Livestock Research for Rural Development 24 (7) 2012 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Genotype by environment interaction for milk production traits in Iranian Holstein dairy cattle using random regression model

Mehdi Bohlouli and Sadegh Alijani

Department of Animal Science, University of Tabriz, Tabriz, Iran
M.bohluly@gmail.com

Abstract

In this research the existence of genotype by environment interaction (G E)on milk production traits (milk yield, fat yield, protein yield, fat percentage and protein percentage) was investigated by considering relevant records in different production levels as different traits. Production level (PL) considered as the average milk production traits of herd-year calving.Data on Holstein cows from 2001 to 2010 were used. Herd-years of calving means for milk production traits were clustered in three levels using the FASTCLUS procedure in SAS software. Production levels included low, medium and high levels. For these traits, G E were investigated by applying a multiple-trait random regression sire model.


Additive genetic and permanent environmental variances of milk production traits varied in different production levels. Estimated heritabilities for milk production traits as a function of days in milk were highest in high production levels with an exception for protein percentage. Generally, the highest heritability estimates of 305-d milk production traitswere found in high PL rather than low PL. Low spearman correlations between estimated breeding values of the 20 top sires among low and medium PL (0.38) and between low and high PL (0.39) of milk yield showed re-ranking of sires for these levels.The greatest GE was observed for milk yield and protein percentage, with a genetic correlation for 305-d equal to 0.79 between low and high production levels. Results from this research indicated that milk production of daughters of the same sires depends greatly on the production environment.  

Keywords: Genetic Correlation, Genotype by Environment Interaction, Production Levels, Random Regression Model


Introduction

The environments in which dairy farming is practiced in Iran vary in many ways, such as the average herd production, level of feeding, elevation and climate variables including temperature and humidity. The phenomenon by which different genotypes respond differently to changes in their environments is known as genotype by environment interaction (GE) or as differences in environmental sensitivity of genotypes (Falconer and Mackay 1996). This interaction can cause different ranking of animals across environments or a change of scale, i.e., variance, across environments (Lynch and Walsh 1998). If a genotype by environment interaction exists, the ranking of sires for milk production traits of their daughters will vary from one environment to another. However, re-ranking of sires across environments is limited for milk production traits (Veerkamp et al 1995; Cromie et al 1998; Calus et al 2002); although there is evidence that variances and heritabilities vary. An environmental parameter reflected the environment encountered by the animals; such as production level of herds (Veerkamp and Goddard 1998; Calus et al 2002), or other characteristics of the herds, such as average age at calving (Fikse et al 2003).

 

Typically, estimated genetic correlations across environments are used to estimate the degree of re-ranking. Genotype by environmental interaction is reflected by the genetic correlation between different production levels. High estimates of genetic correlations between environments (>0.80) suggest no evidence for strong G E (Robertson 1959). Using herd production level as a substitute for the level of feeding, Veerkamp and Goddard (1998) reported a genetic correlation of 0.79 between Australian dairy herds with <20 kg of milk yield per day and herd with >24 kg of milk production per day.

 

 Calus et al(2002) reported similar results for genetic correlations between extreme classes of herd production level in Dutch dairy cattle.Hammami et al (2008) reported that genetic correlation for 305-days milk between Tunisia and Luxemburg countries was 0.60 and spearman rank correlation between estimated breeding values (EBV) of common sires for this trait from within-country analyses was 0.41. Kolver et al (2002) reported significant GE for milk and fertility traits when the performance of New Zealand and imported Holstein Friesian dairy cows were compared on all pasture or total mixed ration systems.The purpose of the present research was to estimate genotype by environment interaction for milk production traits and to investigate the effects of production levels on re-ranking of sires EBVs.


Material and methods

Data

Total of test-day records for first lactation Holstein cows from 2001 to 2010 were extracted from the Animal Breeding Center database at Karaj, Iran. Records were edited on the following criteria: cows with known sires and having age at first calving from 21 to 46 month, edits excluded irregular data for daily milk yield (<1.0 and >75 kg), fat percentage (<1.5% and >9%), and protein percentage (<1% and >7%), cows were required to have a minimum of five test-day (TD) records between 5 and 305 days in milk (DIM) and herd-year of calving subclasses with at last 10 cow records. Additional data edits eliminated sires that had progeny in fewer than three herds and herds that used fewer than three sires.

 

The FASTCLUS procedure of SAS software (SAS 2003) was used to clustering and average of trait in herd-year of calving grouped in three clusters. Common sires had at last 10 daughters in each group of production levels. The pedigree file included animal code, sire code and maternal grandsire code. Therefore, all of the sires had genetic relationship with common sires kept in the analysis.

Statistical analysis

For examining a genotype production levels interaction, the performances of cows in production levels were considered as different traits. Thus, the following multiple-trait sire model, which considered the performance of daughters of sires in the three production levels as different correlated traits, was fitted:

 

 

The following (co)variance structure was assumed for random effects of model:

 

where G is sire genetic (co)variance matrix among random regression coefficients and A is additive numerator relationship matrix between sires. The matrix P was the cow effects variance-covariance matrix among random regression coefficients, and e was residual variances for each traits and I represents an identity matrix with ones on the diagonal. G and P are 1212 (co)variances matrix of regression coefficients. All across-production levels (co)variances in P equal to zero because this effect was considered independent across production levels.

 

The first four Legendre polynomial functions (Kirkpatric et al 1990) were given as: 

where w is a standardized unit of DIM and ranged from -1 to +1. Estimated (co)variance component of milk production traits were obtained by REMLF90 program based on restricted maximum likelihood method (Misztal et al 2002).  

 

Calculation of genetic parameters
 

The sire and permanent environmental (co)variances matrices for each DIM were calculated as:
 

 

In sire model analysis, EBVs were computed via multiplying sire additive genetic value by 2. Spearman rank correlations between EBV of common sires in each group were used to assess the level of re-ranking of sires in different production levels for milk yield, protein yield, fat yield, protein percentage and fat percentage calculated using the procedure CORR of SAS (SAS2003).


Results and discussion

Descriptive statistics

The mean, standard deviation and coefficient of variation of milk production traits and other descriptive statistics for different production levels are summarized in Table 1. The number of cows per common sires was the lowest in the low production level and highest in high production level for milk yield. For other traits, the medium production level has the highest cows number per common sires.For milk yield, the average age in the low production group was larger than in the other levels. Coefficient of determinations by production levels for each trait (using PROCFASTCLUS) were higher than 0.70.PROC FASTCLUS caused that within group’s variances decreased to minimum, and maximized differences between groups; therefore, coefficient of variance in each group was low (Table 1).


Table  1. Descriptive statistics of data sets for milk production traits in different production levels (PL)

 

PL

 

Parameter

Low

Medium

High

Total

Milk yield

 

 

 

 

TD records, no.

87431

261269

478598

827295

Means SD (kg)

19.9 2.42

26.7 1.54

31.2 1.56

27.5 4.08

CV (%)

12.1

5.76

5.00

14.8

Cows, no.

10666

30854

56616

98136

Common sire, no.

373

373

373

373

Cows. /common sire, no.

28.6

82.7

151.8

263

HY, no.

420

951

775

2146

HTD, no.

4782

10214

8003

22999

Age at first calving (avg), mo.

30.2

26.9

26.6

27.0

Fat yield

 

 

 

 

TD records, no.

146561

415377

265114

827052

Means SD (g)

68394.7

91161.4

112690.3

929173

CV (%)

13.9

6.74

8.02

18.7

Cows, no.

17916

53410

33807

105133

Common sire, no.

366

366

366

366

Cows. /common sire, no.

48.9

145

92.4

287

HY, no.

444

1104

658

2206

HTD, no.

4847

12025

7781

24653

Age at first calving (avg), mo.

27.5

26.9

26.5

26.9

Protein yield

 

 

 

 

TD records, no.

46016

326381

217933

590330

Means SD (g)

663102

86348.5

102371.3

891135

CV (%)

15.4

5.62

6.97

15.2

Cows, no.

6845

42802

28325

77972

Common sire, no.

324

324

324

324

Cows. /common sire, no.

21.1

132

87.4

240

HY, no.

263

950

589

1802

HTD, no.

2199

9524

6637

18360

Age at first calving (avg), mo.

27.9

26.8

26.6

26.8

Fat %

 

 

 

 

TD records, no.

199931

376226

136896

713053

Means SD (%)

3.04 0.137

3.36 0.0831

3.67 0.131

3.41 0.248

CV (%)

4.51

2.47

3.57

7.27

Cows, no.

24328

46853

16747

87928

Common sire, no.

405

405

405

405

Cows /common sire, no.

60.1

115

41.4

217

HY, no.

643

1237

515

2395

HTD, no.

6225

12131

5066

23422

Age at first calving (avg), mo.

26.8

27.0

27.0

26.9

Protein %

 

 

 

 

TD records, no.

165047

357267

130133

652446

Means SD (%)

2.92 0.0950

3.11 0.0549

3.36 0.133

3.07 0.142

CV (%)

3.25

1.77

3.96

4.63

Cows, no.

21319

43911

16009

81239

Common sire, no.

430

430

430

430

Cows. /common sire, no.

49.6

102

37.2

188

HY, no.

403

1162

151

1716

HTD, no.

4173

12333

1648

18154

Age at first calving (avg), mo.

26.3

26.8

27.0

26.2


Variance components and heritabilities

 

The sire additive genetic, permanent environmental and residual variances for 305-day production were heterogeneous among production levels, but the sire additive genetic variances of medium and high levels for protein percentage were similar and lower than sire additive variances in low production level (Table 2).


Table 3 reflected (co)variance components for random regression coefficients for calculating of sire additive genetic, permanent environmental  and residual variances ( respectively)  of each trait in different PL. Residual variances for all traits in the low production level were lower than inother levels. Difference in variance component values between PL indicated that there were differences in heritabilities between PL. Heritabilities for milk production traits as a function of DIM in the three levels of production are shown in Figure 1. Heritabilities for milk yield by DIM were higher than for other traits. These heritabilities are in accordance with other studies (Shadparvar and Yazdanshenas 2005; Abdullahpour et al 2010). Generally, heritabilities for milk production traits were the highest in the high production level, except for protein percentage. The highest heritabilities for protein percentage in low PL was due to highest sire additive genetic variances.


Table 3.  (Co)variance components for random regression coefficients of sire additive genetic (G), cow permanent environmental (PE) and residual (R) variances of each trait in different production levels (PL) with multiple-trait random regression sire model

Parameters

 

Milk yield (kg)

 

Fat yield (g)

 

Protein yield (g)

 

Fat % (10-3)

 

Protein % (10-3)

PL

Low

Medium

High

Low

Medium

High

Low

Medium

High

Low

Medium

High

Low

Medium

High

G

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

a0

a0

 

0.910

2.80

3.40

 

1232

1536

2412

 

1056

1975

2760

 

5.26

13.2

18.8

 

4.15

3.07

3.06

a0

a1

 

0.103

0.360

0.604

 

80.8

291

448

 

263

368

623

 

1.11

1.87

2.15

 

0.51

0.47

0.42

a0

a2

 

-0.0483

-0.139

-0.236

 

-27.5

28.5

50.2

 

-23.4

-104

-122

 

-0.05

-0.82

-1.13

 

-0.08

-0.12

-0.01

a0

a3

 

0.0949

0.123

0.224

 

13.6

8.87

-9.52

 

-16.3

34.3

122

 

-0.06

-0.07

0.47

 

0.03

-0.04

-0.07

a1

a1

 

0.122

0.187

0.292

 

109

174

235

 

510

180

322

 

0.60

1.01

1.30

 

0.73

0.37

0.55

a1

a2

 

-0.0258

-0.0322

-0.0509

 

1.65

2.46

23.3

 

-29.6

-28.7

-2.93

 

0.13

-0.35

-0.46

 

-0.11

-0.05

0.04

a1

a3

 

0.0158

0.0142

0.0459

 

-18.4

-3.38

-7.34

 

-26.6

4.25

23.3

 

-0.05

0.24

0.48

 

0.04

0.00

-0.04

a2

a2

 

0.0386

0.0326

0.0544

 

49.7

23.9

37.1

 

56.8

26.0

29.3

 

0.21

0.29

0.45

 

0.09

0.05

0.24

a2

a3

 

-0.0176

-0.0139

-0.0249

 

-15.9

-15.8

-7.02

 

-4.47

-2.32

-7.27

 

-0.09

-0.17

-0.33

 

-0.01

0.01

-0.02

a3

a3

 

0.0264

0.0225

0.0320

 

22.6

19.1

14.6

 

23.3

14.1

15.3

 

0.23

0.21

0.61

 

0.04

0.04

0.14

PE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

p0

p0

 

19.1

33.6

38.0

 

22100

28830

39990

 

20000

26240

32970

 

99.9

134

152

 

36.9

28.4

29.0

p0

p1

 

0.949

1.73

2.86

 

-371

98.5

1154

 

2010

2701

3522

 

8.29

14.1

13.5

 

3.04

3.12

2.74

p0

p2

 

-1.09

-2.08

-2.08

 

-455

-621

-1124

 

-1827

-1423

-1577

 

-0.60

-1.43

-3.08

 

-0.05

0.65

-0.67

p0

p3

 

-0.216

0.119

0.540

 

-3.69

-103

-154

 

436

-88.4

23.3

 

-7.99

-5.07

-3.95

 

-1.51

-1.21

-0.46

p1

p1

 

3.23

5.30

6.17

 

3326

5527

6516

 

252

4598

5829

 

29.1

35.7

34.9

 

9.11

8.71

8.03

p1

p2

 

-0.326

-0.660

-0.661

 

-1661

-1626

-1412

 

-184

-367

-542

 

-7.64

-12.0

-11.5

 

-2.41

-1.88

-1.89

p1

p3

 

-0.206

-0.383

-0.295

 

421

-177

-378

 

40.7

-297

-314

 

8.65

7.89

12.0

 

2.31

2.93

3.55

p2

p2

 

1.483

2.06

2.34

 

1058

2344

2469

 

191

1855

2187

 

19.2

21.4

20.2

 

4.66

4.12

3.96

p2

p3

 

-0.249

-0.404

-0.538

 

-273

-1074

-1288

 

-40.3

-351

-522

 

-5.98

-9.77

-11.8

 

-2.30

-2.24

-1.17

p3

p3

 

0.797

1.10

1.23

 

208

1232

1349

 

30.4

822

1055

 

16.3

18.6

19.8

 

4.05

4.64

4.47

R

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

r1

r1

 

7.69

10.1

12.7

 

37040

39600

53410

 

13100

14620

17610

 

215

401

340

 

41.9

61.4

72.7



Figure 1a. Estimated heritability (h2) as a function of days in milk (DIM) in three levels of production for milk yield Figure 1b. Estimated heritability (h2) as a function of days in milk (DIM) in three levels of production for fat yield Figure 1c. Estimated heritability (h2) as a function of days in milk (DIM) in three levels of production for protein yield
Figure 1d. Estimated heritability (h2) as a function of days in milk (DIM) in three levels of production for fat percentage Figure 1e. Estimated heritability (h2) as a function of days in milk (DIM) in three levels of production for protein percentage

 

Generally, heritability for 305-days production in low PL was lower than in high PL. But for protein percentage, heritability in the low production level was higher than for the other levels (Table 4). Greater heritability values for herds with greater milk yield averages have been observed frequently. Veerkamp and Goddard (1998) indicated that the genetic variance for milk yield in high-input systems is greater than the genetic variances in low-input systems. Van Vleck (1988) indicated that genetic and phenotypic variances were different from farm to farm, in most cases. The reason of this difference is the result of a more complete expression of the genetic potential in the high production level as result of a better environment (Hill et al 1983; Powell et al 1983 and Ceron-Munoz et al 2004).


Table 4. Spearman correlation coefficients for the 20 top sires (below diagonal), estimated 305-days heritabilities (on diagonals and bold) and 305-days genetic correlations (above diagonal) by production levels (PL) for milk production traits

Trait

PL

low

Medium

High

Milk yield

Low

0.18

0.83

0.79

Medium

0.38

0.31

0.99

High

0.39

0.96

0.33

Fat yield

Low

0.21

0.89

0.85

Medium

0.80

0.20

0.99

High

0.92

0.77

0.23

Protein yield

Low

0.20

0.92

0.88

Medium

0.81

0.28

0.98

High

0.60

0.76

0.31

Fat %

Low

0.20

0.93

0.93

Medium

0.80

0.35

0.97

High

0.92

0.77

0.43

Protein %

Low

0.40

0.93

0.79

Medium

0.93

0.39

0.90

High

0.60

0.70

0.38


Different genetic variances and heritabilities in different production levels revealed unequal genetic expression of dairy cattle genes. Differences in additive genetic variances obtained for different production levels imply that a scaling effect exists for EBV of sires across these PL.

 

The genetic correlation coefficients for milk production traits between PL varied and indicated the presence of a GE interaction. Generally, the lowest genetic correlations were estimated across low and high production levels that were 0.79 for milk yield and protein percentage, suggesting that GE would have an important impact on animal performance (Robertson 1959). For fat percentage, genetic correlations between production levels were greater than 0.90, suggesting that sires will rank similarly in the three production levels (Table 4). However, differences in variance estimates across these production levels may lead to scaling effects in sires’ EBV, especially between low and high levels. The low genetic correlations are thought to be due to differences in feeding systems or feeding levels and climate variables between herds on different PL.

 

Spearman rank correlations across three production levels for 20 top sires, are given in Table 4. Spearman rank correlations for milk yield across PL were lower than other traits. These correlations between low and medium and also between low and high production levels were less than 0.40, whereas between medium and high production levels was 0.96. Correlation between EBV of sires in low and high PL for protein percentage was 0.60. This results reflected low genetic correlation between these PL (rg<0.80). Low genetic and spearman correlations are translated as re-ranking of sires across production levels. Cienfuegos-Rivas et al (1999) found low rank correlation coefficient (0.59) between herds in low milk production level in Mexico and all herds in the United States. They concluded that this result was evidence for a significant G E interaction and that sires were ranked differently in the Mexican environment compared with their ranking in the United States. Peterson (1988) reported that re-ranking was observed for Canadian sires when they were used in New Zealand. The authors suspected this is caused by the decreased ability of Canadian sires daughters to get sufficient energy intakes from exclusive pasture regimens in New Zealand. When genetic correlations of traits decreased, the need for a separate breeding program increases (Mulder and Bijma 2006; Nauta et al 2006).  


Conclusion


Acknowledgments

The authors thank animal breeding center of Karaj, Iran for providing the data. We also want to express our deepest thanks to Hedi Hammami for his helpful contribution.


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Received 5 May 2012; Accepted 3 June 2012; Published 1 July 2012

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