Genetic characteristics of Japanese Holstein cows based on multiple-lactation random regression test-day animal models

Genetic Parameter Estimation and Genome-Wide Association Study-Based Loci Identification of Milk-Related Traits in Chinese Holstein

Accurately estimating the genetic parameters and revealing more genetic variants underlying milk production and quality are conducive to the genetic improvement of dairy cows. In this study, we estimate the genetic parameters of five milk-related traits of cows-namely, milk yield (MY), milk fat percentage (MFP), milk fat yield (MFY), milk protein percentage (MPP), and milk protein yield (MPY)-based on a random regression test-day model. A total of 95,375 test-day records of 9,834 cows in the lower reaches of the Yangtze River were used for the estimation. In addition, genome-wide association studies (GWASs) for these traits were conducted, based on adjusted phenotypes. The heritability, as well as the standard errors, of MY, MFP, MFY, MPP, and MPY during lactation ranged from 0.22 ± 0.02 to 0.31 ± 0.04, 0.06 ± 0.02 to 0.15 ± 0.03, 0.09 ± 0.02 to 0.28 ± 0.04, 0.07 ± 0.01 to 0.16 ± 0.03, and 0.14 ± 0.02 to 0.27 ± 0.03, respectively, and the genetic correlations between different days in milk (DIM) within lactations decreased as the time interval increased. Two, six, four, six, and three single nucleotide polymorphisms (SNPs) were detected, which explained 5.44, 12.39, 8.89, 10.65, and 7.09% of the phenotypic variation in MY, MFP, MFY, MPP, and MPY, respectively. Ten Kyoto Encyclopedia of Genes and Genomes pathways and 25 Gene Ontology terms were enriched by analyzing the nearest genes and genes within 200 kb of the detected SNPs. Moreover, 17 genes in the enrichment results that may play roles in milk production and quality were selected as candidates, including CAMK2G, WNT3A, WNT9A, PLCB4, SMAD9, PLA2G4A, ARF1, OPLAH, MGST1, CLIP1, DGAT1, PRMT6, VPS28, HSF1, MAF1, TMEM98, and F7. We hope that this study will provide useful information for in-depth understanding of the genetic architecture of milk production and quality traits, as well as contribute to the genomic selection work of dairy cows in the lower reaches of the Yangtze River.

Effect of climate and season of calving on genetic parameters of production and somatic cells in Holstein cows

Records (111543) of first calving test day milk production with three milking times during the years 1983 to 2016 were used for 62 herds in the Breeding Center data file. The dairy cows under study had 10 test day records. Variables from the herd-year-calving combination as random effect and herd-test-month-age combination at the time of record as fixed effects were created for each animal. Average milk yield in hot and dry climate was higher than hot and wet climate. The average traits of milk fat and milk protein percentage in hot and dry climates were higher than hot and wet climates, but somatic cell traits were higher in hot and wet climates. The phenotypic variance of milk yield was higher in hot and dry regions in all ten test day records. The phenotypic variance of milk fat percent was higher in hot and dry area on the first, second, fifth and sixth test days. The average genetic variance of milk fat percentage in hot and wet region at 9 test day records was higher than in hot and dry region. Phenotypic variance of milk protein percentage in hot and dry area was higher than in hot and wet regions in all test days. In countries where calving occurs throughout the year, mastitis is dependent to the pattern of growth of pathogens in different seasons and years. Changes in animal physiological conditions (calving and changes in breast status) increase the number of somatic cells in milk, which has a physiological effect.

Development of genetic evaluation for milk production traits of Holsteins in Japan

The procedure used for the genetic evaluation of dairy cattle in Japan has developed from a lactation sire–MGS model to a multiple‐lactation random regression test‐day animal model. Genetic evaluation of Holstein bulls in Japan began in 1989 with the use of field‐style progeny testing; dairy herd improvement program data from all over Japan were used, along with a sire and maternal grandsire model. In 1993, an animal model was introduced to estimate breeding values for yield and type traits. A random regression test‐day model was first applied in 2010. In the business of breeding dairy cattle, it is very important to users that estimated breeding values are reliable and stable among subsequent routine evaluations. With experience in the genetic evaluation of dairy cattle in Japan, Japanese researchers have found ways to improve the stability of estimated breeding values. These modifications involve changes in data editing, development of evaluation models, changes to the structures of unknown‐parent groups, awareness of the problems of predicting lactation yield from partial test‐day records, and adjustment for heterogeneity within herd variances. Here, I introduce developments in, and our experiences with, the genetic evaluation of yield traits of Holstein cattle in Japan.

Selection on milk production and conformation traits during the last two decades in Japan

Objective

The purpose of this study was to compare intended and actual yearly genetic gains for milk production and conformation traits and to investigate the simple selection criterion practiced among milk production and conformation traits during the last two decades in Japan. Learning how to utilize the information on intended and actual genetic gains during the last two decades into the genomic era is vital.

Methods

Genetic superiority for each trait for four paths of selection (sires to breed bulls [SB], sires to breed cows [SC], dams to breed bulls [DB], and dams to breed cows [DC]) was estimated. Actual practiced simple selection criteria were investigated among milk production and conformation traits and relative emphasis on milk production and conformation traits was compared.

Results

Selection differentials in milk production traits were greater than those of conformation traits in all four paths of selection. Realized yearly genetic gain was less than that intended for milk production traits. Actual annual genetic gain for conformation traits was equivalent to or greater than intended. Retrospective selection weights of milk production and conformation traits were 0.73:0.27 and 0.56:0.44 for intended and realized genetic gains, respectively.

Conclusion

Selection was aimed more toward increasing genetic gain in milk production than toward conformation traits over the past two decades in Japan. In contrast, actual annual genetic gain for conformation traits was equivalent to or greater than intended. Balanced selection between milk production and conformation traits tended to be favored during actual selection. Each of four paths of selection (SB, SC, DB, and DC) has played an individual and important role. With shortening generation interval in the genomic era, a young sire arises before the completion of sire’s daughters’ milk production records. How to integrate these four paths of selection in the genomic era is vital.

Application of random regression models for genetic analysis of 305-d milk yield over different lactations of Iranian Holsteins

Objective

During the last decade, genetic evaluation of dairy cows using longitudinal data (test day milk yield or 305- day milk yield) using random regression method has been officially adopted in several countries. The objectives of this study were to estimate covariance functions for genetic and permanent environmental effects and to obtain genetic parameters of 305-day milk yield over seven parities.

Methods

Data including 60,279 total 305–day milk yield of 17,309 Iranian Holstein dairy cows in 7 parities calved between 20 to 140 months between 2004 and 2011. Residual variances were modeled by homogeneous and step functions with 7 and 10 classes.

Results

The results showed that a third order polynomial for additive genetic and permanent environmental effects plus a step function with 10 classes for the residual variance was the most adequate and parsimonious model to describe the covariance structure of the data. Heritability estimates obtained by this model varied from 0.17 to 0.28. The performance of this model was better than repeatability model. Moreover, 10 classes of residual variance produce the more accurate result than 7 classes or homogeneous residual effect.

Conclusion

A quadratic Legendre polynomial for additive genetic and permanent environmental effects with 10 step function residual classes are sufficient to produce a parsimonious model that explained the change in 305-day milk yield over consecutive parities of Iranian Holstein cows.

Random regression model with heterogeneous residual variance reduces polynomial order fitted for permanent environmental effect but does not affect genetic parameters for milk production in Sahiwal cattle

The random regression test-day models can accelerate the genetic improvement of Sahiwal cattle as test-day milk yield models offer a faster, accurate and economical approach of genetic evaluation. First three lactation monthly test-day records of Sahiwal cows calved between 1961 and 2012 at ICAR-National Dairy Research Institute, Karnal were used to fit random regression model (RRM) with various order of legendre polynomial, and a constant (RRM-HOM) or heterogeneous residual variance (RRM-HET). For both RRM-HOM and RRM-HET third order legendre polynomial for modelling additive genetic effects were found best. There was reduction in order of fit for modelling permanent environmental effects due to assumption of heterogeneous residual variance, as legendre polynomial of sixth order for RRM-HOM and fourth or fifth order for RRM-HET was found to be best, for modelling the permanent environmental effect. First two eigenvalues of additive genetic random regression coefficient matrix explained more than 99% of the additive genetic variation, whereas four eigenvalues explained ~98% of the permanent environment variations. First eigenfunction from both the models did not show any large change along lactation, suggesting most variation can be explained by genes that act in same way during lactation. The heritability estimates were generally low but moderate for some test-day milk yields, and it ranged from 0.007 to 0.088 for first, 0.044 to 0.279 for second, and 0.089 to 0.129 for third lactation from RRM-HOM. The values of genetic correlation between test-day milk yields ranged from 0.06 to 0.99 for first, 0.77 to 0.99 for second, and 0.07 to 0.99 for third lactation, from RRM-HOM. The value of permanent environment correlation ranged from 0.30 to 0.98 for first, 0.07 to 0.99 for second, and 0.16 to 0.98 for third lactation. The genetic correlations between two adjacent test-days were high (≥0.90). RRM-HET also gave similar heritability and correlation estimates. The rank correlation between sire breeding values for first, second, and third lactation, estimated using two models were 0.98, 1.00, and 0.99, respectively, indicating there was no difference in the ranking of animals using two models. Thus the random regression model with lower order of polynomial for modelling additive genetic effect and higher order polynomial for modelling animal permanent environmental effect was found suitable for genetic evaluation and both RRM-HOM and RRM-HET can be used for modelling test-day milk yield and breeding value prediction in Sahiwal cattle.

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.

Application of supernodal sparse factorization and inversion to the estimation of (co)variance components by residual maximum likelihood

We demonstrated that supernodal techniques were more efficient than traditional methods for factorization and inversion of a coefficient matrix of mixed model equations (MME), which are often required in residual maximum likelihood (REML). Supernodal left-looking and inverse multifrontal algorithms were employed for sparse factorization and inversion, respectively. The approximate minimum degree or multilevel nested dissection was used for ordering. A new computer package, Yet Another MME Solver (YAMS), was developed and compared with FSPAK with respect to computing time and size of temporary memory for 13 test matrices. The matrices were produced by fitting animal models to dairy data and by using simulations from sire, sire-maternal grand sire, maternal and dominance models for phenotypic data and animal model for genomic data. The order of matrices ranged from 32,840 to 1,048,872. The YAMS software factorized and inverted the matrices up to 13 and 10 times faster than FSPAK, respectively, when an appropriate ordering strategy was applied. The YAMS package required at most 282 MB and 512 MB of temporary memory for factorization and inversion, respectively. Processing time per iteration in average information REML was reduced, using YAMS. The YAMS package is freely available on request by contacting the corresponding author.

Lactation persistency as a component trait of the selection index and increase in reliability by using single nucleotide polymorphism in net merit defined as the first five lactation milk yields and herd life

We first sought to clarify the effects of discounted rate, survival rate, and lactation persistency as a component trait of the selection index on net merit, defined as the first five lactation milks and herd life (HL) weighted by 1 and 0.389 (currently used in Japan), respectively, in units of genetic standard deviation. Survival rate increased the relative economic importance of later lactation traits and the first five lactation milk yields during the first 120 months from the start of the breeding scheme. In contrast, reliabilities of the estimated breeding value (EBV) in later lactation traits are lower than those of earlier lactation traits. We then sought to clarify the effects of applying single nucleotide polymorphism (SNP) on net merit to improve the reliability of EBV of later lactation traits to maximize their increased economic importance due to increase in survival rate. Net merit, selection accuracy, and HL increased by adding lactation persistency to the selection index whose component traits were only milk yields. Lactation persistency of the second and (especially) third parities contributed to increasing HL while maintaining the first five lactation milk yields compared with the selection index whose only component traits were milk yields. A selection index comprising the first three lactation milk yields and persistency accounted for 99.4% of net merit derived from a selection index whose components were identical to those for net merit. We consider that the selection index comprising the first three lactation milk yields and persistency is a practical method for increasing lifetime milk yield in the absence of data regarding HL. Applying SNP to the second- and third-lactation traits and HL increased net merit and HL by maximizing the increased economic importance of later lactation traits, reducing the effect of first-lactation milk yield on HL (genetic correlation (rG) = -0.006), and by augmenting the effects of the second- and third-lactation milk yields on HL (rG = 0.118 and 0.257, respectively).

Genetic improvement of total milk yield and total lactation persistency of the first three lactations in dairy cattle

The objective of this study was to compare 6 selection criteria in terms of 3-parity total milk yield and 9 selection criteria in terms of total net merit (H) comprising 3-parity total milk yield and total lactation persistency. The 6 selection criteria compared were as follows: first-parity milk estimated breeding value (EBV; M1), first 2-parity milk EBV (M2), first 3-parity milk EBV (M3), first-parity eigen index (EI(1)), first 2-parity eigen index (EI(2)), and first 3-parity eigen index (EI(3)). The 9 selection criteria compared in terms of H were M1, M2, M3, EI(1), EI(2), EI(3), and first-parity, first 2-parity, and first 3-parity selection indices (I(1), I(2), and I(3), respectively). In terms of total milk yield, selection on M3 or EI(3) achieved the greatest genetic response, whereas selection on EI(1) produced the largest genetic progress per day. In terms of total net merit, selection on I(3) brought the largest response, whereas selection EI(1) yielded the greatest genetic progress per day. A multiple-lactation random regression test-day model simultaneously yields the EBV of the 3 lactations for all animals included in the analysis even though the younger animals do not have the opportunity to complete the first 3 lactations. It is important to use the first 3 lactation EBV for selection decision rather than only the first lactation EBV in spite of the fact that the first-parity selection criteria achieved a faster genetic progress per day than the 3-parity selection criteria. Under a multiple-lactation random regression animal model analysis, the use of the first 3 lactation EBV for selection decision does not prolong the generation interval as compared with the use of only the first lactation EBV. Thus, it is justified to compare genetic response on a lifetime basis rather than on a per-day basis. The results suggest the use of M3 or EI(3) for genetic improvement of total milk yield and the use of I(3) for genetic improvement of total net merit H. Although this study deals with selection for 3-parity milk production, the same principle applies to selection for lifetime milk production.