Genomic analyses of claw disorders in Holstein cows: Genetic parameters,trait associations,and genome-wide associations considering interactions of SNP and heat stress

The aim of the present study was an in-depth genomic analysis to understand the genomic mechanisms of the 3 claw disorders dermatitis digitalis (DD), interdigital hyperplasia (HYP), and sole ulcer (SU). In this regard, we estimated genetic parameters based on genomic relationship matrices, performed genome-wide association studies, annotated potential candidate genes, and inferred genetic associations with breeding goal traits considering the most important chromosomal segments. As a further novelty of this study, we inferred possible SNP × heat stress interactions for claw disorders. The study consisted of 17,264 first-lactation Holstein Friesian cows kept in 50 large-scale contract herds. The disease prevalence was 15.96, 2.36, and 8.20% for DD, HYP, and SU, respectively. The remaining breeding goal traits consisted of type traits of the feet and leg composite, female fertility, health traits, and 305-d production traits. The final genotype data set included 44,474 SNPs from the 17,264 genotyped cows. Heritabilities for DD, HYP, and SU were estimated in linear and threshold models considering the genomic relationship matrix (G matrix). Genetic correlations with breeding goal traits based on G were estimated in a series of bivariate linear models, which were verified via SNP effect correlations for specific chromosome segments (i.e., segments harboring potential candidate genes for DD, HYP, and SU). Genome-wide association studies were performed for all traits in a case-control design by applying a single SNP linear mixed model. Furthermore, for DD, HYP, and SU, we modeled SNP × heat stress interactions in genome-wide association studies. Single nucleotide polymorphism-based heritabilities were 0.04 and 0.08 for DD, 0.03 and 0.10 for SU, and 0.03 and 0.23 for HYP from linear and threshold models, respectively. The genetic correlations between DD, HYP, and SU with conformation traits from the feet and leg composite were positive throughout, indicating the value of indirect selection on conformation traits to improve claw health. Genetic correlations between DD, SU, and HYP with other breeding goal traits indicated impaired female fertility, impaired udder health status, and productivity decline of diseased cows. Genetic correlations among DD, SU, and HYP were moderate to large, indicating that different claw disorders have similar genetic mechanisms. Nevertheless, we identified disease-specific potential candidate genes, and genetic associations based on the surrounding SNPs partly differed from the genetic correlations. Especially for candidate genes contributing to 2 traits simultaneously, correlations based on SNP effects from the respective chromosome segment were close to 1 or to ?1. In this regard, we annotated the candidate genes KRT33A and KRT33B for HYP and DD, KIF27 for HYP and calving to first insemination, and MAN1A1 for SU and the production traits. For SNP × heat stress interactions, we identified significant SNPs on BTA 2, 4, 5, 7, 8, 9, 13, 22, 25, and 28, and we annotated the potential candidate genes FSIP2, CLCN1, ADGRV1, DOP1A, THBD, and RHOBTB1. Results indicate gene-specific mechanisms of the claw disorders only in specific environments.     

Genomic predictions for yield traits in US Holsteins with unknown parent groups

The objective of this study was to assess the reliability and bias of estimated breeding values (EBV) from traditional BLUP with unknown parent groups (UPG), genomic EBV (GEBV) from single-step genomic BLUP (ssGBLUP) with UPG for the pedigree relationship matrix (A) only (SS_UPG), and GEBV from ssGBLUP with UPG for both A and the relationship matrix among genotyped animals (A₂₂; SS_UPG2) using 6 large phenotype-pedigree truncated Holstein data sets. The complete data included 80 million records for milk, fat, and protein yields from 31 million cows recorded since 1980. Phenotype-pedigree truncation scenarios included truncation of phenotypes for cows recorded before 1990 and 2000 combined with truncation of pedigree information after 2 or 3 ancestral generations. A total of 861,525 genotyped bulls with progeny and cows with phenotypic records were used in the analyses. Reliability and bias (inflation/deflation) of GEBV were obtained for 2,710 bulls based on deregressed proofs, and on 381,779 cows born after 2014 based on predictivity (adjusted cow phenotypes). The BLUP reliabilities for young bulls varied from 0.29 to 0.30 across traits and were unaffected by data truncation and number of generations in the pedigree. Reliabilities ranged from 0.54 to 0.69 for SS_UPG and were slightly affected by phenotype-pedigree truncation. Reliabilities ranged from 0.69 to 0.73 for SS_UPG2 and were unaffected by phenotype-pedigree truncation. The regression coefficient of bull deregressed proofs on (G)EBV (i.e., GEBV and EBV) ranged from 0.86 to 0.90 for BLUP, from 0.77 to 0.94 for SS_UPG, and was 1.00 ± 0.03 for SS_UPG2. Cow predictivity ranged from 0.22 to 0.28 for BLUP, 0.48 to 0.51 for SS_UPG, and 0.51 to 0.54 for SS_UPG2. The highest cow predictivities for BLUP were obtained with the most extreme truncation, whereas for SS_UPG2, cow predictivities were also unaffected by phenotype-pedigree truncations. The regression coefficient of cow predictivities on (G)EBV was 1.02 ± 0.02 for SS_UPG2 with the most extreme truncation, which indicated the least biased predictions. Computations with the complete data set took 17 h with BLUP, 58 h with SS_UPG, and 23 h with SS_UPG2. The same computations with the most extreme phenotype-pedigree truncation took 7, 36, and 15 h, respectively. The SS_UPG2 converged in fewer rounds than BLUP, whereas SS_UPG took up to twice as many rounds. Thus, the ssGBLUP with UPG assigned to both A and A₂₂ provided accurate and unbiased evaluations, regardless of phenotype-pedigree truncation scenario. Old phenotypes (before 2000 in this data set) did not affect the reliability of predictions for young selection candidates, especially in SS_UPG2.     

Genetic parameters of endocrine fertility traits based on in-line milk progesterone profiles in Swedish Red and Holstein dairy cows

Evaluating fertility traits based on endocrine progesterone profiles is becoming a promising option to improve dairy cow fertility. Several studies have been conducted on endocrine fertility traits, mainly in the Holstein breed. In this study, focusing also on the Swedish Red (SR) breed, genetic parameters were estimated for classical and endocrine fertility traits, the latter based on in-line milk progesterone records obtained for 14 Swedish herds using DeLaval Herd Navigator (DeLaval International, Tumba, Sweden). A total of 210,403 observations from 3,437 lactations of 1,107 SR and 1,538 Holstein cows were used. Mixed linear animal models were used for estimation of genetic parameters. Least squares means analysis showed that Holstein cows had a 2.5-d-shorter interval from calving to commencement of luteal activity (C-LA) and longer length of first inter-ovulatory interval (IOI) than SR cows. The highest mean interval for C-LA, IOI, and first luteal phase length (LPL) was observed in the fourth parity. The incidence of short (<18 d), normal, (18–24 d), and long (>24 d) IOI was 29.3, 40.7, and 30%, respectively. Genetic analysis indicated moderate heritability (h²) for C-LA (h² = 0.24), luteal activity during the first 60 d in milk (LA60, h² = 0.15), proportion of samples with luteal activity (PLA, h² = 0.13), and calving to first heat (CFH, h² = 0.18), and low heritability estimates for LPL (h² = 0.08) and IOI (h² = 0.03) in the combined data set for both breeds. Similar heritability estimates were obtained for each breed separately except for IOI and LPL in SR cows, for which heritability was estimated to be zero. Swedish Red cows had 0.01 to 0.06 higher heritability estimates for C-LA, LA60, and PLA than did Holstein cows. Calving interval had moderate heritability among the classical traits for Holstein and the combined data set, but h² was zero for SR. Commencement of luteal activity had a strong genetic correlation with LA60 (mean ± SE; ?0.88 ± 0.06), PLA (?0.72 ± 0.11), and CFH (0.90 ± 0.04). Similarly, CFH had a strong genetic correlation with IOI (0.98 ± 0.20). Number of inseminations per series showed a weak genetic correlation with all endocrine traits except IOI. Overall, endocrine traits had higher heritability estimates than classical traits in both breeds, and may have a better potential to explain the actual reproductive status of dairy cows than classical traits. This might favor inclusion of some endocrine fertility traits—especially those related to commencement of luteal activity—as selection criteria and breeding goal traits if recording becomes more common in herds. Further studies on genetic and genomic evaluations for endocrine fertility traits may help to provide firm conclusions. A prerequisite is that the data from automatic devices be made available to recording and breeding organizations in the future and included in a central database.     

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.     

Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

Milk is regarded as an important nutrient for humans, and Chinese Holstein cows provide high-quality milk for billions of Chinese people. Therefore, detecting quantitative trait nucleotides (QTN) or candidate genes for milk production traits in Chinese Holstein is important. In this study, we performed genome-wide association studies (GWAS) in a Chinese Holstein population of 6,675 cows and 71,633 SNP using deregressed proofs (DRP) as phenotypes to replicate our previous study in a population of 1,815 cows and 39,163 SNP using estimated breeding values (EBV) as phenotypes. The associations between 3 milk production traits—milk yield (MY), fat percentage (FP), and protein percentage (PP)—and the SNP were determined by using an efficient rotated linear mixed model, which benefits from linear transformations of genomic estimated values and Eigen decomposition of the genomic relationship matrix algorithm. In total, we detected 94 SNP that were significantly associated with one or more milk production traits, including 7 SNP for MY, 76 for FP, and 36 for PP; 87% of these SNP were distributed across Bos taurus autosomes 14 and 20. In total, 83 SNP were found to be located within the reported quantitative trait loci (QTL) regions, and one novel segment (between 1.41 and 1.49 Mb) on chromosome 14 was significantly associated with FP, which could be an important candidate QTL region. In addition, the detected intervals were narrowed down from the reported regions harboring causal variants. The top significant SNP for the 3 traits was ARS-BFGL-NGS-4939, which is located within the DGAT1 gene. Five detected genes (CYHR1, FOXH1, OPLAH, PLEC, VPS28) have effects on all 3 traits. Our study provides a suite of QTN, candidate genes, and a novel QTL associated with milk production traits, and thus forms a solid basis for genomic selection and molecular breeding for milk production traits in Chinese Holstein.     

Genome-wide associations for heat stress response suggest potential candidate genes underlying milk fatty acid composition in dairy cattle

Contents of milk fatty acids (FA) display remarkable alterations along climatic gradients. Detecting candidate genes underlying such alterations might be beneficial for the exploration of climate sensitivity in dairy cattle. Consequently, we aimed on the definition of FA heat stress indicators, considering FA breeding values in response to temperature-humidity index (THI) alterations. Indicators were used in GWAS, in ongoing gene annotations and for the estimation of chromosome-wide variance components. The phenotypic data set consisted of 39,600 test-day milk FA records from 5,757 first-lactation Holstein dairy cows kept in 16 large-scale German cooperator herds. The FA traits were C18:0, polyunsaturated fatty acids (PUFA), saturated fatty acids (SFA), and unsaturated fatty acids (UFA). After genotype quality control, 40,523 SNP markers from 3,266 cows and 930 sires were considered. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI, which were allocated to 10 different THI classes. The same FA from 3 stages of lactation were considered as different, but genetically correlated traits. Consequently, a 3-trait reaction norm model was used to estimate genetic parameters and breeding values for FA along THI classes, considering either pedigree (A) or genomic (G) relationship matrices. De-regressed proofs and genomic estimated breeding values at the intermediate THI class 5 and at the extreme THI class 10 were used as pseudophenotypes in ongoing genomic analyses for thermoneutral (TNC) and heat stress conditions (HSC), respectively. The differences in de-regressed proofs and in genomic estimated breeding values from both THI classes were pseudophenotypes for heat stress response (HSR). Genetic correlations between the same FA under TNC and HSC were smallest in the first lactation stage and ranged from 0.20 for PUFA to 0.87 for SFA when modeling with the A matrix, and from 0.35 for UFA to 0.86 for SFA when modeling with the G matrix. In the first lactation stage, larger additive genetic variances under HSC compared with TNC indicate climate sensitivity for C18:0, PUFA, and UFA. Climate sensitivity was also reflected by pronounced chromosome-wide genetic variances for HSR of PUFA and UFA in the first stage of lactation. For all FA under TNC, HSC, and HSR, quite large genetic variance proportions were explained by BTA14. In GWAS, 30 SNP (within or close to 38 potential candidate genes) overlapped for HSR of the different FA. One unique potential candidate gene (AMFR) was detected for HSR of PUFA, 15 for HSR of SFA (ADGRB1, DENND3, DUSP16, EFR3A, EMP1, ENSBTAG00000003838, EPS8, MGP, PIK3C2G, STYK1, TMEM71, GSG1, SMARCE1, CCDC57, and FASN) and 3 for HSR of UFA (ENSBTAG00000048091, PAEP, and EPPK1). The identified unique genes play key roles in milk FA synthesis and are associated with disease resistance in dairy cattle. The results suggest consideration of FA in combination with climatic responses when inferring genetic mechanisms of heat stress in dairy cows.     

Modeling missing pedigree in single-step genomic BLUP

The objective was to compare methods of modeling missing pedigree in single-step genomic BLUP (ssGBLUP). Options for modeling missing pedigree included ignoring the missing pedigree, unknown parent groups (UPG) based on A (the numerator relationship matrix) or H (the unified pedigree and genomic relationship matrix), and metafounders. The assumptions for the distribution of estimated breeding values changed with the different models. We simulated data with heritabilities of 0.3 and 0.1 for dairy cattle populations that had more missing pedigrees for animals of lesser genetic merit. Predictions for the youngest generation and UPG solutions were compared with the true values for validation. For both traits, ssGBLUP with metafounders provided accurate and unbiased predictions for young animals while also appropriately accounting for genetic trend. Accuracy was least and bias was greatest for ssGBLUP with UPG for H for the trait with heritability of 0.3 and with UPG for A for the trait with heritability of 0.1. For the trait with heritability of 0.1 and UPG for H, the UPG accuracy (SD) was ?0.49 (0.12), suggesting poor estimates of genetic trend despite having little bias for validations on young, genotyped animals. Problems with UPG estimates were likely caused by the lesser amount of information available for the lower heritability trait. Hence, UPG need to be defined differently based on the trait and amount of information. More research is needed to investigate accounting for UPG in A₂₂ to better account for missing pedigrees for genotyped animals.     

Alternative SNP weighting for single-step genomic best linear unbiased predictor evaluation of stature in US Holsteins in the presence of selected sequence variants

Causal variants inferred from sequence data analysis are expected to increase accuracy of genomic selection. In this work we evaluated the gain in reliability of genomic predictions, for stature in US Holsteins, when adding selected sequence variants to a pre-existent SNP chip. Two prediction methods were tested: de-regressed proofs assuming heterogeneous (genomic BLUP; GBLUP) residual variances and by single-step GBLUP (ssGBLUP) using actual phenotypes. Phenotypic data included 3,999,631 records for stature on 3,027,304 Holstein cows. Genotypes on 54,087 SNP markers (54k) were available for 26,877 bulls. Additionally, 16,648 selected sequence variants were combined with the 54k markers, for a total of 70,735 (70k) markers. In all methods, SNP in the genomic relationship matrix (G) were unweighted or weighted iteratively, with weights derived either by SNP effects squared or by a nonlinear method that resembles BayesA (nonlinear A). Reliability of genomic predictions were obtained by cross validation. With unweighted G derived from 54k markers, the reliabilities (× 100) were 72.4 for GBLUP and 75.3 for ssGBLUP. With unweighted G derived from 70k markers, the reliabilities were 73.4 and 76.0, respectively. Weighting by nonlinear A changed reliabilities to 73.3, and 75.9, respectively. Addition of selected sequence variants had a small effect on reliabilities. Weighting by quadratic functions reduced reliabilities. Weighting by nonlinear A increased reliabilities for GBLUP but had only a small effect in ssGBLUP. Reliabilities for direct genomic values extracted from ssGBLUP using unweighted G with 54k were higher than reliabilities by any GBLUP. Thus, ssGBLUP seems to capture more information than GBLUP and there is less room for extra reliability. Improvements in GBLUP may be because the weights in G change the covariance structure, which can explain a proportion of the variance that is accounted for when a heterogeneous residual variance is assumed by considering a different number of daughters per bull.     

Genetic parameters and association of national evaluations with breeding values for health traits in US organic Holstein cows

Among other regulations, organic cows in the United States cannot receive antibiotics and preserve their organic status, emphasizing the importance of prevention of illness and benefit of high genetic merit for disease resistance. At the same time, data underlying national genetic evaluations primarily come from conventional cows, drawing concern to the possibility of a genotype by environment interaction whereby the value of a genotype varies depending on the environment, and potentially limits the relevance of these evaluations to organic cows. The objectives of this study were to characterize the genetics of and determine the presence of genotype by environment interaction for health traits in US organic dairy cows. Individual cow health data were obtained from 16 US Department of Agriculture certified organic dairy farms from across the United States that used artificial insemination and maintained detailed records. Data were obtained for the following traits: died, lameness, mastitis, metabolic diseases (displaced abomasum, ketosis, and milk fever), reproductive diseases (abortion, metritis, and retained placenta), transition health events (any health event occurring 21 d before or after parturition), and all health events. Binary phenotypes (1 = diseased, 0 = otherwise) for 38,949 lactations on 19,139 Holstein cows were used. Genotypes from 2,347 cows with 87.5% or greater Holstein breed-based representation were incorporated into single-step multitrait threshold animal models that included stayability (1 = completed lactation, 0 = otherwise). Gibbs sampling was used. Genomic predicted transmitting abilities (gPTA) from national genetic evaluations were obtained for sires for production, fitness, health, and conformation traits. We approximated genetic correlations for sires using these gPTA and our estimated breeding values. We also regressed health phenotypes on cow estimated breeding values and sire gPTA. Heritabilities (± standard error) ranged from 0.03 ± 0.01 (reproductive diseases) to 0.11 ± 0.03 (metabolic diseases). Most genetic correlations among health traits were positive, though the genetic correlation between metabolic disease and mastitis was ?0.42 ± 0.17. Approximate genetic correlations between disease resistance for our health trait categories and disease resistance for the nationally-evaluated health traits generally carried the expected sign with the strongest correlation for mastitis (0.72 ± 0.084). Regression coefficients carried the expected sign and were mostly different from zero, indicating that evaluations from primarily conventional herd data predicted health on organic farms. In conclusion, use of national evaluations for health traits should afford genetic improvement for health in US organic herds.     

Phenotypic relationships,genetic parameters,genome-wide associations,and identification of potential candidate genes for ketosis and fat-to-protein ratio in German Holstein cows

Energy demand for milk production in early lactation exceeds energy intake, especially in high-yielding Holstein cows. Energy deficiency causes increasing susceptibility to metabolic disorders. In addition to several blood parameters, the fat-to-protein ratio (FPR) is suggested as an indicator for ketosis, because a FPR >1.5 refers to high lipolysis. The aim of this study was to analyze phenotypic, quantitative genetic, and genomic associations between FPR and ketosis. In this regard, 8,912 first-lactation Holstein cows were phenotyped for ketosis according to a veterinarian diagnosis key. Ketosis was diagnosed if the cow showed an abnormal carbohydrate metabolism with increased content of ketone bodies in the blood or urine. At least one entry for ketosis in the first 6 wk after calving implied a score = 1 (diseased); otherwise, a score = 0 (healthy) was assigned. The FPR from the first test-day was defined as a Gaussian distributed trait (FPRgauss), and also as a binary response trait (FPRbin), considering a threshold of FPR = 1.5. After imputation and quality controls, 45,613 SNP markers from the 8,912 genotyped cows were used for genomic studies. Phenotypically, an increasing ketosis incidence was associated with significantly higher FPR, and vice versa. Hence, from a practical trait recording perspective, first test-day FPR is suggested as an indicator for ketosis. The ketosis heritability was slightly larger when modeling the pedigree-based relationship matrix (pedigree-based: 0.17; SNP-based: 0.11). For FPRbin, heritabilities were larger when modeling the genomic relationship matrix (pedigree-based: 0.09; SNP-based: 0.15). For FPRgauss, heritabilities were almost identical for both pedigree and genomic relationship matrices (pedigree-based: 0.14; SNP-based: 0.15). Genetic correlations between ketosis with FPRbin and FPRgauss using either pedigree- or genomic-based relationship matrices were in a moderate range from 0.39 to 0.71. Applying genome-wide association studies, we identified the specific SNP rs109896020 (BTA 5, position: 115,456,438 bp) significantly contributing to ketosis. The identified potential candidate gene PARVB in close chromosomal distance is associated with nonalcoholic fatty liver disease in humans. The most important SNP contributing to FPRbin was located within the DGAT1 gene. Different SNP significantly contributed to ketosis and FPRbin, indicating different mechanisms for both traits genomically.     

Culling reasons in organic and conventional dairy herds and genotype by environment interaction for longevity

Dairy cow longevity combines all functional traits and is thought to be especially important in organic production, which is an established, increasing part of Swedish dairy production, representing approximately 6% of the market. The aim of this study was to compare dynamics in culling reasons between organic and conventional production and to analyze genotype by environment interactions for longevity. The data contained information from all organic herds with information available from official recording (n = 402) and from approximately half of the conventional herds (n = 5,335). Records from Swedish Holsteins (n = 155,379) and Swedish Red cows (n = 160,794) that had their first calf between January 1998 and September 2003 were included. The opportunity period for longevity was at least 6 yr. Six longevity traits were defined: length of productive life; survival through first, second, and third lactations; fertility-determined survival; and udder health-determined survival. Twenty codes were used to describe the cause of culling, and these were divided into 8 groups: udder health, low fertility, low production, leg problems, metabolic diseases, other diseases, other specified causes, and unspecified cause. The main reason for culling cows in organic herds was poor udder health, whereas for cows in conventional herds it was low fertility. Furthermore, the shift in main culling reason from fertility, which was most common in first lactation regardless of production system, to udder health occurred at a lower age in organic production. Heritabilities and genetic correlations for the longevity traits expressed in organic and conventional herds were estimated from a bivariate animal model. The genetic correlations were close to unity (>0.88), except for fertility-determined survival in the Swedish Red breed (0.80). Heritabilities were low to moderate, and no clear pattern was identified for production system or breed. In general, the results indicate that farmers’ culling criteria differ between organic and conventional production. Different preferences may influence the need for alternative selection indexes for organic production, with different weightings of traits, or a separate breeding program. However, no genotype by environment interaction of importance was found between the production systems.     

Invited review: selection on net merit to improve lifetime profit

Genetic selection has made dairy cows more profit-able producers of milk. Genetic evaluations began with 2 traits measured on a few cows but now include many traits measured on millions of cows. Selection indexes from USDA included yield traits beginning in 1971, productive life and somatic cell score beginning in 1994, conformation traits in 2000, and cow fertility and calving ease in 2003. This latest revision of net merit should result in 2% more progress, worth 5 million dollars/yr nationally, with improved cow health and fitness, but slightly less progress for yield. Fertility and longevity evaluations have similar reliability because cows can have several fertility records, each with lower heritability, compared with one longevity record with higher heritability. Lifetime profit can be estimated more accurately if less heritable traits are evaluated and included instead of ignored. Milk volume has a positive value for fluid use, but a negative value for cheese production. Thus, multiple selection indexes are needed for different markets and production systems. Breeding programs should estimate future rather than current costs and prices. Many other nations have derived selection indexes similar to US net merit.     

Genetic and phenotypic variation and consistency in cow preference and circadian use of robotic milking units

Many dairy herds use automatic milking stations (AMS), with cows in large herds often having access to 2 or more AMS, and must choose between them when they go for milking. Individual cows acquire routines of either consistently using a specific milking box or consistently using any available milking box. Here, we hypothesized that the degree of use of the same milking box was an expression of preference, and quantified it as preference consistency score (PCS). The PCS was calculated as a ratio between the excess frequencies of the first choice over the base frequency of “not first choice” over 15-d segments of lactation. This ratio was 0 if all choices were taken equally, and became 1.0 if only the first choice was taken in all events. We investigated the consistency of milking box preference in 2 cohorts (one Holstein and one Jersey) across 6 commercial dairy herds in Denmark (n = 4,665 cows total). In addition to PCS, we recorded and analyzed associated milking and behavior traits, including a time profile index showing use of specific clock hours when cows were milked (Time_profile, based on excess use of specific clock hours), milking frequency, time spent in the milking box, and milk yield. Records from each milking event were condensed into 15-d segments based on days in milk. The data were analyzed using a linear mixed model, with random genetic and individual cow effects, to estimate heritability (h²), repeatability (t), and individual level correlations (r_i) between traits. The average PCS was 0.43 and 0.41 in Holstein and Jersey, respectively, showing that cows developed routines for consistently using the same milking box; however, some cows had lower preference (i.e., greater flexibility in use). The Time_profile indicated that some cows were milked in a few hour-bins, whereas others were more flexible. The PCS and Time_profile traits had low heritability (h², PCS/Time_profile = 0.07 ± 0.02/0.11 ± 0.02 Holstein, 0.13 ± 0.03/0.04 ± 0.02 Jersey) and moderate repeatability (t, PCS/Time_profile = 0.47/0.40 Holstein, 0.50/0.42 Jersey). The 2 traits were weakly correlated with each other (r_i = 0.18 and 0.17), and were weakly correlated with milk yield (r_i range: 0.0 to ?0.10). However, the time profile was strongly correlated with milking frequency (r_i range: ?0.81 to ?0.73), and was moderately correlated with daily box time (r_i range: ?0.43 to ?0.35). In general, Holstein and Jersey parameter estimates were of similar size, and thus in good agreement. Overall, individual cows covered a broad spectrum of preference consistency, both regarding the use of specific milking boxes and time profiles, with these 2 traits representing different aspects or dimensions of milking behavior. The findings that some cows have strong preferences for specific AMS may be most useful in herd management and farm design. The weak correlation to milk yield indicated that yield minimally affected these 2 milking associated behavior traits. In conclusion, although the traits were repeatable, heritability was low; thus, genetic selection for milk yield might minimally affect these 2 traits.     

Genome-wide association study of normal and atypical progesterone profiles in Holstein-Friesian dairy cows

Female fertility has a major role in dairy production and affects the profitability of dairy cattle. The genetic progress obtained by traditional selection can be slow because of the low heritability of classical fertility traits. Endocrine fertility traits based on progesterone concentration in milk have higher heritability and more directly reflect the cow's own reproductive physiology. The aim of our study was to identify genomic regions for 7 endocrine fertility traits in dairy cows by performing a genome-wide association study with 54,000 SNP. The next step was to fine-map targeted genomic regions with significant SNP using imputed sequences to identify potential candidate genes associated with the normal and atypical progesterone profiles. The association between a SNP and a phenotype was assessed by a single SNP analysis, using a linear mixed model that included a random polygenic effect. Phenotypes and genotypes were available for 1,126 primiparous and multiparous Holstein-Friesian cows from research herds in Ireland, the Netherlands, Sweden, and the United Kingdom. In total, 44 significant SNP associated with 7 endocrine fertility traits were identified on Bos taurus autosome (BTA) 1–4, 6, 8–9, 11–12, 14–17, 19, 21–24, and 29. Three chromosomes, BTA8, BTA17, and BTA23, were imputed from 54,000 SNP genotypes to the whole-genome sequence level with Beagle version 4.1. The fine-mapping identified several significant associations with delayed cyclicity, cessation of cyclicity, commencement of luteal activity, and inter-ovulatory interval. These associations may contribute to an index of markers for genetic improvement of fertility. Several potential candidate genes reported to affect reproduction were also identified in the targeted genomic regions. However, due to high linkage disequilibrium, it was not possible to identify putative causal genes or polymorphisms for any of the regions.     

Investigating the day-to-day variations of potential marker fatty acids for organic milk in milk from conventionally and organically raised cows

Milk samples from organic and conventionally raised cows differ in the content of specific fatty acids due to the different feed used. Several marker fatty acids (phytanic acid, α-linolenic acid (18:3n-3), eicosapentaenoic acid (20:5n-3)) have been proposed to distinguish both types of dairy agriculture. Owing to the higher amount of grass in the feed of organic cows, the concentrations of these fatty acids are usually higher than in conventional milk. In this study, we were interested to determine the day-to-day fluctuation of these and further fatty acids in milk of cows. For this purpose, milk samples from an organic and a conventionally raised cow taken on subsequent days were analyzed for potential marker fatty acids for the authentication of organic milk. An organic cow was milked daily in the morning over a period of 30 days while milk of a conventional cow was collected for 15 days in the morning and evening. Based on milk lipids, no differences were found between morning and evening milk. However, the concentrations of phytanic acid, α-linolenic acid (18:3n-3), eicosapentaenoic acid (20:5n-3) were ~twofold higher in organic milk fat than in conventional samples. The day-to-day fluctuations were small, and the lowest value in organic milk was generally higher than the highest value in conventional milk. Phytanic acid is interesting as it will not be influenced by supplementation of n-3 fatty acids in the fodder of conventional cows. Pristanic acid was also ~40% higher in organic milk fat, but five conventional samples reached the lowest value in organic milk. Significantly higher concentrations in organic milk fat were also observed for 12-methyltridecanoic acid (i14:0) and 16-methylheptadecanoic acid (i18:0) as well as for vaccenic acid (18:1n-7tr).     

Genomic selection for producer-recorded health event data in US dairy cattle

Emphasizing increased profit through increased dairy cow production has revealed a negative relationship of production with fitness and health traits. Decreased cow health can affect herd profitability through increased rates of involuntary culling and decreased or lost milk sales. The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. The principal objective of this study was to use health data collected from on-farm computer systems in the United States to estimate variance components and heritability for health traits commonly experienced by dairy cows. A single-step analysis was conducted to estimate genomic variance components and heritabilities for health events, including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. A blended H matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (standard deviation = 0.005) for lameness to 0.36 (standard deviation = 0.08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and ketosis, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data are feasible in the United States, and that the inclusion of genomic data substantially improves reliabilities for these traits.     

Practical implementation of genetic groups in single-step genomic evaluations with Woodbury matrix identity–based genomic relationship inverse

The growing amount of genomic information in dairy cattle has increased computational and modeling challenges in the single-step evaluations. The computational challenges are due to the dense inverses of genomic (G) and pedigree (A₂₂) relationship matrices of genotyped animals in the single-step mixed model equations. An equivalent mixed model equation is given by single-step genomic BLUP that are based on the T matrix (ssGTBLUP), where these inverses are avoided by expressing G⁻¹ through a product of 2 rectangular matrices, and (A₂₂)⁻¹ through sparse matrix blocks of the inverse of full relationship matrix A⁻¹. A proper way to account genetic groups through unknown parent groups (UPG) after the Quaas-Pollak transformation (QP) is one key factor in a single-step model. When the UPG effects are incompletely accounted, the iterative solving method may have convergence problems. In this study, we investigated computational and predictive performance of ssGTBLUP with residual polygenic (RPG) effect and UPG. The QP transformation used A⁻¹ and, in the complete form, T and (A₂₂)⁻¹ matrices as well. The models were tested with official Nordic Holstein milk production test-day data and model. The results show that UPG can be easily implemented in ssGTBLUP having RPG. The complete QP transformation was computationally feasible when preconditioned conjugate gradient iteration and iteration on data without explicitly setting up G or A₂₂ matrices were used. Furthermore, for good convergence of the preconditioned conjugate gradient method, a complete QP transformation was necessary.     

Analysis of survival in dairy cows with supplementary data on type scores and housing systems from a region of northwest Germany.

In survival analysis, type traits can be included as covariates to evaluate their use as predictors for survival. One problem in such an analysis is the availability of suitable data. Whereas data on the length of productive life (LPL) of individual cows can be retrieved from milk recording data, for type traits, all cows in the population must be scored for type at least once. In the present analysis, a dataset from the Osnabruck region in northwestern Germany, which fulfilled this requirement in recent years, was used. Data consisted of 169,733 cows with information on LPL for calving years 1980 to 1996 (dataset I) and of 39,233 cows with information on LPL and type for calving years 1990 to 1996 (dataset II). A further dataset (III) contained 43,116 cows from calving years 1987 to 1996 and included information on the housing system for each herd. The basic model included stage of lactation, relative production within herd, change of herd size, and year-season as time dependent effects; age at calving as a time-independent effect; and herd-year-season and sire as random effects. Other effects (information on type, housing system) were included additionally. For data-set II, the scores for 15 linear type traits were also included as corrected phenotypic values, estimated breeding values, and residuals from a previous BLUP analysis. The package Survival Kit 3.0 was used for all analyses. The results indicate a moderate heritability of 0.17 and 0.18 for true and functional LPL (dataset I). Almost all type traits analyzed (dataset II) exceeded a 0.001 level of significance in their effect on survival. The strongest relationships between survival and type were found for udder depth, fore udder attachment, and front teat placement. The main result from the comparison of housing systems (dataset III) was that bedding has a positive effect on survival.     

Phenotypic,genetic, and single nucleotide polymorphism marker associations between calf diseases and subsequent performance and disease occurrences of first-lactation German Holstein cows

A total of 31,396 females born from 2010 to 2013 in 43 large-scale Holstein-Friesian herds were phenotyped for calf and cow disease traits using a veterinarian diagnosis key. Calf diseases were general disease status (cGDS), calf diarrhea (cDIA), and calf respiratory disease (cRD) recorded from birth to 2 mo of age. Incidences were 0.48 for cGDS, 0.28 for cRD, and 0.21 for cDIA. Cow disease trait recording focused on the early period directly after calving in first parity, including the interval from 10 d before calving to 200 d in lactation. For cows, at least one entry for the respective disease implied a score = 1 (sick); otherwise, score = 0 (healthy). Corresponding cow diseases were first-lactation general disease status (flGDS), first-lactation diarrhea (flDIA), and first-lactation respiratory disease (flRD). Additional cow disease categories included mastitis (flMAST), claw disorders (flCLAW), female fertility disorders (flFF), and metabolic disorders (flMET). A further cow trait category considered first-lactation test-day production traits from official test-days 1 and 2 after calving. The genotype data set included 41,256 single nucleotide polymorphisms (SNP) from 9,388 females with phenotypes. Linear and generalized linear mixed models with a logit link-function were applied to Gaussian and categorical cow traits, respectively, considering the calf disease as a fixed effect. Most of the calf diseases were not significantly associated with the occurrence of any cow disease. By trend, increasing risks for the occurrence of cow diseases were observed for healthy calves, indicating mechanisms of disease resistance with aging. Also by trend, occurrence of calf diseases was associated with decreasing milk, protein, and fat yields. Univariate linear and threshold animal models were used to estimate heritabilities and breeding values (EBV) for all calf and cow traits. Heritabilities for cGDS and cRD were 0.06 and 0.07 for cDIA. Genetic correlations among all traits were estimated using linear-linear animal models in a series of bivariate runs. The genetic correlation between cDIA and cRD was 0.29. Apart from the genetic correlation between flRD with cGDS (?0.38), EBV correlations and genetic correlations between calf diseases with all cow traits were close to zero. Genome-wide association studies were applied to estimate SNP effects for cRD and cDIA, and for the corresponding traits observed in cows (flRD and flDIA). Different significant SNP markers contributed to cDIA and flDIA, or to cRD and flRD. The average correlation coefficient between cRD and flRD considering SNP effects from all chromosomes was 0.01, and between cDIA and flDIA was ?0.04. In conclusion, calf diseases are not appropriate early predictors for cow traits during the early lactation stage in parity 1.     

Effects of lysophospholipids on short-term production,nitrogen utilization,and rumen fermentation and bacterial population in lactating dairy cows

An experiment was conducted to examine effects of supplemental lysophospholipids (LPL) in dairy cows. Eight ruminally cannulated lactating Holstein cows were used in a replicated 4 × 4 Latin square design. Dietary treatments were (1) a dairy ration [CON; 55% forage and 45% concentrate on a dry matter (DM) basis], (2) a positive control diet supplemented with monensin (MON; 16 mg/kg in dietary DM; Elanco Animal Health, Greenfield, IN], (3) a control diet supplemented with low LPL (0.05% of dietary DM; Lipidol Ultra, Easy Bio Inc., Seoul, South Korea), and (4) a control diet supplemented with high LPL (0.075% of dietary DM). Experimental periods were 21 d with 14-d diet adaptation and 7-d sample collection. Daily intake and milk yield were measured and rumen contents were collected for fermentation characteristics and bacterial population. Spot urine and fecal samples (8 samples/cow per period) were collected to determine nutrient digestibility and dietary N utilization. All data were analyzed using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC; group and cow within group were random effects and treatments, time, and their interaction were fixed effects). Preplanned contrasts were made to determine effect of MON versus CON, effect of LPL versus MON, and linear effect of increasing LPL. In the current study, responses to MON generally agreed with effects of monensin observed in the literature (increased milk yield and feed efficiency but decreased milk fat content). Supplementation of LPL to the diet did not alter DM intake but linearly increased milk yield, resulting in increases in feed efficiency (milk yield/DM intake) and milk protein and fat yields. However, total-tract digestibility of DM and organic matter tended to be lower (60.9 vs. 62.2% and 61.8 vs. 63.1%, respectively) for LPL compared with CON. Linear increases in milk N secretion and decreases in urinary N excretion were observed with increasing LPL in the diet. A slight decrease in acetate proportion in the rumen for LPL was found. Relative to MON, very few bacteria in the rumen were affected with increasing LPL. In conclusion, LPL is a potential feed additive that can increase milk yield and components and dietary N utilization. However, more studies with large numbers of animals are needed to confirm the effect of LPL on production. Similar positive effects on production were observed between LPL and MON, but individual mechanisms were likely different according to ruminal fermentation characteristics. Further studies are needed to explore the mode of action of LPL in dairy cows.