Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis

J. Dairy Sci. 102 https://doi.org/10.3168/jds.2018-15561 © American Dairy Science Association®, 2019.

Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis B. S. Olasege,1 S. Zhang,1 Q. Zhao,1 D. Liu,1 H. Sun,1 Q. Wang,1,2 P. Ma,1* and Y. Pan1,2* 1 2

Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, PR China Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai 200240, PR China

ABSTRACT

Information about genetic parameters is population specific and it is crucial for designing animal breeding programs and predicting response to selection. This study was carried out to estimate the genetic parameters for 23 body conformation traits of 45,517 Chinese Holstein reared in Eastern China from 1995 to 2017 with the Bayesian inference method using a linear animal mixed model. The methods to integrate these traits included (1) using the composite index from the Dairy Association of China and (2) applying principal component analysis and factor analysis to explore the relationship between the conformation traits. Estimates of heritability using the composite index were low (0.04; feet and legs) to moderate (0.23; body capacity). Strong genetic correlations were observed between the individual body conformation traits. Both principal components (1 to 7; eigenvalues ≥ 1) and latent factors (1 to 7; eigenvalues ≥ 1) explained 60.37% of total variability. Principal component 1 and factor 1 accounted for the traits that are usually associated with milk production. Moderate to low heritability were estimated through multi-trait analysis for principal components (from 0.07 to 0.21) and latent factors (from 0.07 to 0.23). Genetic correlations among the 2 multivariate techniques are typically lower compared with the one existing among the measured traits. Results from these analyses suggest the possibility of using both principal component analysis and factor analysis in morphological evaluation, simplifying the information given by the body conformation traits into new variables that could be useful for the genetic improvement of the Chinese Holstein population. This information could also be used to avoid analyzing large number of correlated

Received August 16, 2018. Accepted March 3, 2019. *Corresponding authors: panyuchun1963@​ aliyun​ .com and peipei. ma@​sjtu​.edu​.cn

traits, thereby improving precision and reducing computation burdens to analyze large and complex data. Key words: genetic parameter, body conformation trait, principal component analysis, factor analysis INTRODUCTION

The Chinese Holstein is a famous population of dairy cattle developed by crossing European HolsteinFriesian with Chinese Yellow cattle and has been raised in China for more than 60 yr (Hu et al., 2009). This population is currently the main Chinese dairy cattle and their breeding goal has been long-term selection for milk production (Alim et al., 2012; Dai et al., 2016). Chinese Holsteins are, however, sensitive to the hot and humid climate in Southern China, which has a negative influence on their performance (Xiong et al., 2013). Furthermore, they are usually faced with longevity problems and are mostly culled before their third parity due to challenges such as lameness, disease, and poor fertility (Wu et al., 2012; Liu et al., 2013), which is a major concern for cattle breeders in China. However, selection for conformation traits has been emphasized as a plausible solution to enhance future profitability of the Chinese dairy industry. Interestingly, these traits are easy to measure in the early life of an animal and can be used as indirect predictor of economically important traits (Chapinal et al., 2013; Abo-Ismail et al., 2017). The population is required to be genetically improved under prevailing management conditions and this can be achieved by formulating appropriate breeding schemes and selection methods based on the knowledge of genetic parameters. Moreover, these parameters are population specific, and decisions on selection can only be accurate if they are based on the use of information retrieved from the population under study (Petrini et al., 2016). Currently, the Dairy Association of China (DAC) uses linear combinations of several related traits to form a composite index based on linear score, weights, and defective traits (Wu et al., 2013). Many of the

OLASEGE ET AL.

combinations used are strongly correlated, suggesting redundancy (Berry et al., 2004; Mazza et al., 2014), and avoiding the analysis of a large number of highly correlated traits has been a longstanding interest of animal breeders. Principal component analysis (PCA) and factor analysis (FA) are 2 multivariate techniques that can be used to reduce the dimensionality of data and explore the relationship between traits with minimal loss of information (Schneider and Fikse, 2007). The 2 statistical approaches aimed at synthesizing multivariate complex phenotypes in a linear combination of the original variables where the variable weight is obtained from the correlation matrix of the raw data (Kirkpatrick and Meyer, 2004; Macciotta et al., 2006). The PCA assumes that the principal components (PC) account for all the variation in the data, whereas FA assumes a 2-component model that includes the communality and specific residual variance (Morrison, 1976; Sadek et al., 2006; Budaev, 2010). In view of the importance of these statistical approaches and the need to avoid redundancies among traits of interest, this study was designed to estimate genetic parameters for body conformation traits in Chinese Holstein dairy population using the composite index, PCA, and FA and also make some relevant comparisons. The result obtained could be used for national genetic evaluation for the selection of body conformation traits to be used in selection programs across the region of study. MATERIALS AND METHODS Phenotypic Data

Data from 62,623 Chinese Holstein cows born between 1995 and 2017 were used in this study. All of the cows were from 144 dairy cattle farms in the Shanghai Bright Holstein Co. Ltd. (Shanghai, China) where regular performance tests, including measurement of conformation traits, have been carried out as part of the DHI system. According to the linear classification system defined by Dairy Data Center of China (Dairy Association of China; DAC), 23 linear type traits were scored from 1 to 9, and 8 composite traits were measured using an index with values ranging from 40 to 100. Calculation of the scores for the 8 composite traits was based on functional score, weights, and defective traits (Wu et al., 2013). The details and descriptive statistics of the traits are presented in Table 1. Data from herd-yearseason with less than 5 records were discarded. At the end of data editing, 45,517 records were retained for subsequent analysis. After pruning the pedigree data, Journal of Dairy Science Vol. 102 No. 6, 2019

the record used for subsequent analyses contained 64,416 animals, with each animal traced back to its ancestor as many generations as possible. Statistical Analyses

Variance components for both the composite and individual conformation traits (IBCT) were computed with a single- and multi-trait model using GIBBS2F90 software (Misztal et al., 2002), a Fortran 90 program that uses a Bayesian approach via the Gibbs sampling algorithm. The animal model can be represented in matrix notation as follows:

y = Xb + Za + e, [1]

where y is a vector of observations for the analyzed body conformation traits, b is a vector of fixed effects [classes are presented in Table 2: herd-year-season (623 levels), age at scoring (4 classes), and DIM (7 classes)], a is the vector of random additive animal effects, e is the vector of random residual effects, and X and Z are incidence matrices relating records to their respective effects. Posterior means and standard deviations for (co) variance components, heritabilities, and genetic and phenotypic correlations were obtained using the POSTGIBBSF90 (Misztal et al., 2002). The analysis consisted of a single chain of 200,000 cycles discarding the first 20,000 cycles, taking a sample at every 100 iterations. Thus, 1,800 samples were used to obtain the parameters. The data convergence was verified with the graphical evaluation of sampled values versus interactions according to the criteria proposed by several authors (Heidelberger and Welch, 1983; Gweke, 1992), using the Bayesian Output Analysis (BOA) software in the R program (Smith, 2007; R Core Team, 2018). Standard error of heritability estimates and genetic correlations were obtained using the specific option (OPTION se_covar_function) of the POSTGIBBSF90 program. PCA

The PCA was performed using “PRINCOMP” function implemented in R program (R Core Team, 2018). This analysis condenses the information contained in several original variables (IBCT) into a smaller set of new composite dimensions with minimum loss of information and to explore the relationship between the traits. Principal components are orthogonal by definition (Macciotta et al., 2006) and before the application of the statistical software; the data were standardized

MULTIVARIATE TECHNIQUES IN GENETIC EVALUATION

Table 1. Descriptive statistics, variance components, and h2 estimates of body conformation trait on 45,517 Chinese Holstein cattle1 Single trait Trait

Mean

Composite trait   Final score   Body capacity  Rump   Feet and legs   Mammary system   Fore udder   Rear udder   Dairy character Individual body conformation trait   Body capacity   Stature   Body depth   Front end   Animal size   Chest width   Loin strength  Rump   Rump angle   Rump width   Feet and leg   Bone quality   Foot angle    Set of rear legs    Rear leg rear view   Heel depth   Mammary system   Udder depth   Udder texture   Median suspensory   Fore udder    Fore udder attachment    Fore teat placement    Fore teat length   Rear udder    Rear attachment height    Rear attachment width    Rear teat placement   Dairy character   Angularity

 

79.83 83.22 78.94 80.60 79.16 79.40 78.85 80.38     6.35 6.40 5.53 6.38 5.81 5.64   5.01 5.38   6.33 5.08 5.32 6.32 4.57   5.00 5.37 5.70   5.30 5.41 4.94   5.60 5.60 6.05   6.07

σa2

SD  

3.64 5.64 4.65 3.97 4.41 4.96 4.67 3.94     1.49 1.11 1.04 1.40 1.28 1.26   0.99 1.23   1.13 1.23 1.35 1.51 1.30   1.19 1.56 1.41   1.43 0.88 0.84   1.60 1.34 1.07   1.01

 

0.79 3.04 0.76 0.50 1.54 2.05 1.52 1.06     0.28 0.07 0.05 0.18 0.06 0.19   0.09 0.07   0.04 0.04 0.06 0.10 0.02   0.15 0.11 0.09   0.20 0.04 0.03   0.13 0.10 0.16   0.08

σp2

σe2

 

6.03 8.91 14.11 11.19 13.53 18.04 13.31 8.65     0.62 0.56 0.62 0.69 0.71 0.93   0.77 0.83   0.84 0.96 1.40 1.66 1.02   1.10 1.57 1.56   1.60 0.56 0.61   1.17 0.96 0.83   0.55

 

6.82 11.95 14.87 11.69 15.07 20.09 14.83 9.71     0.90 0.63 0.67 0.87 0.77 1.12   0.86 0.90   0.88 1.00 1.46 1.76 1.04   1.25 1.68 1.65   1.80 0.60 0.64   1.30 1.06 0.99   0.63

Multi-trait h2 (SE)

 

0.12 0.25 0.05 0.04 0.10 0.10 0.10 0.11

(0.01) (0.02) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)     0.31 (0.02) 0.11 (0.01) 0.08 (0.01) 0.21 (0.02) 0.08 (0.01) 0.17 (0.02)   0.11 (0.01) 0.08 (0.01)   0.05 (0.01) 0.04 (0.01) 0.04 (0.01) 0.06 (0.01) 0.02 (0.01)   0.12 (0.01) 0.07 (0.01) 0.06 (0.01)   0.11 (0.01) 0.07 (0.01) 0.05 (0.01)   0.10 (0.01) 0.10 (0.01) 0.16 (0.02)   0.13 (0.01)

σa2

   

1.03 3.18 0.99 0.61 1.65 2.22 1.61 1.37     0.30 0.09 0.07 0.21 0.06 0.18   0.11 0.08   0.04 0.06 0.09 0.14 0.05   0.19 0.15 0.15   0.27 0.06 0.05   0.17 0.14 0.20   0.10

σp2

σe2

 

5.85 8.81 13.94 11.10 13.44 17.92 13.25 8.43     0.61 0.55 0.61 0.67 0.71 0.94   0.76 0.82   0.84 0.94 1.34 1.63 1.01   1.07 1.54 1.51   1.56 0.54 0.60   1.14 0.93 0.81   0.54

 

6.88 11.99 14.93 11.71 15.09 20.14 14.86 9.80     0.91 0.64 0.68 0.88 0.77 1.12   0.87 0.90   0.88 1.00 1.47 1.77 1.06   1.26 1.69 1.66   1.83 0.60 0.65   1.31 1.07 1.01   0.64

h2 (SE)  

0.15 0.27 0.07 0.05 0.11 0.09 0.11 0.14

(0.01) (0.02) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)     0.33 (0.02) 0.14 (0.01) 0.10 (0.01) 0.24 (0.02) 0.08 (0.01) 0.15 (0.01)   0.13 (0.01) 0.09 (0.01)   0.05 (0.01) 0.06 (0.01) 0.06 (0.01) 0.08 (0.01) 0.05 (0.01)   0.15 (0.01) 0.09 (0.01) 0.09 (0.01)   0.15 (0.01) 0.10 (0.01) 0.05 (0.01)   0.13 (0.01) 0.13 (0.01) 0.20 (0.01)   0.20 (0.02)

 σa2 = additive variance, σe2 = residual variance, σp2 = phenotypic variance.

1

to assign equal weights to all the variables. Kaiser (1960) criterion was used to select the PC that explain most of the variation in the data. This criterion takes into account only the PC with eigenvalues ≥1. The first PC explains the largest percentage of total variance in the data set. The second PC explains the second largest percentage and so on, until all the variances in the data set are explained. The mth principal component is represented by the linear combination of the observed variables:

PCm = α1mX1 + α2mX2 + ... αnmXn,

where coefficients αnm are the elements of the eigenvector of the (co)variance (or correlation) matrix of

observed variables corresponding to the mth eigenvalue and Xn is the nth measure of the original variables. FA

Factor analysis was performed using the “FACTOR” procedure in SAS (SAS Institute Inc., 2009). This analysis synthesizes information contained in a set of n observed variables (y1,..., yn) by seeking a new set of p (p < n) variables (X1,..., Xp), termed common latent factors. The varimax rotation as described by Kaiser (1960) was chosen to maintain the orthogonality of the extracted factors. Only components with eigenvalues ≥1 were kept for the analyses (i.e., Kaiser criterion; Russel, 2002). The analysis was interpreted from the Journal of Dairy Science Vol. 102 No. 6, 2019

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Table 2. Classes for fixed effects used in the mixed model Effect

Class  

Age at scoring (d)  10–365  366–730  731–1,095  1,096–1,460 DIM  10–60  61–120  121–180  181–240  241–300  301–360  >360 Season (mo)   12, 1–2  3–5  6–8  9–11

1 2 3 4   1 2 3 4 5 6 7   1 2 3 4

biological point of view by observing the loadings of the IBCT. The sample scores were then calculated for each animal using the standardized scoring coefficients. According to Morrison (1976), the FA equation can be factored as a consequence of (co)variance modeling and each n original variable can be represented as linear combination of p common factors that generates the communality and residual covariance (uniqueness). The model can be written as

yn = bn1X1 + bn2X2 + ... bnpXp + en,

where yn is the nth original variables, bnp represents the factor coefficients or loadings of each nth variable on the respective factors, Xp is the pth common factors that underlie the nth measure being analyzed, and en reflects the residual specific variable in each nth measure. It is noteworthy that each measured trait has its own unique factor, reflecting systematic variance in the item that is not shared with the other measures being analyzed. Estimate of Genetic Parameters Using PCA and FA

Genetic parameters were estimated by fitting the PC and factor scores as y into the multivariate animal mixed model [1] presented above. The results from the multi-trait model of the composite traits were compared with the estimates from the new variables. RESULTS Heritability Estimates

The heritability estimate for both single- and multitrait of the body conformation traits are presented in Journal of Dairy Science Vol. 102 No. 6, 2019

Table 1. The estimates for the composite traits ranged from 0.04 [feet and leg (FL) composite] to 0.25 [body capacity (BC) composite] for single-trait and 0.05 [FL and rump (RP) composite] to 0.27 (BC composite) for the multi-trait model. The estimation for final score (FS) was 0.12 and 0.15 for single and multi-trait, respectively. For the IBCT, the heritability estimates for single trait model ranged from 0.02 (heel depth; HD) to 0.31 (ST) and the estimates for the multi-trait ranged from 0.05 (HD) to 0.33 (ST). The standard errors of heritability estimates were all ≤0.02. In general, the highest estimates of heritability were associated with BC traits, whereas those estimated for the FL were the lowest. Phenotypic and Genetic Correlations Between the Conformation Traits

Supplemental Tables S1 and S2 (https:​/​/​doi​.org/​10​ .3168/​jds​.2018​-15561) depict the phenotypic and genetic correlations among all the composite and IBCT, respectively. In general, a range of strong to weak genetic and phenotypic correlations was recorded between the composite traits (Supplemental Table S1; https:​/​ /​doi​.org/​10​.3168/​jds​.2018​-15561). For the IBCT, a range of positive and negative phenotypic correlations were observed among all the individual traits with the strongest positive phenotypic correlation estimated between ST and animal size (AS; 0.61), and the strongest negative correlation between set of rear legs (SRL) and RLV (−0.35) (Supplemental Table S2; https:​/​/​doi​.org/​ 10​.3168/​jds​.2018​-15561). The genetic correlations between the IBCT vary from weak to strong and ranged from −0.17 [between chest width (CW) and front end (FE)] to 0.88 (between ST and AS). Genetic correlations between the individual RP traits were weak (0.07), and moderate negative to strong positive genetic correlation existed among the individual FL traits ranging from −0.35 (between RLV and SRL) to 0.74 (between foot angle and HD). For the individual mammary traits and rear udder (RU) traits, a range of moderate to strong genetic correlation was observed between the individual mammary system (MAS) ranging from 0.35 [between udder depth (UD) and udder texture (UT)] to 0.52 [between UT and median suspensory (MS)] and that of the individual RU ranged from 0.22 [between rear attachment width (RAW) and rear teat placement (RTP)] to 0.65 [between rear attachment height (RAH) and RAW]. A notable exemption was individual fore udder (FU) trait recording a negative to moderate positive genetic correlation that ranged from −0.39 [between fore teat placement (FTP) and fore teat length (FTL)] to 0.35 [between fore udder attachment (FUA) and FTP]. The ANG in the individual

MULTIVARIATE TECHNIQUES IN GENETIC EVALUATION

dairy character (DC) trait had moderate to strong genetic correlation with all other individual traits with the exemption of SRL (0.05), rump angle (RA; −0.09), and FTL (−0.09). PCA

Table 3 presents the eigenvalues, proportional variance, and percentage cumulative variance explained by each component of the body conformation traits. There were 7 components with eigenvalues ≥1 accounting for 60.37% of the total variance. The first PC accounted for the largest proportion (24.92) of the total variation in the original 23 measurements of the conformation traits. The loading coefficients of the IBCT for the extracted PC are shown in Table 4. Due to low values of the loadings for each variable, only coefficients ≥|0.20| were reported for each PC obtained. This was done to minimize the loss of useful information in the PC. The PC1 had higher loadings on ST (0.34), FE (0.24), AS (0.33), CW (0.29), rump width (RW; 0.27), RAH (0.26), RAW (0.30), and ANG (0.33). This component seemed to represent BC, RU, and DC traits. The PC2 accounted for 8.47% variability and was loaded heavily for body depth (BD; 0.31), bone quality (BQ; 0.32), RLV (0.45), and UT (0.35), whereas PC3 accounted for 6.92% proportional variability and had higher loadings on SRL (−0.36), MS (0.28), FUA (0.35), and FTP (0.43). The PC4 explained 6.26% variability and had higher loadings on foot angle (0.63) and HD (0.56), whereas PC5 explained 4.77% variability and load heavily for UT (0.68). The PC6 accounted for 4.63% proportional variability and loaded heavily on RA (0.64), FTL (0.54), and RTP (−0.35). The PC7 accounted for 4.40% proportional variability with higher loading for loin strength (LS; 0.27). Due to the lesser amount of variance explained by the remaining subsequent PC with eigenvalue <1 to describe a group of trait clearly, they were not considered for further analysis.

FA

The eigenvalues and the proportion of the phenotypic variation explained by each factor are listed in Table 3. Seven latent factors had an eigenvalue ≥1. Similar to the PCA, the 7 factors after varimax rotation also explained 60.37% of the total variation among the 23 IBCT but with differences in terms of magnitude of each eigenvalue and the proportion of variance explained by each factor. The first factor (F1) accounted for the largest proportion (20.92%) of the total variability. The pattern coefficients using varimax rotation and communalities are shown in Table 5. Only loading coefficients ≥|0.40| (Hair et al., 2014) were reported for each for each IBCT. The F1 was heavily loaded for ST (0.88), BD (0.56), FE (0.47), AS (0.86), CW (0.75), LS (0.60), RW (0.69), RAH (0.56), RAW (0.57), and ANG (0.69), respectively. The F1 accounted for traits belonging to BC, RU, and DC, which corroborate with the result obtained from PCA. Factor 2 (F2) with 9.22% variability had higher loadings on UT (0.52), MS (0.74), FUA (0.62), and FTP (0.50), whereas the third factor (F3) accounted for 6.92% proportional variability and loaded heavily on BQ (0.52), RLV (0.69), and SRL (−0.56). The fourth factor (F4) explained 6.26% variability and only loaded on foot angle (0.89) and HD (0.80), whereas the fifth factor (F5) explained 4.77% variability and had higher loadings on UD (0.86). The F6 accounted for 4.63% proportional variability and loaded heavily on FTL (0.77) and RTP (−0.48), whereas F7 accounted for 4.40% proportional variability and had higher loadings for RA (0.93). Due to the small amount of variance explained by the remaining subsequent factors with eigenvalue <1 to describe a group of traits clearly, they were not considered for further analysis. The communalities (common factors) obtained for all the conformation traits ranges from 0.36 (FE) to 0.78 (ANG). Traits with low communality with factors denotes that they were less effective or completely independent to account for the total varia-

Table 3. Eigenvalues and proportion of total and cumulative variance explained by both principal components (PC) and factor analysis of the phenotypic values of body conformation traits in a Chinese Holstein population PCA1

Eigenvalue

Proportional variance (%)

Cumulative variance (%)

PC1 PC2 PC3 PC4 PC5 PC6 PC7

5.73 1.95 1.59 1.44 1.10 1.06 1.01

24.92 8.47 6.92 6.26 4.77 4.63 4.40

24.92 33.39 40.31 46.57 51.34 55.97 60.37

 

Factor2

Eigenvalue

Proportional variance (%)

Cumulative variance (%)

4.81 2.12 1.92 1.69 1.22 1.07 1.05

20.92 9.22 8.33 7.33 5.31 4.67 4.58

20.91 30.14 38.47 45.80 51.11 55.78 60.37

F1 F2 F3 F4 F5 F6 F7

1

PC1 to PC7 = 7 components with eigenvalues ≥1. PCA = principal component analysis. F1 to F7 = 7 factors with eigenvalues ≥1.

2

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Table 4. Principal component (PC) loading of individual conformation traits (loading coefficients ≥|0.20|) for Chinese Holstein cattle1 Trait

PC1

PC2

PC3

PC4

PC5

PC6

PC7

Stature Body depth Front end Animal size Chest width Loin strength Rump angle Rump width Bone quality Foot angle Set of rear legs Rear legs rear view Heel depth Udder depth Udder texture Median suspensory Fore udder attachment Fore teat placement Fore teat length Rear attachment height Rear attachment width Rear teat placement Angularity

0.34 0.24 0.20 0.33 0.29 0.26   0.27                       0.26 0.30   0.33

−0.22 0.31   −0.25         0.32   −0.22 0.45     0.35   0.26            

                  −0.29 −0.36     −0.31   0.28 0.35 0.43       −0.20  

                  0.62     0.56   0.21 0.20 0.21            

  −0.25             −0.20         0.68   −0.25 0.09   −0.25   −0.26    

            0.64                     −0.20 0.54     −0.35  

          0.27 0.60                   0.21 −0.28 0.52     −0.22  

1

Bolded values are the highly loaded coefficient for each trait on the selected PC.

tion compared with other body conformation traits with higher estimates for common factors. Variance Component Estimation for PCA and FA

The estimates of heritability and genetic correlations for the 7 PC and factor scores are presented in Tables 6 and 7, respectively. The estimates for the 2 statistical approaches were almost similar. The heritability estimates of the factor scores were slightly higher in magnitude than those of the PC. These estimates are quite similar to the estimate from the composite index. Also, the mean heritability for the factor scores (0.13) was slightly higher than the estimate for both PC (0.12) and FS (0.12). Generally, the estimates of the genetic correlations among the factors scores were higher than the correlation among the PC. The genetic correlations among the PC and factors are typically lower compared with the ones existing among the measured traits. The standard error of the genetic correlation of both PCA and latent factors were all ≤0.09. DISCUSSION Heritability

Multi-trait models are supposed to be more efficient and have higher heritability estimates compared with single-trait models due to their utilization of additional genetic information and complex covariance structure Journal of Dairy Science Vol. 102 No. 6, 2019

between traits (Analla et al., 1995; Zhang et al., 2018). This was the case for the estimates of the body conformation traits in our study. The heritability estimates for individual BC traits were lower than previously published results for the Holstein population (DeGroot et al., 2002; Pérez-Cabal and Alenda, 2002; Kadarmideen and Wegmann, 2003). Conversely, our result agrees with reports from earlier studies for Chinese Holstein in Northern China (Wu et al., 2013), Brazil Holstein (Kern et al., 2015), and Czech Holstein (Zink et al., 2011). The differences in magnitude across these studies may be premised on the scales used for scoring, number of animals, breeds, statistical model, data editing procedures, as well as consistency among evaluators (Campos et al., 2012). The heritability for RP, FL, MAS, FU, and RU traits were quite lower than previously reported studies (DeGroot et al., 2002; Kadarmideen and Wegmann, 2003; Zink et al., 2011). However, the lower heritability observed for foot and leg traits in our study are similar to those reported in the aforementioned studies. In contrast, the values recorded for most of the body conformation is in agreement with Wu et al. (2013), whereas those observed for heritability of udder traits falls within 0.06 to 0.20 reported by Boettcher et al. (1998). Heritability estimate is population specific (Petrini et al., 2016), and the low heritability of these traits implies that little response to selection could be achieved. This result revealed the importance of nonadditive genetic and environmental effects in the total

MULTIVARIATE TECHNIQUES IN GENETIC EVALUATION

Table 5. Latent factors, loading of individual conformation traits (loading coefficients ≥ |0.40|), and communality after varimax rotation of the 23 body conformation traits for Chinese Holstein cattle1 Varimax latent factor Trait

F1

F2

F3

F4

F5

F6

F7

Communality

Stature Body depth Front end Animal size Chest width Loin strength Rump angle Rump width Bone quality Foot angle Set of rear legs Rear legs rear view Heel depth Udder depth Udder texture Median suspensory Fore udder attachment Fore teat placement Fore teat length Rear attachment height Rear attachment width Rear teat placement Angularity

0.88 0.56 0.47 0.86 0.75 0.60   0.69                       0.56 0.57   0.69

                            0.52 0.74 0.62 0.50     0.38 0.33 0.36

  −0.37             0.52   −0.56 0.69               0.40 0.31    

                  0.89     0.80                    

  −0.33                       0.86             −0.32    

                                  −0.44 0.77     −0.48  

          0.32 0.93                                

0.79 0.62 0.36 0.78 0.59 0.52 0.89 0.49 0.44 0.81 0.41 0.53 0.75 0.77 0.53 0.59 0.53 0.53 0.69 0.55 0.67 0.39 0.65

1

Bolded values are the highly loaded coefficient for each trait on the selected factors.

Table 6. Estimate of genetic correlations (below diagonal), h2 (diagonal), and SE (above diagonal) among principal components (PC)1 PCA2

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC1 PC2 PC3 PC4 PC5 PC6 PC7

0.21 0.08 −0.32 0.25 0.12 −0.07 −0.21

0.03 0.11 −0.29 0.47 0.16 −0.22 0.07

0.06 0.05 0.10 −0.69 0.43 −0.46 0.13

0.03 0.05 0.08 0.07 0.10 −0.35 −0.18

0.07 0.08 0.09 0.09 0.12 0.11 0.12

0.07 0.06 0.09 0.08 0.08 0.10 −0.04

0.07 0.07 0.09 0.08 0.07 0.01 0.13

1

Bolded values are the heritability of each PC along the diagonal. PCA = principal component analysis.

2

Table 7. Estimate of genetic correlations (below diagonal), h2 (diagonal), and SE (above diagonal) among latent factors using varimax rotation1 FA2

F1

F2

F3

F4

F5

F6

F7

F1 F2 F3 F4 F5 F6 F7

0.23 0.67 0.64 0.57 0.19 0.42 0.05

0.03 0.14 0.69 0.58 0.32 0.30 0.08

0.06 0.05 0.08 0.62 0.36 0.20 0.10

0.03 0.05 0.08 0.07 0.36 0.21 0.08

0.07 0.08 0.09 0.09 0.15 0.04 0.06

0.07 0.06 0.09 0.08 0.08 0.14 0.07

0.07 0.07 0.09 0.08 0.07 0.08 0.11

1 2

Bolded values are the heritability of each factor along the diagonal. FA = factor (F) analysis. Journal of Dairy Science Vol. 102 No. 6, 2019

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variation of these traits. According to Pantelić et al. (2011) and Campos et al. (2012), care should be taken when comparing conformation traits with the result obtained from similar studies because different scoring system, breed, population size, classification system, and model are used in estimating these traits. Genetic Correlations

Generally, the phenotypic correlations for all the conformation traits follow similar trends as the genotypic correlations but are weaker in terms of magnitude than their respective genetic correlations between the same traits. Similar trends were also reported by Brotherstone (1994), Vukasinovic et al. (1997), and Mazza et al. (2014) for the same traits in Holstein, Brown Swiss, and Italian Rendena breed populations, respectively. The strong genetic correlations among all body traits are also in accordance with the studies by Ducrocq (1993), Gengler et al. (1999), and de Haas et al. (2007). These strong correlations suggest redundancy, which indicates the possibility of reducing the number of traits measured with minimal loss of information on each animal (Berry et al., 2004; Mazza et al., 2014). Therefore, selection for one of these selected traits will lead to a correlated response in other body traits not selected for, suggesting that selection aimed at improving body size of Chinese Holstein does not need to incorporate all of these traits in the selection index. The genetic correlations among the FL traits are in accordance with those reported for Swedish and Czech Holstein (Uggla et al., 2008; Němcová et al., 2011). Moderate to strong correlation estimated for MAS, fore, and udder traits, with FTL being the notable exception, were also reported by Boettcher et al. (1998) and DeGroot et al. (2002). This indicates that when selection is based on one of these traits, there would be negative knock-on effect on the placement of the teats. However, Sewalem et al. (2004) reached a conclusion that when the objective of selection is to improve conformation traits, much emphasis should be placed on the MAS following the FS due to their influence on functional longevity. PCA and FA

The total variance for PCA was higher than 49.74% (2 components) reported by Guiterrez and Goyache (2002) and lower than 65.77% (7 components) reported by Roughsedge et al. (2000) for 10 and 23 type traits, respectively. For FA, Mazza et al. (2016a) selected 6 factors with eigenvalues greater than 1 for both Rendena and Aosta Red Pied cattle accounting for 63% (20 type traits) and 58% (22 type traits) of the total Journal of Dairy Science Vol. 102 No. 6, 2019

variability in the 2 dual-purpose breed, whereas Mantovani et al. (2005) used 23 type traits and selected 7 factors with eigenvalues greater than 1 accounting for 63% total variability. The total variations in the aforementioned studies were higher than the total variability recorded in our study. However, our result was higher than the report of Vukasinovic et al. (1997) and Chu and Shi (2002) who chose 7 (23 type traits) and 2 (15 type traits) factors, respectively, accounting for 58 and 49.1% total variability. The difference in the magnitude of total variability and the number of principal components or factors chosen might be due to differences in breed, number of estimated type traits, and available records. In addition, the difference in the magnitude of proportional variance explained by the extracted PC and latent factors might be due to the fact that FA estimates shared variance (communalities) from the original variable, whereas PCA does not partition unique variance from shared variance, thereby setting the estimate of communalities to 1 (Costello and Osborne, 2005). The PC1 and F1 in the present study accounted for the traits that are usually associated with milk production. These results correspond with the report by Kern et al. (2014). Therefore, selection aimed at increasing milk production could incorporate PC1 or F1 as the selection index. Genetically deep, and taller cows, with wider udder that is soft to touch, and well-structured angularity (ANG) might improve milk production (Veerkamp and Brotherstone, 1997; de Haas et al., 2007; Kern et al., 2014). According to Manafiazar et al. (2016), stature (ST), ANG, CW, RAW, and RP width have a strong genetic correlation with residual feed intake which is an alternative measure for characterizing feed efficiency. In this study, heritability of PC and FA are moderate, indicating that additive genetic variability of these traits can provide moderate genetic gains through selection. The Chinese Holsteins are renowned for their high milking potentials; however, they are usually faced with longevity problem and are mostly culled before their third parity due to leg problem (Wu et al., 2012; Liu et al., 2013). The PC2 and PC4 or F3 and F4 accounted for the traits that are usually associated with lameness. Moreover, moderate genetic correlation existed between this 2 PC and latent factors. Thus, selection of cows with high score for FL, steeper foot angle, straighter legs, and fine bone structure might improve locomotion with low chances of claw disorder (Van Dorp et al., 2004; Pérez-Cabal and Charfeddine, 2016). One of the most frequent and important disease causing great economics losses to the dairy industry is clinical mastitis (Nash et al., 2003). The incidence rate is about 25 to 60% worldwide and Chinese cattle are

MULTIVARIATE TECHNIQUES IN GENETIC EVALUATION

prone to having a relatively higher rate (Ruegg, 2003; Chen et al., 2011). Indirect selection using SCC has been practiced widely because of the difficulties in direct selection for resistance to clinical mastitis (Shook, 2006). Somatic cell count has proven to be effective because the traits are inexpensive and easy to record, and due to the strong positive genetic correlation of SCC with clinical mastitis (Wang et al., 2007). Indirect selection of Chinese Holstein for lower SCC could reduce the incidence of mastitis and we strongly advocate for this across the dairy herds in China. Genetic correlations between conformation traits and SCS have been widely studied by several researchers (Rogers et al., 1998; Chrystal et al., 1999; Němcová et al., 2007). The strongest correlations were obtained for FUA, FTP, UD, and ANG. The PC3 and PC5 or F2 and F5 accounted for the traits that are usually associated with SCS, and according to DeGroot et al. (2002), selection index based on higher udders with tighter attachments and closer teats would be favorable for reducing SCS. Dube et al. (2008) also opined that a low, shallow udder with narrowly placed teats strongly correlates with low SCC in South African Holstein population. Composite indexes used for conformation traits combine linear trait information on several related traits into one numerical value used as a selection tool in breeding programs to identify animals with the ability to transmit a desirable linear combinations of traits (Holstein Association USA, 2018). In China, DAC uses linear combination of these strongly correlated traits to form a composite index. The aggregation of these traits in selection index may be hampered by collinearity between some traits due to a coherent (co)variance matrix (Macciotta et al., 2012). The PCA and FA are 2 statistical approaches that can be used to avoid analyzing large numbers of highly correlated traits and explore the relationship between traits with minimal loss of information (Schneider and Fikse, 2007; Mazza et al., 2016a). In this way, correlated traits could be loaded in the same PC or latent factor with each including traits with common biological or physiological characters, or both (Ali et al., 1998). In the present study, the heritability estimates of both PC and factors obtained were in agreement with the estimate of the heritability of the composite traits with PC1 and F1 (BC) showing the highest heritability and PC4 and F4 (FL) with the lowest estimate. These results are in accordance with the report of Mazza et al. (2016a,b) where similar findings were disclosed for both individual traits and the multivariate techniques used in this study and this affirmed the possible use of both PCA and FA in animal breeding (Macciotta et al., 2012; Mazza et al., 2016b). The low genetic correlations among the PC are in accordance with the estimate reported by Macciotta

et al. (2006). However, the values recorded among the latent factors were higher than the report of Mazza et al. (2016b) in the Aosta Red Pied breed and Macciotta et al. (2012) for the Brown Swiss cow. The genetic correlations among the 2 multivariate techniques are typically lower compared with the one existing among the measured traits. This suggests that the multivariate techniques are more efficient than the measured phenotypes in implementing aggregate selection index for breeding purpose. However, because the expected response to selection depends on the signs of the loadings, the knowledge of the loadings of the phenotypic traits that each PC and latent factors underlies allows more accurate interpretation of the expected trait variation (Mazza et al., 2016a). CONCLUSIONS

In this study, body conformation traits investigated recorded a low to moderate range of heritability, with those relating to body traits providing the highest estimates. Many of the traits analyzed were strongly correlated, indicating redundancy among traits. Both principal component analysis and factor analysis were able to derive 7 new components and latent factors, although about 40% of the information contained in the initial data was lost. Both approaches accounted for the main traits that are usually associated with selection for milk production, feed efficiency, locomotion, and mastitis. Principal components and latent factors had low to moderate heritability, which is in agreement with the estimates obtained from composite index, thus affirming the possibility of their inclusion as breeding goals for selection programs. Genetic correlations among the 2 multivariate techniques are typically lower compared with the one existing among the measured traits. Results from these analyses suggest the possibility of using both PCA and FA in morphological evaluation, simplifying the information given by the body conformation traits into new variables that could be useful for the genetic improvement of the Chinese Holstein population. This information could also be used to avoid analyzing large numbers of correlated traits, thereby improving precision and reducing computation burdens to analyze large and complex data. ACKNOWLEDGMENTS

Funding from the National Natural Science Foundation of China (Shanghai; no. 31672386, 31370043, 31701077) and the Shanghai Agricultural Committee Introduction Project (no. 2017:1-1, China) are gratefully acknowledged. The authors are also indebted to the contributions of S. O. Jimoh (Grassland Research Journal of Dairy Science Vol. 102 No. 6, 2019

OLASEGE ET AL.

Institute, Chinese Academy of Agricultural Sciences, Hohhot, China), and A. M. Isa (Department of Animal Science, Usamanu Danfodiyo University, Sokoto, Nigeria) for revision of the manuscript. We also thank the anonymous reviewers for their careful revision, as well as the provision of many insightful comments and suggestions on the manuscript. REFERENCES Abo-Ismail, M. K., L. F. Brito, S. P. Miller, M. Sargolzaei, D. A. Grossi, S. S. Moore, G. Plastow, P. Stothard, S. Nayeri, and F. S. Schenkel. 2017. Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet. Sel. Evol. 49:82. Ali, A. K., K. R. Koots, and E. B. Burnside. 1998. Factor analysis of genetic evaluations for type traits of Canadian Holstein sires and cows. Asian-Australas. J. Anim. Sci. 11:463–469. Alim, M. A., Y. P. Fan, X. P. Wu, Y. Xie, Y. Zhang, S. L. Zhang, D. X. Sun, Y. Zhang, Q. Zhang, L. Liu, and G. Guo. 2012. Genetic effects of stearoyl-coenzyme A desaturase (SCD) polymorphism on milk production traits in the Chinese dairy population. Mol. Biol. Rep. 39:8733–8740. Analla, M., A. Muiloz-Serrano, J. M. Cruz, and J. M. Serradilla. 1995. Estimation of genetic parameters of growth in Segureiia lambs. J. Anim. Breed. Genet. 112:183–190. Berry, D. P., F. Buckley, P. Dillon, R. D. Evans, and R. F. Veerkamp. 2004. Genetic relationships among linear type traits, milk yield, body weight, fertility and somatic cell count in primiparous dairy cows. Ir. J. Agric. Food Res. 43:161–176. Boettcher, P. J., J. C. M. Dekkers, and B. W. Kolstad. 1998. Development of an udder health index for sire selection based on somatic cell score, udder conformation, and milking speed. J. Dairy Sci. 81:1157–1168. Brotherstone, S. 1994. Genetic and phenotypic correlations between linear type traits and production traits in Holstein-Friesian dairy cattle. Anim. Prod. 59:183–187. Budaev, S. V. 2010. Using principal components and factor analysis in animal behavior research: caveats and guidelines. Ethology 116:472–480. Campos, R. V., J. A. Cobuci, C. N. Costa, and J. Braccini Neto. 2012. Genetic parameters for type traits in Holstein cows in Brazil. Rev. Bras. Zootec. 41:2150–2161. Chapinal, N., A. K. Barrientos, M. A. G. von Keyserlingk, E. Galo, and D. M. Weary. 2013. Herd-level risk factors for lameness in free stall farms in the northeastern United States and California. J. Dairy Sci. 96:318–328. Chen, R., Z. P. Yang, D. J. Ji, Y. J. Mao, Y. Chen, Y. Zhang, Hamza, X. Wang, and Y. Li. 2011. SNPs of CXCR1 gene and its associations with somatic cell score in Chinese Holstein cattle. Anim. Biotechnol. 22:133–142. Chrystal, M. A., A. J. Seykora, and L. B. Hansen. 1999. Heritabilities of teat end shape and teat diameter and their relationships with somatic cell score. J. Dairy Sci. 82:2017–2022. Chu, M. X., and S. K. Shi. 2002. Phenotypic factor analysis for linear type traits in Beijing Holstein cows. Asian-Australas. J. Anim. Sci. 15:1527–1530. Costello, A. B., and J. W. Osborne. 2005. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Pract. Assess. Res. Eval. 10:1–9. Dai, R., Y. Fang, W. Zhao, S. Liu, J. Ding, K. Xu, L. Yang, C. He, F. Ding, and H. Meng. 2016. Identification of alleles and genotypes of beta casein with DNA sequencing analysis in Chinese Holstein cow. J. Dairy Res. 83:312–316. de Haas, Y., L. L. Janss, and H. N. Kadarmideen. 2007. Genetic and phenotypic parameters for conformation and yield traits in three Swiss dairy cattle breeds. J. Anim. Breed. Genet. 124:12–19.

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