Genetic parameters for somatic cell score and production traits in the first three lactations of Chinese Holstein cows

Journal of Integrative Agriculture 2015, 14(1): 125–130 Available online at www.sciencedirect.com

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RESEARCH ARTICLE

Genetic parameters for somatic cell score and production traits in the first three lactations of Chinese Holstein cows ZHAO Fu-ping1*, GUO Gang1, 2, 3*, WANG Ya-chun3, GUO Xiang-yu3, ZHANG Yuan3, DU Li-xin1 1

National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, P.R.China 2 Beijing Sanyuan Dairy Cattle Breeding Center, Beijing 100076, P.R.China 3 College of Animal Science and Technology, China Agricultural University, Beijing 100193, P.R.China

Abstract The objectives of this study were to estimate genetic parameters of lactation average somatic cell scores (LSCS) and examine genetic associations between LSCS and production traits in the first three lactations of Chinese Holstein cows using single-parity multi-trait animal model and multi-trait repeatability animal model. There were totally 273 605 lactation records of Chinese Holstein cows with first calving from 2001 to 2012. Heritability estimates for LSCS ranged from 0.144 to 0.187. Genetic correlations between LSCS and 305 days milk, protein percentage and fat percentage were –0.079, –0.082 and –0.135, respectively. Phenotypic correlation between LSCS and 305 days milk yield was negative (–0.103 to –0.190). Genetic correlation between 305 days milk and fat percentage or protein percentage was highly negative. Genetic correlation between milk fat percentage and milk protein percentage was highly favorable. Heritabilities of production traits decreased with increase of parity, whereas heritability of LSCS increased with increase of parity. Keywords: somatic cell score, genetic parameters, Chinese Holstein, single-parity multi-trait animal model, multi-trait repeatability animal model

1. Introduction Mastitis is the most costly disease in the dairy industry (Shook and Schutz 1994) because of its prevalent incidence (Seegers et al. 1997) and its biological effects. Both incidence and cost are likely to increase as a result of selection

Received 16 December, 2013 Accepted 27 March, 2014 ZHAO Fu-ping, E-mail: [email protected]; GUO Gang, E-mail: [email protected]; Correspondence ZHANG Yuan, E-mail: [email protected]; DU Li-xin, E-mail: [email protected] * These authors contributed equally to this study. © 2015, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(14)60758-9

for increased milk yield (Mrode and Swanson 1996). It is important to offset possible deterioration in udder health using selection against mastitis in a national breeding scheme. However, mastitis is not routinely recorded in China. Fortunately, mastitis resistance can be improved by indirect selection using somatic cell score (SCS), which is highly favorable correlation with mastitis (Carlén et al. 2004). SCS is generally derived using log transformation of somatic cell count (SCC) with a base of two. SCC is routinely recorded in most milk recording systems, and information on SCC is easily available. Moreover, SCS is not only an indicator for mastitis, but also a measure for amount of the cells necessary to combat the infection (Detilleux et al. 1997). Currently, price penalties are applied to milk with high SCC in milk payment systems. Therefore, high SCC and mastitis incidence tremendously decrease

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net income for the dairy farm and the value of milk for manufacturing, which is particularly important in China. Most countries in Europe and North America have carried out genetic evaluations for SCS. The genetic evaluation systems in most countries are based on lactation average somatic cell scores (LSCS) and test-day SCS. Miglior et al. (2009) analyzed data of Chinese Holstein by test-day model, but records of only 9 706 cows were used due to the requirement of model. In current study, the number of cows with above 6 test-day records in the first three lactations is 273 605 cows, including 266 000 cows in first lactation, 172 706 cows in second lactation and 110 311 cows in third lactation. Some cows only had test-day records in first lactation, whereas other cows had only second- or third-lactation test-day records, lacking first-lactation test-day records. Therefore, for utilizing all the information, lactation models are used to do genetic analysis in this study. Genetic parameters, which are functions of (co)variance components, provide information about the genetic nature of a trait and are needed for a national genetic evaluations and selection strategies. The purpose of this study was to estimate heritabilities, repeatabilities and genetic correlations of LSCS and production traits and provide the foundation of genetic evaluation in Chinese Holstein.

2. Results In this study, parameter estimates from three single parity multi-trait animal models (from the first parity to the third parity) and multi-trait repeatability animal model (three parities) gave generally similar results.

2.1. Basic statistics The overall means for the traits and the definition of traits were shown in Table 1. LSCS was lower for first-lactation cows than for cows in second lactation, whereas milk yield was lower for first-lactation cows than for cows in later lactations. The mean of LSCS (3.56) is obviously higher than LSCS for American Holstein cows over birth years 1990 to 2001 (2.98) (AIPL 2004). Thus, the predicted incidence rate of mastitis in Chinese Holstein cows could be higher than that of Holstein cows in developed country, such as America and Europe.

2.2. Additive genetic variances Additive genetic variances of traits were provided in Table 2. The variation of additive genetic variance of LSCS increased from 0.135 to 0.210 from first to third lactation. Additive genetic variances of fat and protein content decreased with increase of parity, whereas those for milk increased.

2.3. Heritabilities and correlations Heritabilities and correlations between traits were shown in Table 3. Heritabilities of LSCS were low but increased (0.05) slightly with increasing parity. Estimated heritability of LSCS across the first three lactations (0.09) was obviously lower than estimates in single lactation. Moreover, heritabilities of LSCS were considerably lower than those of production traits (milk yield, fat percentage and protein percentage). Heritabilities of milk yield, fat percentage, and protein percentage were of moderate level, ranging from 0.11 to 0.48,

Table 1 Description statistics of lactation records in the complete edited dataset

Milk yield (kg 305 d–1) Fat percentage (%) Protein percentage (%) LSCS1)

Parity 1 Mean SD2) 8 048 1 951 3.69 0.51 3.16 0.22 3.35 1.23

Parity 2 Mean SD 8 515 2 731 3.70 0.53 3.18 0.23 3.66 1.31

Parity 3 Mean SD 8 295 2 413 3.69 0.51 3.17 0.23 3.93 1.27

Total Mean SD 8 245 2 324 3.69 0.51 3.17 0.22 3.56 1.29

1)

LSCS, lactation average somatic cell scores. SD, standard deviation. The same as below.

2)

Table 2 Additive variance of LSCS and production traits in Chinese dairy cattle population

Milk (kg) Fat percentage (%) Protein percentage (%) LSCS

Parity 1 Parity 2 σ2a σ2e σ2a σ2e 1 307 740 1 390 219 2 614 125 4 883 685 0.058 0.083 0.049 0.116

Parity 3 σ2a σ2e 483 251 3 855 944 0.041 0.119

Total σ2a σ2e 535 002 2 033 136 0.035 0.117

0.023

0.015

0.020

0.024

0.017

0.024

0.013

0.024

0.135

0.801

0.171

0.934

0.210

0.912

0.098

0.863

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Table 3 Heritabilities (on the diagonal using bold), genetic correlations (above the diagonal) and phenotypic correlations (below the diagonal) between LSCS and milk yield, fat percentage or protein percentage Lactation trait Parity 1 LSCS Milk yield Fat percentage Protein percentage Parity 2 LSCS Milk yield Fat percentage Protein percentage Parity 3 LSCS Milk yield Fat percentage Protein percentage Total LSCS Milk yield Fat percentage Protein percentage

Trait LSCS

Milk yield

0.140.01 –0.10 0.03

–0.14 0.480.02 –0.17

0.11

0.160.02 –0.20 0.03 0.16

0.190.02 –0.19 0.01 0.13

0.090.01 –0.18 0.03 0.14

Fat Protein percentage percentage

–0.22

–0.28 0.350.02 –0.20 –0.36

–0.16 0.110.03 –0.14 –0.28

–0.08 0.180.02 –0.18 –0.26

–0.04 –0.24 0.410.03 0.44

–0.08 –0.18 0.300.03 0.42

–0.27 –0.12 0.260.04 0.39

–0.14 –0.23 0.220.02 0.41

–0.01 –0.21 0.69 0.610.03 0.06 –0.25 0.68 0.460.04 –0.19 –0.18 0.68 0.410.05 –0.08 –0.18 0.71 0.330.02

The subscripts of data are standard error.

and estimates decreased clearly with increased parity. Estimated genetic correlations between LSCS and milk yield were about –0.28 to –0.08, with the highest estimate found across the first three lactations. Phenotypic correlations between milk yield and LSCS in third lactation were low with a mean of –0.14. Genetic correlations between LSCS and fat percentage were from –0.27 to –0.04, with the highest estimate found in first lactation. Phenotypic correlations between fat percentage and LSCS in the first three lactations were close to zero with a mean of 0.023. Genetic correlations between LSCS and protein percentage were about –0.19 to 0.06, with the highest estimate found in second lactation. The strength of most of correlations declined with increasing parity, especially between milk yield and fat percentage. Phenotypic correlations were all moderate, about –0.26 to 0.44, and the highest estimates were found between fat percentage and protein percentage in first lactation. Estimated genetic correlations between LSCS and milk yield, fat percentage or protein percentage were –0.08,

–0.14 and –0.08, respectively, from the multiparity model. Phenotypic correlations between LSCS and milk yield, fat percentage or protein percentage were –0.18, 0.03 and 0.14, respectively, which were rather close to the mean of the three single lactation analyses. Genetic correlations and phenotypic correlations between production traits from the multiparity model were highly similar with those of three single lactation analyses. In addition, the genetic and phenotypic correlations among the first three lactations for LSCS were presented in Table 4. The genetic correlations for LSCS between different lactations were high, ranging from 0.76 to 0.97, while the phenotypic correlations were ranged from 0.20 to 0.29.

Table 4 Genetic correlations (above the diagonal) and phenotypic correlations (below the diagonal) among the first three lactations for LSCS Trait

LSCS1

LSCS1 LSCS2 LSCS3 1)

0.29 0.20

Trait1) LSCS2 0.87

LSCS3 0.76 0.97

0.36

LSCS1, LSCS for first lactation; LSCS2, LSCS for second lactation; LSCS3, LSCS for third lactation.

2.4. Repeatabilities Repeatabilities of traits across first three lactations are given in Table 5. Repeatability of LSCS (0.195) was obviously lower than those of production traits. Repeatabilities of production traits were moderate, ranging from 0.274 to 0.379, and the highest estimate was found in protein percentage. Table 5 Estimates of repeatabilities of the traits using multipletraits repeatability animal model Trait LSCS Milk yield Fat percentage Protein percentage

Repeatability 0.195 0.318 0.274 0.379

Stand error 0.010 0.009 0.009 0.007

3. Discussion 3.1. Heritabilities Estimated heritabilities of LSCS (0.09 to 0.19) are in the range of reported estimates from other studies using linear models. In a review by Mrode and Swanson (1996), estimates of heritabilities of LSCS were between 0.05 and 0.47, with weighted average heritabilties of SCC of 0.11 (SD 0.04) and 0.11 (SD 0.07) in first and later lactations, respectively.

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In a later review, Heringstad et al. (2000) reported estimates ranging from 0.08 to 0.19, and more recent estimates are of similar level varying between 0.09 and 0.18 (Haile-Mariam et al. 2001; Castillo-Juarez et al. 2002; Søndergaard et al. 2002; Mrode and Swanson 2003; Ødegård et al. 2003). Carlén et al. (2004) also reported the similar result that heritabilities of LSCS ranged from 0.10 to 0.14. In most studies including later lactations, the variation of heritability of LSCS in various lactations was slight. However, Da et al. (1992) reported heritability of SCS increases as parity increases in the first three lactations (0.05 to 0.11), whereas Banos and Shook (1990) found that heritability decreases as parity increases in the first three lactations (0.14 to 0.11). Miglior et al. (2009) estimated the genetic parameter using 109 005 test-day records of 9 706 cows in Chinese Holstein by testday model random regression and found that average daily heritabilities ranged between 0.092 and 0.187 for SCS, which is the same with the result of this study. One would expect the heritability for a single test day to be lower than that from a lactation average, so that the range is the same is difficult to interpret; this could be due to chance. In our study, the heritability of LSCS increased as parity increased. It was because the genetic variance increased more than the residual variance did with increase of parity. On the other hand, the heritability of production traits decreased as parity increased. This was consistent with the fact that residual variance increases with lactation number, and in some cases, genetic variance decreases. The heritabilities of all traits in the first three lactations using multi-trait repeatability animal model (MTRA) were slightly lower than the means of heritabilities for three lactation using single-parity multi-trait animal model (SMTA), which could be the result of the increased amount of data in MTRA. It was confirmed that amount of effective data should be increased during genetic evaluation, which could increase reliability of genetic analysis.

3.2. Correlations between LSCS and production traits Estimated genetic correlations between LSCS and production traits for the first three lactations ranged from –0.265 to 0.056, with the strongest correlations between LSCS and milk yield for second lactation and between LSCS and fat percentage for third lactation. There is an upper range for previously reported estimates. In a review, Mrode and Swanson (1996) reported a weighted average genetic correlation between somatic cell score and milk yield in first lactation of 0.14 (SD 0.04). Rupp and Boichard (1999) reported that the moderately unfavorable genetic correlation between production traits and LSCS (–0.11 to –0.27) was found. The range of more recent estimates was from about 0 to 0.3 (Castillo-Juarez et al. 2002; Carlén et al. 2004; Weller

et al. 2006). In contrast to previous studies, stronger genetic correlation between LSCS and milk yield was found in first lactation (Carlén et al. 2004; Weller et al. 2006). Genetic correlations between LSCS and production traits in our study were mostly favorable. Other studies reported that the genetic correlation between SCC and milk production, changed from positive, thus unfavorable, in first lactation, to negative in later lactations (Banos and Shook 1990; Haile-Mariam et al. 2001). There are two possible explanations for the change of genetic correlation between parities. First, in different lactations, partly different genes may affect SCC because different pathogens may be mainly responsible for the mastitis cases. Second, the population of Chinese Holstein cows is different from Holstein population in other developed countries. In dairy industry in developed countries, culling practices, especially during first lactation, remove many low-producing cows with high occurrence of mastitis and high levels of SCC, whereas culling is uncommon in China due to special raising system and dairy industry conditions. However, this system and conditions in China dairy industry are changing.

3.3. Model comparison Single-parity multi-trait animal model was implemented in this study as well as multiparity multi-trait animal model. The reliability of estimated heritabilities using multiparity analysis was slightly higher than that using single-parity analysis. This is because increasing the number of repeated records of traits may improve the accuracy of genetic parameter estimate. In addition, a low repeatability of LSCS (0.195) was found. Therefore, it is important to improve the accuracy of genetic parameter estimate for LSCS by increasing the number of repeated records for LSCS. Currently, random test-day model is widely used in most studies due to advantages of test-day model over animal model. However, it requires that multiple lactation records are available, whereas in the current database, most cows had only records in first lactation. Thus, animal model is more suited for genetic parameter estimate in current data material than random test-day.

4. Conclusion The genetic correlation of SCS and milk yield were negative and low. Hence, selection for milk yield will not only increase SCS but it will also not improve it much. In order to improve SCS and thereby hopefully resistance to mastitis, SCS must be included in the breeding goal and selection criterion in Chinese Holstein population. Heritability estimates of LSCS were low (0.09 to 0.19). Heritability estimates of production traits (milk yield 0.11 to 0.48, fat percentage 0.22 to 0.41, and

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protein percentage 0.33 to 0.61) were higher than those of LSCS. Level of heritability of LSCS increased with increasing parity. Therefore, it is important that genetic selection programs seek to improve mastitis resistance in all lactations. Multiparity analysis was more accurate in estimating genetic parameters than single-parity analysis. However, waiting for information from later lactations before selecting young bulls using multiparity analysis, generation interval would be lengthened, which is not desirable. Therefore, only using first-lactation records would improve resistance in later parities as well, because the genetic correlation between LSCS in first and later lactations is high. The results of this study will give an efficient reference for mastitis resistance breeding in Chinese Holstein population.

5. Materials and methods 5.1. Data The data used for this study were extracted from the national milk-recording database of Dairy Association of China (DAC). The data comprised the first three lactation testday SCC records, lactation milk yield, protein percentage and fat percentage records of Chinese Holstein cows, with calving dates between January 2001 and November 2012. Records were edited on the following criteria: Sire must be known, first test must take place within the first 45 d in milk (DIM); tests before 5 d and after 305 d were excluded; cows with test interval within 50 d were considered; only cows with more than 6 test-day records per lactation were included; reasonable age of calving in a specific lactation (23 to 32 mon for lactation 1, 35 to 49 mon for lactation 2, and 47 to 62 mon for lactation 3); and SCC between 5 000 and 6 400 000 cells mL–1 milk. In addition, cows were from sires with at least 20 daughters, who were distributed in more than 5 herds. The data set then consisted of 266 000 cows from 10 640 sires in first lactation, 172 706 cows from 8635 sires in second lactation, and 110 311 cows from 6 128 sires in third lactation. An additive relationship file was created by tracing pedigrees at least three generations back. For each record, SCC was transformed to SCS to achieve normality and homogeneity of variances: SCS=log2(SCC/100 000)+3. Lactation SCS (LSCS) was the arithmetic mean of monthly test-day SCS from 5 to 305 d after calving.

5.2. Statistical models The analysis was executed using the DMU package (Madsen and Jensen 2007). The following linear model was used for the analysis of data from a single parity: (1) yijk=µ+hysi+agej+ak+eijkl Where, yijk is observation of a trait (SCS, milk yield, fat

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percentage or protein percentage) for cow k from herd-yearseason i; µ is the population mean; hysi is the fixed effect of ith herd-year-season in order to include the significant interactive effects among herd, year and season of calving; agej is the fixed effect of the jth month age of calving; ak is the random additive genetic effect of cow k, Var(a)=Aσa2, where A is the additive relationship matrix; eijkl is random residual, Var(e)=Iσe2. In the multiparity animal model, parity treated as fixed effect, and permanent environmental effect treated as a random effect were added to the single parity model. The age of calving (mon) was defined for the first three parities: The first parity is 23–33 mon age; the second parity is 35–49 mon age; the third parity is 48–62 mon age. The season of calving was defined for three seasons (Jan to Apr, May to July, Aug to Dec) according to the results of GLM analysis by SAS 9.1 (2004). Single-trait variance component estimates were used as initial values for multi-trait analysis to facilitate convergence of multi-trait analysis. Heritability (h2) and repeatability (t) were calculated based on the following equations: σ2 h2= 2 2a 2 σ a+σ pe+σ e

(2)

and t=

σ2a+σ2pe σ +σ2pe+σ2e 2 a

(3)

Where, σ2a, σ2Pe and σ2e are additive, permanent environmental and error variances, respectively.

Acknowledgements We also acknowledge the fundings from the National Natural Science Foundation of China (31200927), the National Modern Agricultural Industry Technology Fund for Scientists in Sheep Industry System, China (CARS-39-04B), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2011BAD28B02, 2012BAD12B06) and the Chinese Academy of Agricultural Sciences Foundation (2012cj-2).

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