have high economic value [1, 2]. Linkage studies and genome-wide association studies (GWAS) have led to the identification of a handful of causative mutations that affect milk production traits in dairy cattle [37]. However, the mutations that underlie most of the genetic variation remain elusive, reflecting the fact that the majority of these mutations are likely to have small effects and, therefore, individually explain a small proportion of the genetic variance [8, 9]. New methods are needed to analyse the large quantity of genetic information provided by high-density SNP (single nucleotide polymorphism) panels in order to identify novel genetic variants that have a functional role in lactation traits.

One potential approach is to first filter genetic variants for association analysis by considering pathways of genes that are likely to be involved in lactation. The advantage of this method is that less stringent significance thresholds can be used than in traditional GWAS, since the level of multiple testing is not as high. This also means that associations of smaller effect can be detected. However, the approach does have the limitation that any mutations that affect the traits outside the selected pathways will be missed, which means that the variation we can identify may be reduced compared with that from whole-genome studies.

For dairy traits, genes that are involved in mammary gland development, prolactin signalling and involution pathways are relevant candidates. Genes in the lactation pathway have been well-described but are largely inferred from mouse studies [1013]. Development of the mammary gland (or mammogenesis) involves the formation of the rudimentary mammary structure before puberty and is triggered by secreted signalling proteins and transcription factors that regulate developmental processes, such as the Wnt, notch and hedgehog signalling pathways [12]. When the mammary structure begins to form, genes for growth hormone and proteins involved in basement membrane architecture are expressed. At puberty, the concentration of several hormones increases and stimulates the formation of alveolar buds [14]. Prolactin signalling is vital for lobulo-alveolar development and establishment of lactation but appears less important after teat formation in dairy cattle [15, 16]. One hypothesis is that in cattle, prolactin may be more important for immune support at calving [17]. Prolactin interacts with its receptors to trigger paracrine signalling mechanisms through a highly regulated feedback mechanism involving JAK/STAT and map kinase activity, as well as other downstream targets, which in turn regulate proliferation and cell differentiation [14]. In involution, milk producing epithelial cells are removed via cell detachment and apoptosis. Cytokines, interleukins and MMP (matrix metalloproteinases) are involved in complex signal transduction cascades to regulate proliferation and apoptosis in this pathway. The mammary epithelium undergoes several rounds of proliferation, differentiation and apoptosis over up to eight lactations in dairy cattle [18]. These processes are regulated by a number of genes, which represent excellent candidates for harbouring mutations that explain part of the observed variation in milk production traits and thus link genetic variation with the biological mechanisms underlying the phenotype. In this study, we have assembled sets of genes involved in mammary gland development, prolactin and involution biological pathways. Then, we tested SNPs in windows of 200 kb surrounding these genes for association with milk production traits in dairy cattle. Our hypothesis is that genes in these pathways will harbour genetic mutations that explain variation in production traits in dairy cattle, and that our approach will detect more of these associations than a traditional GWAS, since we can test variants at lower significance thresholds because of the smaller number of tests conducted.

Methods

Genome-wide association studies

To determine whether SNPs within key lactation pathways were significant for milk production traits, an association analysis was used. We analysed several traits, including fat kg, fat percentage, milk volume, protein kg, and protein percentage [19, 20]. A total of 16 812 dairy cattle were genotyped using the Illumina Bovine HD BeadChip, or the BovineSNP50 array [21] and imputed to the higher density [22] (1785 animals were actually genotyped at the higher density). After quality control (as in [22]), the final number of SNPs was 632 003. The genotyped animals included 9015 Holstein cows, 2770 Holstein bulls, 4202 Jersey cows, and 825 Jersey bulls [see Additional file 1: Table S1]. Phenotypes of bulls and cows were constructed as daughter trait deviations (the average of the bull’s daughters trait deviations corrected for breed of mate) and trait deviations, respectively (corrected for herd year season and permanent environment effects) [see Additional file 2: Table S2]. The distributions of the number of lactations (for cows) and daughters (for bulls) are in Additional file 3: Figure S1. Records were standardised in both breeds to have a mean of 0 and a standard deviation of 1. In all analyses, phenotypes on bulls were weighted as

1 h 2 1 + 4 h 2 n
where n represents the number of records [23] and h 2 is the heritability of the trait (0.33 for milk volume, fat kg and protein kg, and 0.5 for protein percentage and fat percentage, for both breeds [20]). Phenotypes on cows were weighted using the formula [23]:
1 h 2 1 + r 2 l 1 l

where r2 is the repeatability (0.56 for all milk production traits) and l is the number of lactations. For the percentage traits, we were not able to fit weights for bulls in the model due to problems with convergence, likely because the heritability for these traits was high.

The linear mixed model used to determine the association between individual SNP and each milk production trait:
y = + Wb + Zu + e

where y is the vector of phenotypes, expressed as the trait deviations for cows and daughter trait averages for bulls, β is the vector of fixed effects, including the overall mean and the effects of breed and sex, X is a design matrix allocating phenotypes to fixed effects, W is the vector of animal genotypes (the number of copies of the second allele at the SNP that the animal carries, coded as 0, 1 or 2), b is the additive effect of the second allele of the SNP, Z is an incidence matrix mapping phenotype to animals, u is the vector of polygenic effects (one for each animal), and e is the vector of random residuals. The polygenic breeding values were fitted as random effects following a normal distribution N 0 , A σ α 2 where A is the expected relationship among individuals constructed from the pedigree (which dates back to the 1940s) and σ a 2 is the polygenic genetic variance. Variance components and fixed effects were estimated for each SNP with ASReml [24].

Analysis of key lactation pathways

Gene sets for analysis were chosen using published reviews of three important developmental stages of the lactating mammary gland. These included the mammary development pathway [12] and the prolactin signalling [14] and involution pathways [25]. We identified 64 genes involved in mammary development, 27 genes involved in prolactin signalling, and 40 genes involved in involution (Tables 1, 2 and 3). The gene families MAP kinase, P13K and frizzled were not included in the pathways since specific genes were not suggested in the reviews and these gene families have a wide range of signalling functions. The genomic location of these genes were determined using UMD3.1 in the NCBI database [26]. The SNPs within the genes of a pathway, or within 100 kb to each side of those genes, were then tested for association with each trait using the model above. The effect of a SNP was determined to be significant at P ≤ 0.05. The GWAS was repeated using other significance thresholds (P < 10−3 and P < 10−5) but 0.05 had the greatest power to detect enrichment (results not shown). The number of SNPs significant for each pathway was expressed as a proportion of the total number of SNPs in that pathway (PropSig).
Table 1

Proportion of significant SNPs for genes in the mammary development pathway and number of SNPs significantly (P < 0.05) associated with each trait

Gene

Chr

Start

Stop

Nb SNPs

Fat

Fat %

Milk

Protein

Prot %

ADAM17

11

87798074

88040943

84

0.51

0.10

0.06

0.48

0.14

AREGB

6

91026256

91238391

53

0.42

0.60

0.55

0.62

0.64

BMP4

10

66651296

66855026

51

0.10

0.45

0.22

0.20

0.24

BMPR1A

28

41717915

41975988

55

0.00

0.02

0.20

0.31

0.04

CSN2

6

87079502

87288025

58

0.40

0.59

0.83

0.78

0.90

CCND1

29

47444380

47653820

43

0.35

0.35

0.16

0.12

0.44

DKK1

26

6752970

6955647

48

0.50

0.13

0.58

0.58

0.25

EDAR

11

44351547

44567795

31

0.26

0.13

0.23

0.39

0.23

EGF

6

16465618

16768065

103

0.08

0.09

0.12

0.12

0.00

EGFR

22

792005

1169280

81

0.23

0.05

0.11

0.16

0.05

ESR1

9

89869586

90355801

103

0.21

0.19

0.33

0.38

0.13

FGF1

7

55408007

55701836

65

0.60

0.15

0.11

0.58

0.80

FGF10

20

30510292

30719199

30

0.03

0.50

0.50

0.00

0.63

FGFR1

27

33150508

33400219

43

0.14

0.21

0.19

0.47

0.19

GH1

19

48668618

48872014

73

0.08

0.16

0.32

0.26

0.34

GHR

20

31790736

32299996

97

0.62

0.93

0.84

0.54

0.96

GLI2

2

72877209

73268370

82

0.16

0.10

0.11

0.27

0.05

GLI3

4

79344243

79858476

71

0.23

0.20

0.38

0.32

0.25

IGF1

5

66432877

66704734

49

0.37

0.24

0.31

0.24

0.18

IGF1R

21

8108822

8368093

61

0.07

0.05

0.11

0.10

0.07

IRS1

2

115690540

115894253

22

0.18

0.32

0.14

0.41

0.09

IRS2

12

88564525

88769181

64

0.28

0.02

0.55

0.28

0.64

LEF1

6

18235031

18550774

59

0.25

0.08

0.20

0.31

0.00

MFGE8

21

20789913

21004968

52

0.21

0.42

0.37

0.33

0.29

MMP14

10

21706054

21914533

43

0.19

0.02

0.12

0.19

0.00

MMP2

18

23728638

23955657

94

0.12

0.11

0.12

0.10

0.55

MMP3

15

5928011

6134595

52

0.06

0.23

0.15

0.13

0.02

MMP9

13

75366513

75573824

45

0.33

0.04

0.56

0.53

0.04

MSX1

6

105961463

106165759

56

0.43

0.61

0.46

0.38

0.50

MSX2

20

6260600

6465489

39

0.05

0.49

0.41

0.18

0.54

NRG1

27

27523938

27933470

79

0.18

0.24

0.29

0.30

0.15

NRG3

28

38201492

38451092

66

0.02

0.02

0.00

0.02

0.00

NTN1

19

28984419

29369338

87

0.36

0.23

0.37

0.34

0.31

PGR

15

8004485

8322755

64

0.48

0.13

0.38

0.48

0.06

PCBD1/TCF1

28

27126795

27331724

67

0.34

0.30

0.13

0.16

0.19

PRL

23

35005135

35213759

53

0.19

0.04

0.11

0.19

0.36

PRLR

20

38973246

39237480

56

0.48

0.61

0.50

0.25

0.82

PTHLH

5

82146522

82358858

30

0.30

0.50

0.47

0.33

0.33

PTH1R

22

53061302

53324114

48

0.40

0.15

0.13

0.35

0.23

PTH

15

39628332

39830868

31

0.23

0.06

0.58

0.19

0.26

TNFRSF11A

24

61139109

61375194

65

0.15

0.08

0.03

0.02

0.18

TNFSF11

12

12641069

12882474

73

0.33

0.56

0.05

0.08

0.51

RELN

4

44792394

45389293

131

0.60

0.07

0.69

0.34

0.47

SIRPA

13

53567570

53810792

80

0.70

0.09

0.46

0.73

0.48

SLIT2

6

41136589

41740789

145

0.09

0.18

0.17

0.19

0.46

SOCS1

25

9875299

10075970

56

0.30

0.00

0.16

0.32

0.00

SOCS2

5

23423981

23628860

46

0.28

0.67

0.59

0.52

0.67

SOCS3

19

54358856

54559555

62

0.44

0.21

0.18

0.40

0.50

STAT5A

19

42933597

43154075

59

0.36

0.85

0.61

0.75

0.80

STAT5B

19

42860226

43096671

60

0.10

0.83

0.78

0.55

0.80

TBX2

19

11843185

12051411

81

0.11

0.15

0.14

0.17

0.41

TBX3

17

62252245

62463636

45

0.27

0.20

0.33

0.18

0.29

TCF3

7

45499593

45730734

34

0.44

0.00

0.41

0.50

0.21

TCF4

24

54956409

55261459

73

0.03

0.14

0.19

0.23

0.05

TGFA

11

13772149

14086616

65

0.42

0.26

0.40

0.55

0.11

TGFB1

18

50671354

50885924

55

0.42

0.58

0.49

0.71

0.65

TGFBR1

8

64470093

64741796

64

0.23

0.33

0.33

0.27

0.41

TGFBR2

22

5041232

5333083

92

0.14

0.13

0.15

0.12

0.07

WAP

4

77111371

77312672

35

0.26

0.20

0.17

0.23

0.31

WNT10B

5

30913104

31114446

44

0.14

0.91

0.91

0.09

0.86

WNT11

15

56284700

56504335

51

0.37

0.35

0.22

0.31

0.12

WNT3

19

45921803

46171153

55

0.11

0.36

0.13

0.11

0.78

WNT5A

22

45996228

46212683

45

0.02

0.02

0.09

0.18

0.33

WNT6

2

107444683

107656681

64

0.34

0.52

0.56

0.44

0.34

N = 64

   

3968

1079

1034

1247

1249

1350

Bold values indicate where >50% of SNPs in a gene region were significant.

Table 2

Proportion of significant SNPs for genes in the prolactin pathway and number of SNPs significantly (P < 0.05) associated with each trait

Gene

Chr

Start

Stop

Nb SNP

Fat

Fat %

Milk

Protein

Prot %

AKT2

18

49804012

50050072

55

0.38

0.31

0.13

0.20

0.11

CSN1S1

6

87041556

87259096

98

0.07

0.18

0.05

0.09

0.22

CSN2

6

87079502

87288025

58

0.40

0.59

0.83

0.78

0.90

CISH

22

50220205

50425617

38

0.21

0.00

0.21

0.24

0.32

RAF1

22

57022412

57304951

115

0.34

0.06

0.47

0.52

0.01

ELF5

15

65724442

65954386

80

0.25

0.40

0.30

0.34

0.18

ERBB4

2

99560620

100097642

71

0.35

0.20

0.37

0.42

0.14

ESR1

9

89869586

90355801

103

0.21

0.19

0.33

0.38

0.13

GAL

29

46659818

46865617

59

0.15

0.29

0.07

0.31

0.37

GATA3

13

15884602

16102940

29

0.21

0.14

0.24

0.24

0.17

IGF2

29

49946626

50165230

27

0.37

0.04

0.30

0.22

0.15

IL6

4

31478311

31682667

25

0.40

0.00

0.24

0.36

0.00

IRS1

2

115690540

115894253

22

0.18

0.32

0.14

0.41

0.09

JAK2

8

39531342

39850796

32

0.28

0.47

0.63

0.56

0.44

NR3C1

7

56131970

56450496

65

0.63

0.37

0.28

0.52

0.43

PRL

23

35005135

35213759

53

0.19

0.04

0.11

0.19

0.36

PRLR

20

38973246

39237480

56

0.48

0.61

0.50

0.25

0.82

PTH

15

39628332

39830868

31

0.23

0.06

0.58

0.19

0.26

SOCS1

25

9875299

10075970

56

0.30

0.00

0.16

0.32

0.00

SOCS2

5

23423981

23628860

46

0.28

0.67

0.59

0.52

0.67

SOCS3

19

54358856

54559555

62

0.44

0.21

0.18

0.40

0.50

GH1

19

48668618

48872014

73

0.08

0.16

0.32

0.26

0.34

STAT3

19

42956660

43232624

58

0.43

0.84

0.53

0.78

0.84

STAT5A

19

42933597

43154075

59

0.36

0.85

0.61

0.75

0.80

STAT5B

19

42860226

43096671

60

0.10

0.83

0.78

0.55

0.80

TNFRSF11A

24

61139109

61375194

65

0.15

0.08

0.03

0.02

0.18

TNFSF11

12

12641069

12882474

73

0.33

0.56

0.05

0.08

0.51

N = 27

   

1569

458

519

571

636

628

Bold values indicate where >50% of SNPs in a gene region were significant.

Table 3

Proportion of significant SNPs for genes in the involution pathway and number of SNPs significantly (P < 0.05) associated with each trait

Gene

Chr

Start

Stop

Nb SNP

Fat

Fat %

Milk

Protein

Prot %

AKT1

21

70778138

70995537

30

0.17

0.17

0.13

0.13

0.17

ATF4

5

111362845

111564936

52

0.54

0.12

0.52

0.23

0.60

BAK1

23

7555892

7758885

31

0.19

0.16

0.23

0.03

0.45

BAX

18

55885202

56089378

35

0.11

0.49

0.51

0.40

0.49

BCL2L1

13

61666806

61917383

28

0.04

0.07

0.50

0.36

0.11

CASP3

27

13984622

14210610

49

0.12

0.10

0.16

0.18

0.08

CEBPA

18

43828610

44029840

30

0.10

0.13

0.20

0.07

0.13

CEBPD

14

20638814

20840407

28

0.46

0.25

0.46

0.43

0.32

CEBPG

18

43905707

44112657

68

0.24

0.06

0.15

0.16

0.03

CISH

22

50220205

50425617

38

0.21

0.00

0.21

0.24

0.32

CTNNA1

7

51588098

51980519

14

0.14

0.79

0.14

0.21

0.00

CTNNA2

11

54622279

56182035

514

0.23

0.40

0.53

0.48

0.38

E2F1

13

63605710

63814008

21

0.62

0.10

0.48

0.43

0.19

FOXO3

9

41908606

42218673

56

0.09

0.02

0.09

0.14

0.00

IGFBP5

2

105278991

105497646

61

0.62

0.08

0.21

0.64

0.72

IL11

18

62461915

62664977

61

0.51

0.11

0.05

0.36

0.16

IL6

4

31478311

31682667

25

0.40

0.00

0.24

0.36

0.00

IL6ST

20

23112633

23370316

64

0.23

0.39

0.28

0.39

0.67

IRF1

7

23135653

23343697

46

0.72

0.00

0.61

0.67

0.20

JAK1

3

80675557

81015026

65

0.18

0.45

0.55

0.35

0.46

JAK2

8

39531342

39850796

32

0.28

0.47

0.63

0.56

0.44

LEF1

6

18235031

18550774

59

0.25

0.08

0.20

0.31

0.00

LIF

17

71313855

71518166

53

0.13

0.02

0.09

0.26

0.08

LIFR

20

35817479

36066671

74

0.54

0.58

0.68

0.58

0.80

MMP2

18

23728638

23955657

94

0.12

0.11

0.12

0.10

0.55

MMP3

15

5928011

6134595

52

0.06

0.23

0.15

0.13

0.02

MMP9

13

75366513

75573824

45

0.33

0.04

0.56

0.53

0.04

MYC

14

13669244

13874438

38

0.61

0.76

0.24

0.13

0.32

OSM

17

71334468

71537372

51

0.16

0.06

0.06

0.24

0.08

OSMR

20

35421410

35688186

53

0.60

0.70

0.64

0.58

0.74

TP53

19

27885495

28097841

23

0.43

0.48

0.22

0.43

0.17

PTEN

26

9398226

9695849

33

0.03

0.36

0.42

0.36

0.42

PTK2

14

3770893

4165010

121

0.79

0.90

0.84

0.66

0.82

RAF1

22

57022412

57304951

115

0.34

0.06

0.47

0.52

0.01

SFRP4

4

49909882

50120466

51

0.22

0.49

0.59

0.57

0.47

SOCS3

19

54358856

54559555

62

0.44

0.21

0.18

0.40

0.50

STAT3

19

42956660

43232624

58

0.43

0.84

0.53

0.78

0.84

STAT5A

19

42933597

43154075

59

0.36

0.85

0.61

0.75

0.80

STAT5B

19

42860226

43096671

60

0.10

0.83

0.78

0.55

0.80

TIMP3

5

71651415

71909052

72

0.54

0.21

0.49

0.38

0.42

N = 40

   

2521

803

841

1048

1044

972

Bold values indicate where >50% of SNPs in a gene region were significant.

To determine if the proportion of significant SNPs observed for each pathway was significantly greater than by chance at an experiment-wise level, distributions under the null hypothesis of no association were constructed with random permutations of the data. A list of 24 617 uniquely annotated bovine genes was created from the Ensembl Biomart database [27, 28]. From this, three sets of genes, each with a length equal to the respective pathway tested were selected at random. SNPs were selected from within and 100 kb surrounding these genes to reflect the moderate to high linkage disequilibrium in Holstein cattle [29, 30]. Each pathway SNP set was analysed in ASReml using the mixed linear model described above. This procedure was repeated 10 000 times to construct null distributions and the 500th highest proportion of significant SNPs was taken at the experiment-wise P < 0.05 threshold. If the observed ratio for a pathway was greater than this value for a particular trait, the pathway was considered significant.

To account for differences in functional clustering of genes in the experimental pathways and in the random control gene sets, we compared the distance between genes on the same chromosome [see Additional file 4: Figure S2]. The experimental and control sets were distributed similarly but, due to the smaller number or paired genes for the experimental pathways, there were fewer gene pairs at long distances across the chromosomes (particularly > 10 Mb).

KEGG annotations were used to determine the gene sets that represented other biological pathways [31, 32].

Finally, a variance component analysis was used to determine whether the SNPs within each pathway explained a greater proportion of the genetic variance than an equal number of randomly selected SNPs from the whole genome. The model fitted was
y = W b + Zg + e ,

where terms were the same as above, and g is a vector of random effects, assumed distributed N 0 , G σ g 2 , where G is a genomic relationship matrix, constructed using the rules of [33]. The genomic relationship matrix was based on the SNPs from each pathway, plus a set of 4000 SNPs randomly selected from the whole genome. The reason for adding the 4000 randomly chosen SNPs was that SNPs in the genes of the pathways are typically clustered by genomic location (i.e. a number of the genes are located in close proximity) [see Additional file 4: Figure S2]. Given the large number of animals in our dataset, this means that a considerable number of animals can have genomic relationships that are equal to or close to 1, i.e. they have inherited the same segments of the genome at all of the locations of the pathway genes. Consequently, the genomic relationship matrix is singular and impossible to invert. Adding 4000 random SNPs removed the singularities and the genomic relationship matrix could be inverted and variance components estimated. However, with the 4000 SNPs included, we could only assess the marginal contribution of adding SNPs in the pathway.

Estimates of the variance components σ g 2 and σ e 2 were obtained from the REML analysis with ASREML [24]. The proportion of variance explained by the SNPs in these pathways was compared to that explained by the same number of randomly chosen SNPs within 100 kb of a gene, i.e. the additional SNPs were chosen to be close to genes, plus the set of 4000 randomly chosen SNPs corresponding to each pathway. Five replicates of the randomly chosen sets were performed to obtain standard errors.

Results

Mammary development pathway

The 64 genes identified in the mammary development pathway included 3968 SNPs (Table 1). When the proportion of significant SNPs, at P < 0.05, (PropSig) was compared to the null distributions, the mammary development pathway was significantly associated with protein percentage (PropSig = 0.340, P < 0.01; Table 4 and Additional file 5: Figure S3). The null distributions compared with the experimental results are shown in Additional file 5: Figure S3, Additional file 6: Figure S4 and Additional file 7: Figure S5. The genes that contained the largest proportion of significant SNPs (> 50% significant SNPs) were the following: AREGB, CASB, DKK1, FGF1, FGF10, GHR, PRLR, SOCS2, STAT5A, STAT5B, TGFB1 and WNT10B (Table 1 and Additional file 8: Table S3 for gene abbreviations).
Table 4

Proportion of significant SNPs for milk production traits in the mammary development, prolactin and involution pathway genes

 

Mammary development

Prolactin Signalling

Involution

Trait

PropSig

μ( PropSig)

PropSig

μ( PropSig)

PropSig

μ( PropSig)

Fat

0.272

0.266

0.292

0.267

0.319

0.267

Milk

0.314

0.295

0.364

0.296

**0.415

0.295

Protein

0.315

0.314

*0.405

0.315

**0.414

0.315

Fat %

0.261

0.221

**0.331

0.222

**0.333

0.221

Protein %

**0.340

0.262

*0.400

0.262

**0.385

0.262

Nb SNP

3968

 

1569

 

2521

 

The observed proportion of SNPs significant was compared to the mean (μ) of the simulated null distribution (μ(PropSig)), created by permutation testing, to determine if the observed proportion was significant at an experiment-wise level (* denotes significant at P < 0.05 experiment-wise, ** denotes significant at P < 0.01 experiment-wise) (see Methods). The total number of SNPs is presented in bold.

Four genes in the mammary development pathway were located on BTA20, which contains a well-known QTL for milk production [5]. These genes included FGF10, MSX2, PRLR and GHR. FGF10 is located 1 Mb downstream of GHR, which is the gene often described with, though not necessarily underlying [34], this large QTL. To account for any potential bias associated with over-represented genes, we re-ran the pathway test and control permutations without BTA20. The mammary development pathway still reached significance for protein percentage when this chromosome was removed [see Additional file 9: Figure S6].

KEGG annotations of these 64 genes found 25 genes in pathways associated with cancer and 8 to 14 other genes in signalling pathways, such as JAK-STAT, that are known to be activated during lactation (Table 5). The PI3K-Akt pathway is involved in mammary development, and mutations in genes of this pathway are found in approximately 70% of breast cancers [35]. There were eight genes involved in Wnt signalling pathways, which are prominent in mammary development and cancers [36].
Table 5

KEGG associations for the mammary development, prolactin signalling and involution pathways

Mammary development

ID

Pathway

Nb Genes

bta05200

Pathways in cancer

25

bta05166

HTLV-I infection

14

bta04151

PI3K-Akt signalling pathway

14

bta04060

Cytokine-cytokine receptor interaction

12

bta04630

Jak-STAT signalling pathway

10

bta05217

Basal cell carcinoma

9

bta04380

Osteoclast differentiation

8

bta05218

Melanoma

8

bta04310

Wnt signalling pathway

8

bta04010

MAPK signalling pathway

8

Prolactin signalling

ID

Pathway

Nb Genes

bta04630

Jak-STAT signalling pathway

12

bta04151

PI3K-Akt signalling pathway

8

bta04910

Insulin signalling pathway

6

bta04380

Osteoclast differentiation

5

bta05200

Pathways in cancer

5

bta05161

Hepatitis B

5

bta05164

Influenza A

5

bta04920

Adipocytokine signalling pathway

5

bta05162

Measles

5

bta04060

Cytokine-cytokine receptor interaction

5

Involution

ID

Pathway

Nb Genes

bta05200

Pathways in cancer

18

bta04630

Jak-STAT signalling pathway

15

bta05161

Hepatitis B

14

bta04151

PI3K-Akt signalling pathway

13

bta05166

HTLV-I infection

9

bta05203

Viral carcinogenesis

9

bta05152

Tuberculosis

8

bta05213

Endometrial cancer

8

bta05210

Colorectal cancer

7

bta05202

Transcriptional misregulation in cancer

7

To determine the extent of pleiotropy for variants in the pathway, we correlated the SNP effect estimates (for the 3968 SNPs in the pathway) for each pair of traits. Milk volume was negatively correlated with fat percentage and protein percentage, while fat percentage and protein percentage were highly positively correlated (Table 6). Fat kg and milk volume were also highly positively correlated with protein kg, as expected.
Table 6

Correlation between core traits for SNP within the mammary development, prolactin signalling and involution pathways

Mammary development

  
 

Fat

Milk

Protein

Fat %

Milk

0.490

   

Protein

0.753

0.703

  

Fat %

0.221

-0.719

-0.178

 

Protein %

0.121

-0.618

0.116

0.792

Prolactin signalling

  
 

Fat

Milk

Protein

Fat %

Milk

0.524

   

Protein

0.695

0.663

  

Fat %

0.140

-0.748

-0.227

 

Protein %

0.145

-0.456

0.357

0.643

Involution

   
 

Fat

Milk

Protein

Fat %

Milk

0.097

   

Protein

0.364

0.825

  

Fat %

0.442

-0.833

-0.539

 

Protein %

0.225

-0.774

-0.288

0.806

Bold values represent a high correlation, italicised values represent a moderate correlation and all other values correspond to low to zero correlation.

Prolactin signalling pathway

The prolactin signalling gene set was considerably smaller (27 genes, 1569 SNPs) than the involution and mammary development sets, since it only represents only one signalling pathway, while mammary development and involution represent the combined effects of several sub-pathways (Table 2). Protein kg, fat kg and fat percentage were significantly associated with the prolactin signalling gene set (Table 4) and [see Additional file 6: Figure S4]. The SOCS2, STAT3, STAT5A, STAT5B, PRLR and CASB genes had more than 50% of SNPs significant for three or more milk production traits (Table 2).

KEGG annotations for genes in the prolactin pathway showed 12 associations with the JAK-STAT signalling pathway, followed by the PI3K-Akt and insulin signalling pathways (Table 5).

Involution pathway

The involution pathway contained 40 genes and 2521 SNPs (Table 3). The proportion of associated SNPs was significant at the experiment-wise level for all milk production traits, except fat [see Additional file 7: Figure S5] and (Table 4). We identified a large ratio of significant SNPs for ATF4, IGFBP4, IRF1, LIFR, OSMR, PTK2, STAT3, STAT5A and STAT5B (Table 3). KEGG analysis showed a trend towards infection-related pathways (Table 5). JAK-STAT, hepatitis B and PI3K signalling pathways were also highly represented. Traits showed moderate to high correlations, which suggested pleiotropy for milk production traits within SNPs in the involution pathway (Table 6).

Three genes in the involution pathway were located on BTA14 and may be biased by associations with the large QTL at the beginning of BTA14 associated with the mutation in DGAT1[37]. The CEPBD and MYC genes are located more than 13 Mb upstream of this QTL but PTK2 sits 2 Mb upstream from DGAT1, well within the bounds of this very large QTL. When BTA14 was removed from the analysis, the involution pathway remained significant for the traits for which this was tested [see Additional file 9: Figure S6].

There was some overlap in the genes of the three pathways. Genes STAT5A, STAT5B and SOCS3 were common to all three pathways (Figure 1). Prolactin and mammary development pathways showed the largest overlap, which included TNF, SOCS and prolactin genes. KEGG analyses showed that similar pathways were represented in mammary development and involution but infection-related pathways were more prominent due to the abundance of acute phase response genes such as interleukins and STAT genes (Table 5).
Figure 1

Venn diagram showing the number of overlapping genes in three lactation pathways.

Proportion of variance explained by mutations in pathways

For milk production traits, SNPs in the involution pathway explained 10 to 13% more genetic variation than expected by chance for all traits (Table 7). SNPs in the mammary development pathway explained 7 to 9% more genetic variation than expected by chance for milk, protein percentage and fat percentage. SNPs in the prolactin pathway explained less variation than expected by chance, although results were not significantly different from zero. This could be the result of a combination of two factors, i.e. (1) SNPs within the prolactin signalling pathway do not really explain much variation, and (2) because of the small number of genes in this pathway, the SNPs did not cover all chromosomes (and therefore did not capture variation on those chromosomes), unlike the randomly sampled SNPs. The overall significance of each milk production trait for each pathway tested was very similar, though not identical, to the results from SNP by SNP association testing (perhaps a result of random sampling to construct the null distributions).
Table 7

Additional genetic variance explained by SNPs in genes or within 100 kb of genes in the mammary development, prolactin signalling, and involution pathways, compared with an equal number of randomly chosen SNPs within 100 kb of genes

 

Mammary development

Prolactin signalling

Involution

Milk volume

7.0 ± 3.2

−1.3 ± 1.9

13.3 ± 1.9

Fat kg

2.0 ± 2.7

−1.2 ± 1.7

11.0 ± 1.5

Protein kg

−0.4 ± 3.5

−2.4 ± 2.0

11.6 ± 2.8

Fat %

9.3 ± 3.2

−0.3 ± 1.0

12.1 ± 1.5

Protein %

6.8 ± 3.2

−2.2 ± 1.4

13.0 ± 2.3

Standard errors were derived from the genetic variation explained in five random sets of SNPs; significant (based on the variance explained being greater than 2 standard errors) trait x pathway combinations are in bold.

Discussion

We used information on mammary development, prolactin signalling and involution pathways to identify candidate gene regions that could be associated with milk production traits. SNPs in genes that are involved in the mammary development pathway were highly associated with protein percentage and explained a considerable proportion of the variance for three milk production traits. The prolactin signalling pathway did not explain any additional variance in milk production traits, but contained a significant number of associated SNPs for protein kg, protein percentage and fat percentage. SNPs in genes involved in the involution pathway explained the greatest level of variance in milk production traits in our variance component approach. The involution pathway was also significant for all milk production traits except fat in the association testing approach.

Mammary development, prolactin signalling and involution pathways contained highly significant genes that have been described in GWAS or are known to be important lactation genes. These include, CASB, SOCS2, GHR, PRLR, LIFR and the STAT genes. In particular, SNPs within STAT5A have a large effect on milk composition and have been validated in vitro[38, 39]. Figure 2 shows a GWAS for protein percentage as an example, and displays the relationship between genes studied from these pathways and genome-wide QTL patterns. Most genes are located in regions that could not be identified by a traditional GWAS. SNPs within regions not previously associated with milk production traits, such as AREGB, ATF4, IRF1, DKK1, and TGFB1, which were significant for mammary development, may contain novel mutations that affect milk production traits and may represent key genes from the mammary development pathway that explain some of the variance in these traits in cattle.
Figure 2

GWAS of protein percentage in Holsteins and Jerseys. SNPs within the mammary development, prolactin signalling, and involution pathways are highlighted as red, blue and green dots, respectively; * identifies chromosomes 14 and 20, which have been scaled down to allow observation of smaller effects.

The reason why the involution pathway explained the greatest level of variance in milk production traits in our variance component approach, although only half the number of SNPs of the mammary development pathway were available, could be because this pathway includes genes in or close to a previously described QTL with quite large effects on milk production traits (Figure 2), particularly protein percentage [5]. However, when the analysis was ran without the genes on BTA20 (FGF10, MSX2, PRLR and GHR), this pathway was still significant, even for protein percentage. Note that removing the GHR gene from the analysis is questionable because the growth hormone receptor is a vital component of the lactation pathway since it interacts with several relevant substrates during lactation [5]. Similarly, removing the CEBPD, MYC and PTK2 genes on BTA14 (because they were in the region of DGAT1) did not affect the overall significance of the mammary development pathway. The clustered expression of the genes in a pathway, i.e. they are expressed with other secreted milk genes [40], may result in significant associations that are due to nearby, co-expressed genes. The permutation method generated some replicates with similar genome distributions to the experimental data [see Additional file 4: Figure S2], which implies that the clustered expression of genes probably does not greatly affect the results. There is currently no ideal approach to control for the complicated genetic architectures of traits in pathway analyses. While these genetic structures should be accounted for, caution should be taken to avoid losing information from highly relevant genes.

One of the main limitations of our approach is that if a mutation that affects milk production is not in the analysed pathways, it will automatically be excluded. Perhaps even more importantly, our interpretations could be biased if irrelevant genes are included in the pathways. This may have occurred in cases where broad-acting cellular processes are represented in the gene sets. Improved descriptions of pathways would increase the power to identify genomic regions that influence these traits. The pathways used in this study were primarily derived from mouse studies and are relatively poorly described in cattle. For mammary development, the signalling interactions in the placode epithelium are particularly poorly described. For the prolactin signalling pathway, little is known about the downstream signalling of progesterone receptors. For the involution pathway, it is not known how membrane apoptosis is triggered although this would represent a significant contribution to the description of this biological process. Approaches such as microarray and RNAseq technologies using time-course data could help refine this method so that it represents more closely the true biological action. These approaches have successfully identified genes acting at different physiological states in the lactation cycle. Another potential limitation of our study is that the phenotypes were averages of several records across lactation. The same analyses could be performed using just early or late lactation records. Lactation curve parameters have been used in similar modelling experiments and may further refine these numerous SNP associations [41].

Finally, the value of KEGG pathway annotations was questionable. The relevance of these annotations for the target traits is difficult to establish for genes that are involved in broad and numerous biological processes. A further problem is that KEGG annotations are heavily dominated by cancer-related information.

Conclusions

We have successfully used the information from characterised mammary development, prolactin signalling and involution pathways to identify novel SNP associations with milk production traits. The proportion of significant SNPs in or near genes from the mammary development pathway was considerably greater than expected by chance for protein percentage. Of the three pathways studied, the involution pathway was highly associated with milk production traits and explained the highest level of variation above that expected by chance (up to 13% for protein kg). While we have reported many novel candidates useful for further studies, we must point out that pathway-based methods are restricted by the quality of annotations and completeness of pathway information.

Declarations

Acknowledgements

LR is supported by the Dairy Futures CRC Australia.

Authors’ Affiliations

(1)
Biosciences Research Division, Department of Primary Industries Victoria, AgriBio
(2)
La Trobe University
(3)
Dairy Futures Co-operative Research Centre
(4)
Faculty of Land and Food Resources, University of Melbourne

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© Raven et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

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