Search

The purpose of this study was to analyze the impact of seasonal losses due to heat stress in different environments and genetic group combinations. Data were available for 2 different swine populations: purebred Duroc animals raised in nucleus farms in Texas and North Carolina and crosses of Duroc and F1 females (Landrace × Large White) raised in commercial farms in Missouri and North Carolina; pedigrees provided links between animals from different states. Traits included BW at harvest age for purebred animals and HCW for crossbred animals. Weather data were collected at airports located close to the farms. Heat stress was quantified by a heat load function, defined by the units of temperature-humidity of temperature–humidity index (THI) greater than a certain threshold for 30 to 70 d before phenotype collection. Heat stress responses were quantified by a linear regression of phenotype on heat load. The greatest coefficient of determination occurred with a length of 30 d before phenotype measurements for all states and genetic groups. In the crossbreed data, THI thresholds were 67 in Missouri and 72 in North Carolina. For pure breeds, heat load had the best fit for THI thresholds greater than 70 in North Carolina, although differences in coefficient of determinations were negligible. On the other hand, no optimal THI threshold existed in Texas. In this study, heat stress had a greater impact in commercial farms than in nucleus farms and the effect of heat stress on weight varied by year and state. [ABSTRACT FROM AUTHOR]

The objective of this study was to deter-mine if weaning weight performance is genetically consistent across different environments in the United States. The American Angus Association provided weight and pedigree data. Weaning weights observed in the Southeast (SoE) and Northwest (NW) were the focus of this study, as these regions are perceived as opposite extremes in climate. The 2 most represent-ed calving seasons in each region were fall and win-ter in the SoE and winter and spring in the NW. The original data were edited to remove weaning weight records outside of 3 SD from the respective region-season mean, contemporary groups smaller than 20, and single-sire contemporary groups. The final dataset included 884,465 weaning weight records with 64,907 from fall-bom calves in the SoE, 74,820 from winter-born calves in the SoE, 346,724 from winter-bom calves in the NW and 398,014 from spring-bom calves in the NW. Weaning weights of calves bom in different region-season classes adjusted to 205 d of age were considered different but genetically correlated traits in a multivariate analysis. The sole fixed effect was wean-ing contemporary group and random effects included direct, maternal, maternal permanent environment, and a residual. Direct heritability estimates differed little across environments: 0.31 and 0.35 for weight in fall-and winter-born calves in the SoE, and 0.29 and 0.32 for winter- and spring-bom calves in NW. Maternal heritability estimates ranged from 0.12 in the NW to 0.16 the SoE. Genetic correlations spanned from 0.69 to 0.93 among direct effects and from 0.65 to 0.95 among maternal effects. All heritability estimates had small (0.01 to 0.04) SE. The most distinct environ-ments appeared to be winter in SoE and spring in NW (correlations of 0.69 and 0.65 for the direct and mater-nal effects). Different choices of sires for different environments might be justified to achieve the growth performance expected. [ABSTRACT FROM AUTHOR]

The objectives of this study were to determine if sires perform consistently across altitude and to quantify the genetic relationship between growth and survival at differing altitudes. Data from the American Angus Association included weaning weight (WW) adjusted to 205 (n = 77,771) and yearling weight adjusted to 365 (n = 39,450) d of age from 77,771 purebred Angus cattle born in Colorado between 1972 and 2007. Postweaning gain (PWG) was calculated by subtracting adjusted WW from adjusted yearling weight. Altitude was assigned to each record based upon the zip code of each herd in the database. Records for WW and PWG were each split into 2 traits measured at low and high altitude, with the records from medium altitude removed from the data due to inconsistencies between growth performance and apparent culling rate. A binary trait, survival (SV), was defined to account for censored records at yearling for each altitude. It was assumed that, at high altitude, individuals missing a yearling weight either died or required relocation to a lower altitude predominantly due to brisket disease, a condition common at high altitude. Model 1 considered each WW and PWG measured at 2 altitudes as separate traits. Model 2 treated PWG and SV measured as separate traits due to altitude. Models included the effects of weaning contemporary group, age of dam, animal additive genetic effects, and residual. Maternal genetic and maternal permanent environmental effects were included for WW. Heritability estimates for WW in Model 1 were 0.28 and 0.26 and for PWG were 0.26 and 0.19 with greater values in low altitude. Genetic correlations between growth traits measured at different altitude were moderate in magnitude: 0.74 for WW and 0.76 for PWG and indicate possibility of reranking of sires across altitude. Maternal genetic correlation between WW at varying altitude of 0.75 also indicates these may be different traits. In Model 2, heritabilities were 0.14 and 0.27 for PWG and 0.36 and 0.47 for SV. Genetic correlation between PWG measured at low and high altitude was 0.68. Favorable genetic correlations were estimated between SV and PWG within and between altitudes, suggesting that calves with genetics for increased growth from weaning to yearling also have increased genetic potential for SV. Genetic evaluations of PWG in different altitudes should consider preselection of the data, by using a censoring trait, like survivability to yearling. [ABSTRACT FROM AUTHOR]

Currently, the USDA uses a single-trait (ST) model with several intermediate steps to obtain genomic evaluations for US Holsteins. In this study, genomic evaluations for 18 linear type traits were obtained with a multiple-trait (MT) model using a unified single-step procedure. The phenotypic type data on up to 18 traits were available for 4,813,726 Holsteins, and single nucleotide polymorphism markers from the Illumina BovineSNP50 genotyping Beadchip (Illumina Inc., San Diego, CA) were available on 17,293 bulls. Genomic predictions were computed with several genomic relationship matrices (G) that assumed different allele frequencies: equal, base, current, and current scaled. Computations were carried out with ST and MT models. Procedures were compared by coefficients of determination (R²) and regression of 2004 prediction of bulls with no daughters in 2004 on daughter deviations of those bulls in 2009. Predictions for 2004 also included parent averages without the use of genomic information. The R² for parent averages ranged from 10 to 34% for ST models and from 12 to 35% for MT models. The average R² for all G were 34 and 37% for ST and MT models, respectively. All of the regression coefficients were <1.0, indicating that estimated breeding values in 2009 of 1,307 genotyped young bulls' parents tended to be biased. The average regression coefficients ranged from 0.74 to 0.79 and from 0.75 to 0.80 for ST and MT models, respectively. When the weight for the inverse of the numerator relationship matrix (A-1) for genotyped animals was reduced from 1 to 0.7, R² remained almost identical while the regression coefficients increased by 0.11-0.26 and 0.12-0.23 for ST and MT models, respectively. The ST models required about 5 s per iteration, whereas MT models required 3 (6) min per iteration for the regular (genomic) model. The MT single-step approach is feasible for 18 linear type traits in US Holstein cattle. Accuracy for genomic evaluation increases when switching ST models to MT models. Inflation of genomic evaluations for young bulls could be reduced by choosing a small weight for the A-1 for genotyped bulls. [ABSTRACT FROM AUTHOR]

Existence of individual variation in the onset of heat stress for daily milk yield of dairy cows was assessed. Data included 353,376 test-day records of 38,383 first-parity Holsteins from a random sample of US herds. Three hierarchical models were investigated. Model 1 inferred the value of a temperature-humidity index (THI) at which mean yield began to decline as well as the extent of that decline. Model 2 assumed individual variation in yield decline beyond a common THI threshold. Model 3 additionally assumed individual variation for the onset of heat stress. Deviance information criteria indicated the superiority of model 3 over model 2. For model 2, genetic correlation between milk yield in the absence of heat stress and the THI threshold for heat stress was -0.4 (0.11) [marginal posterior mean (marginal posterior standard deviation)]. For model 3, genetic correlations were -0.53 (0.05) between milk yield and THI threshold and -0.62 (0.08) between milk yield and yield decay beyond the THI threshold. Total standard deviation (sum of additive genetic and permanent environmental standard deviations) for the THI threshold was 3.95 (0.06), and more than half of that variation had an additive genetic origin [56% (5%)]. Because of the high genetic correlation [0.95 (0.03)] between yield decay and THI threshold with model 3, using only one of them as a selection criterion for heat tolerance would modify the other in the desired direction. [ABSTRACT FROM AUTHOR]

Heat stress was evaluated as a factor in differences between regional evaluations for milk yield in the United States. The national data set (NA) consisted of 56 million first-parity, test-day milk yields on 6 million Holsteins. The Northeastern subset (NE) included 12.5 million records on 1.3 million first-calved heifers from 8 states, and the Southeastern subset (SE) included 3.5 million records on 0.4 million heifers from 11 states. Climatic data were available from 202 public weather stations. Each herd was assigned to the nearest weather station. Average daily temperature-humidity index (meanTHI) 3 d before test date was used as an indicator of heat stress. Two test-day repeatability models were implemented. Effects included in both models were herd-test date, age at calving class, frequency of milking, days in milk x season class, additive genetic (regular breeding value) and permanent environmental effects. Additionally, the second model included random regressions on degrees of heat stress (t = max[0, meanTHI - 72]) for additive genetic (breeding value for heat tolerance) and permanent environmental effects. Both models were fitted with the national and regional data sets. Correlations involved estimated breeding values (EBV) from SE and NE for sires with ≥100 and ≥300 daughters in each region. When heat stress was ignored (first model) the correlations of regular EBV between SE and NE for sires with ≥100 (≥300) daughters were 0.85 (0.87). When heat stress was considered (second model), the correlation increased by up to 0.01. The correlations of heat stress EBV between NE and SE for sires with ≥100 (≥300, ≥700) daughters were 0.58 (0.72, 0.81). Evaluations for heat tolerance were similar in cooler and hotter regions for high-reliability sires. Heat stress as modeled explains only a small amount of regional differences, partly because test-day records depict only snapshots of heat stress. [ABSTRACT FROM AUTHOR]

The objectives of this study were to establish a pattern for the seasonality of days open (DO) by state and region within the United States and to present statistics on regional trends for DO. Data included 8,676,915 records on DO for Holsteins from 1997 to 2002 covering all regions of the United States. Fixed effects in the model included herd, parity, milk-class, state x month of calving (MOC), year of calving x MOC, and parity x MOC. Least squares means of DO were highest for calvings in March and lowest for calvings in September. The highest mean DO of 155 d was recorded in the Southeast, while the mean DO for the Midwest, Northeast, Northwest, and Southwest were 142, 141, 140, and 137 d, respectively. Variation in monthly averages of DO was highest in Southeast with a range of 51 d, and less than 25 d in all the other regions. Seasonality of calving was defined as the ratio of the fewest to the most calvings in months. The SOC was ≥60% in Southeast and ≤23% in the other regions. Selected states: Texas, Oklahoma, and Arizona in the Southwest and Missouri, Kansas, and Kentucky in the Midwest showed patterns of variation in monthly averages and seasonality of calving similar to those of Southeast. Distributions of DO were bimodal for some months of calving due to postponed breeding during the hot season or depressed fertility as a result of thermal stress; the second mode at >200 d was highest in the Southeast but also could be observed in Texas, Wisconsin, and California. High level of heat stress for DO exists in the Southeast and in selected states of the Midwest and the Southwest; these regions contribute less than 10% of national records. A methodology for analyzing DO especially under heat stress needs to consider effects of intentionally delayed breeding--by using a model that accounts for bimodality, for example. [ABSTRACT FROM AUTHOR]

Presents a study which aimed to estimate genetic parameters for a sire-maternal grandsire (MGS) model for use in the U.S. national genetic evaluation system based on current U.S. data, as of May 2003. Materials and methods; Results and discussion.

The purpose of this study was to quantify the effect of heat stress on genotype by environment interaction among different regions of the United States. The national data consisted of 55.5 million first parity test-day (TD) milk yield records on 5.8 million Holstein cows. Subsets of the national data were from the Northeast (NE) with 12.5 million TD records on 1.3 million cows, and from the Southeast (SE) with 3.5 million TD records on 0.36 million cows. Meteorological data from 202 public weather stations were matched with herds based on distance. Daily mean temperature-humidity index (THI) was calculated from temperature and relative humidity records three days prior to TD. The first model that lacked the effect of heat stress included fixed effects of herd-test date, age at calving class, frequency of milking, and DIM x season class, and random genetic additive (regular breeding value) and permanent environmental effects. The second model that accounted for heat stress included two additional random regressions on degrees of heat stress (t=max[0,THI-72]), one for additive genetic (heat breeding value) and one for permanent environmental effect. Breeding values (BV) were computed by BLUP90IOD. Correlations involved sires with at least 300 daughters in regions being compared. When heat stress was ignored, the correlations of regular BV between NE and SE were 0.86. When the heat stress was considered, the correlation increased by 0.01. The correlation between heat BV for NE x SE was 0.72. Heat stress effect as applied explains only a fraction of differences in sire ranking between SE and the NE. The real impact of heat stress may be higher because THI as used accounts only for a fraction of variability due to heat and low correlations are in part due to limited accuracies. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate genotype by environment interactions for culling rates and milk production in large and small dairy herds in 3 US regions, using genotypes, pedigree, and phenotypes. Single nucleotide polymorphism (SNP) marker variances were also estimated in these different environments. Culling rates including cow mortality were based on 6 Dairy Herd Improvement termination codes reported by dairy producers. Separate data sets for culling rates and 305-d milk yield were created for large and small dairy herds in the US regions of the Southeast (SE), Southwest (SW), and Northeast (NE) for the first 3 lactation cows that calved between 1999 and 2008. Genomic information from 42,503 SNP markers on 34,506 bulls was included in the analysis to predict genomic estimated breeding value (GEBV) of culling rates and 305-d milk yield with a single-step genomic BLUP using a bivariate threshold-linear model. Cow replacement rates in large SE and NE herds were higher. Heritability estimates of culling rates ranged from 0.03 to 0.11, but the differences were small between large and small herds and among the 3 US regions. Genetic correlations between culling rates and 305-d milk yield were medium to high for cows sold for poor production and reproduction problems. Correlations of GEBV for culling rates among the 3 US regions ranged from 0.34 to 0.92 and were lower between the SW and the other regions, especially in small herds. Correlations of GEBV between large and small herds ranged from 0.44 to 0.90 and were lower in the SW. These results indicate genotype by environment interactions of cow culling rate between the US regions and between large and small herds. Correlations of top 30 SNP marker effects for culling rates between 2 US regions ranged from 0.64 to 0.98 and were higher than those of more SNP marker effects except for a culling reason "sold for dairy purpose." Those correlations between large and small herds ranged from 0.67 to 0.98. High correlations of top SNP marker effects on culling reasons between the US regions and between large and small herds suggest that major markers can be useful for selection in different environments. The SNP variance shown in a marker gene segment on chromosome 14 was strongly associated with milk production in large and small herds in the NE but not in the SE and SW. Marker genes on chromosome 14 also showed a strong association with cow culling rates due to poor production and mortality in large herds in the NE. [ABSTRACT FROM AUTHOR]

A reaction norm approach was used to estimate trends for days open (DO) with a model that indirectly accounted for heat stress. Data included 3.4 million first-parity records of DO of US Holsteins. A fixed effect model included herd-year, month of calving within region (MOC), age class, and regression on 305-d milk yield. An index calculated from the standardized solutions to MOC derived from the fixed effect model was treated as a proxy for an index on heat stress (SI). The lowest index for any region was set to zero. The highest index was 1.00 for the Southeast, 0.56 for the Northeast, 0.54 for the Midwest, 0.33 for the Northwest, and 0.42 for the Southwest. In all regions except the Northwest, the highest DO and the corresponding highest indices were in March-April. Compared with the fixed model, the reaction norm model also included the effect of an animal and a random regression on the SI; the 2 animal solutions are subsequently referred to as an intercept and a slope. Genetic trends were calculated for cows and sires separately. For cows, the trend for the intercept was -0.1 d/yr, whereas the trend for the slope was 1 d/yr. For sires, the same trends were -0.3 and 1.5, respectively. Official proofs were used to characterize the 100 top and 100 bottom bulls with at least 50 daughters for the intercept and the slope. Compared with the top bulls, the bottom bulls for the intercept gave 56 kg more milk and their type performance index was higher by 212 points. For the slope, the same numbers were -435 kg and -242 points, respectively. Trends for seasonal changes of days open are unfavorable. [ABSTRACT FROM AUTHOR]

A method used to approximate reliabilities for animal models was modified to estimate reliabilities for sire-maternal grandsire (MGS) models. Accuracy of the approximation was tested on a calving-ease data set for 2,968 bulls for which the inverse of the coefficient matrix could be obtained. Correlations between estimated and true reliabilities ranged from 0.984 to 0.998 for first- and later-parity calving ease for sire and MGS effects. With no modification of the animal-model procedure, MGS identification was treated as if it were dam identification, which resulted in overestimated reliability. When pedigree information was ignored, reliability was underestimated. Correlations with true values were lower for both of those cases when compared with correct processing of MGS information. The modification provided a slight improvement over assuming MGS to be unknown and will be used for routine USDA evaluation of calving traits. [ABSTRACT FROM AUTHOR]

The objective of this study was to add a maternal grandsire (MGS) effect to the existing sire model for national calving ease genetic evaluations. The Animal Improvement Programs Laboratory (AIPL) of USDA assumed responsibility for conducting the national genetic evaluation for calving ease and maintaining the associated database in 1999. Existing evaluations used a sire threshold model. Adding an MGS effect to the model was expected to improve accuracy by partially accounting for merit of mates and differences in maternal ability of the dams. Dystocia data were migrated to a relational database integrated with the AIPL production database. This database design allowed more rigorous data edits by comparison with the production data and improved MGS identification (ID) rate by utilizing pedigrees from the production records. Integration of dystocia data with production data increased MGS ID rate from 58 to 73%. In addition, nearly 200,000 duplicate records were identified using the new edit system. Sire and sire-MGS models were compared using over 10 million observations available for the August 2002 national genetic evaluation. The sire model included herd-year, season, sex of calf, parity of dam, birth year group of sire, and sire. For the sire-MGS model, MGS and birth year group of MGS were added, year-seasons rather than seasons were used, and sex of calf and parity of dam were combined into a single interaction effect. Herd-year, sire, and MGS were random effects. Variance components used for the sire model were those previously used in the national evaluation and for the sire-MGS model were estimated in a separate study. Correlations between predicted genetic merits for service sire calving ease from the two models was 85%, indicating general agreement, but with some significant... [ABSTRACT FROM AUTHOR]

The objective of this study was to clarify how bias in genomic predictions is created by investigating a relationship among selection intensity, a change in heritability (Δ h 2), and assortative mating (ASM). A change in heritability, resulting from selection, reflects the impact that the Bulmer effect has on the reduction in between-family variation, whereas assortative mating impacts the within-family variance or Mendelian sampling variation. A partial data set up to 2014, including 841K genotyped animals, was used to calculate genomic predictions with a single-step genomic model for 18 linear type traits in US Holsteins. A full data set up to 2018, including 2.3 million genotyped animals, was used to calculate benchmark genomic predictions. Inbreeding and unknown parent groups for missing parents of animals were included in the model. Genomic evaluation was performed using 2 different genetic parameters: those estimated 14 yr ago, which have been used in the national genetic evaluation for linear type traits in the United States, and those newly estimated with recent records from 2015 to 2018 and those corresponding pedigrees. Genetic trends for 18 type traits were estimated for bulls with daughters and cows with phenotypes in 2018. Based on selection intensity and mating decisions, these traits can be categorized into 3 groups: (a) high directional selection, (b) moderate selection, and (c) intermediate optimum selection. The first 2 categories can be explained by positive assortative mating, and the last can be explained by negative assortative or disassortative mating. Genetic progress was defined by genetic gain per year based on average standardized genomic predictions for cows from 2000 to 2014. Traits with more genetic progress tended to have more "inflated" genomic predictions (i.e., "inflation" means here that genomic predictions are larger in absolute values than expected, whereas "deflation" means smaller than expected). Heritability estimates for 14 out of 18 traits declined in the last 16 yr, and Δ h 2 ranged from −0.09 to 0.04. Traits with a greater decline in heritability tended to have more deflated genomic predictions. Biases (inflation or deflation) in genomic predictions were not improved by using the latest genetic parameters, implying that bias in genomic predictions due to preselection was not substantial for a large-scale genomic evaluation. Moreover, the strong selection intensity was not fully responsible for bias in genomic predictions. The directional selection can decrease heritability; however, positive assortative mating, which was strongly associated with large genetic gains, could minimize the decline in heritability for a trait under strong selection and could affect bias in genomic predictions. [ABSTRACT FROM AUTHOR]

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Official multibreed genomic evaluations for dairy cattle in the United States are based on multibreed BLUP evaluation followed by single-breed estimation of SNP effects. Single-step genomic BLUP (ssGBLUP) allows the straight computation of genomic (G)EBV in a multibreed context. This work aimed to develop ssGBLUP multibreed genomic predictions for US dairy cattle using the algorithm for proven and young (APY) to compute the inverse of the genomic relationship matrix. Only purebred Ayrshire (AY), Brown Swiss (BS), Guernsey (GU), Holstein (HO), and Jersey (JE) animals were considered. A 3-trait model with milk (MY), fat (FY), and protein (PY) yields was applied using about 45 million phenotypes recorded from January 2000 to June 2020. The whole data set included about 29.5 million animals, of which almost 4 million were genotyped. All the effects in the model were breed specific, and breed was also considered as fixed unknown parent groups. Evaluations were done for (1) each single breed separately (single); (2) HO and JE together (HO&#95;JE); (3) AY, BS, and GU together (AY&#95;BS&#95;GU); (4) all the 5 breeds together (5&#95;BREEDS). Initially, 15k core animals were used in APY for AY&#95;BS&#95;GU and 5&#95;BREEDS, but larger core sets with more animals from the least represented breeds were also tested. The HO&#95;JE evaluation had a fixed set of 30k core animals, with an equal representation of the 2 breeds, whereas HO and JE single-breed analysis involved 15k core animals. Validation for cows was based on correlations between adjusted phenotypes and (G)EBV, whereas for bulls on the regression of daughter yield deviations on (G)EBV. Because breed was correctly considered in the model, BLUP results for single and multibreed analyses were the same. Under ssGBLUP, predictability and reliability for AY, BS, and GU were on average 7% and 2% lower in 5&#95;BREEDS compared with single-breed evaluations, respectively. However, validation parameters for these 3 breeds became better than in the single-breed evaluations when 45k animals were included in the core set for 5&#95;BREEDS. Evaluations for Holsteins were more stable across scenarios because of the greatest number of genotyped animals and amount of data. Combining AY, BS, and GU into one evaluation resulted in predictions similar to the ones from single breed, especially when using about 30k core animals in APY. The results showed that single-step large-scale multibreed evaluations are computationally feasible, but fine tuning is needed to avoid a reduction in reliability when numerically dominant breeds are combined. Having evaluations for AY, BS, and GU separated from HO and JE may reduce inflation of GEBV for the first 3 breeds., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objective of this study was to model differences in pedigree accuracy caused by selective genotyping. As genotypes are used to correct pedigree errors, some pedigree relationships are more accurate than others. These accuracy differences can be modeled with uncertain parentage models that distribute the paternal (maternal) contribution across multiple sires (dams). In our case, the parents were the parent on record and an unknown parent group to account for pedigree relationships that were not confirmed through genotypes. Pedigree accuracy was addressed through simulation and through North American Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach, assuming accurate pedigrees, and uncertain parentage, assuming variable pedigree accuracy. The uncertain parentage model was used to add pedigree relationships for alternative parents when pedigree relationships were not certain. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). An additional set of assumptions was tested in simulation to mimic an extensive dairy production system by using a sire probability of 0.75, a dam probability of 0.85, and the remainder attributed to the unknown parent groups. In the simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation (≥0.99) and in Holstein (≥0.99). For Holsteins, the estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Several research reports have indicated increasing dairy cow mortality in recent years. The objectives of this research were to characterize the phenotypic differences in mortality in the first 3 parities across 3 regions of the United States to estimate the heritability of mortality of Holstein cows across regions and parities, and to estimate genetic and environmental correlations between milk yield and mortality across parities and regions. Dairy Herd Information (DHI) milk yield and mortality data were obtained from 3 different US regions: the Southeast (SE), Southwest (SW), and Northeast (NE). A total of 3,522,824 records for the first 3 parities were used: 732,009 (SE), 656,768 (SW), and 2,134,047 (NE) from 1999 to 2008. Cows that received a termination code of 6--"Cow died on the dairy; downer cows that were euthanized should be included here"--were given a mortality score of 2 (dead), whereas all other codes were assigned a mortality score of 1 (alive). Average annual mortalities in the first 3 parities across regions ranged from 2.2 to 7.2%, with mortality frequency increasing with increasing parity across all regions and with the SE having the highest mortality frequency. For genetic analysis, a 2-trait (305-d milk yield and mortality) linear-threshold animal model that fitted fixed effects of herd-year (for 305-d milk yield), cow age, days in milk (in month classes), month-of-termination, and random effects of herd-year (for mortality), animal, and residual was implemented. The model was used to estimate variance components separately for each region and parity. Heritability estimates for mortality were similar for all regions and parities, ranging from 0.04 to 0.07. Genetic correlations between mortality and 305-d milk yield across the first 3 parities were 0.14, 0.20, and 0.29 in SE; -0.01, 0.01, and 0.31 in SW; and 0.28, 0.33, and 0.19 in NE. We detected an adverse genetic relationship between milk production and mortality; however, the moderate magnitudes of the genetic correlations suggest that indices that include both milk yield and mortality could be effective in identifying sires that would provide opportunities for minimizing death loss even when selecting for increased milk yield., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to assess breed, and breed x region interactions for several longevity-related traits, measured up to 5 yr of age in Brown Swiss, Holstein, and Jersey cows in 7 regions of the United States. Data were analyzed using logistic, poisson, and linear models, and survival analyses. The traits were stayability (yes/no survived to 5 yr of age), number of completed lactations, days lived, herd-life, and days in milk (DIM) to 5 yr of age. Probable lifetime DIM were also estimated using data from the first 5 yr of age of the cows. Herd-life was defined as the days lived up to 5 yr of age minus the age at first calving. Days in milk consisted of herd-life up to 5 yr of age minus the dry periods. Three data files were analyzed: herds with one breed of cows, herds with Holstein and Brown Swiss, and herds with Holstein and Jersey cows. Breed x region interaction was usually significant, with larger effects for the southern regions. Jerseys obtained largest values for the ratio of DIM to days lived, and for the number of completed lactations to 5 yr of age. Brown Swiss had the largest probabilities of surviving to 5 yr of age (stayabilities) in all regions. For the other traits, the results for Brown Swiss were inconsistent, but usually the cows of this breed had shorter herd-life and DIM to 5 yr of age than Holsteins. Brown Swiss cows were expected to have more total DIM in their lifetime in the Southeast than Holsteins. Survival analysis gave the most readily interpretable information, although the linear, poisson, and logistic analyses answered slightly different questions. Adjustment for herd size did not modify the results.

Our objective was to evaluate breed differences for heat-stress resistance as reflected by age at first calving and first calving interval. We examined the effect of geographic location and birth season on age at first calving, and geographic location and first calving season on first calving interval on Holsteins and Jerseys, and Holsteins and Brown Swiss located on the same farm. We defined 7 regions within the United States: Northwest, Central north, Northeast, Central, Central south, Southwest, and Southeast, and analyzed 7 individual states: Ohio, Wisconsin, Oregon, California, Arizona, Texas, and Florida. Brown Swiss were older than Holsteins at first calving (833 +/- 2.4 vs. 806 +/- 2.0 d in regions, and 830 +/- 3.1 vs. 803 +/- 2.4 d in states), but Holsteins and Brown Swiss did not differ for first calving interval. Jerseys were younger than Holsteins at first calving and had shorter first calving intervals. In data from individual states, Holsteins housed with Brown Swiss were older at first calving than were Holsteins housed with Jerseys (800 +/- 2.7 vs. 780 +/- 2.5 d). Holsteins housed with one breed or the other were analyzed as a separate data set, and referred to as "type of Holstein." The interaction of "type of Holstein" with first calving season was highly significant for first calving interval. Geographic location and season effects were smaller for Jerseys than for Holsteins; thus, Jerseys showed evidence of heat-stress resistance with respect to Holsteins. Management modified age at first calving in Holsteins to more nearly match that of the other breed. Longer calving intervals might be partly due to voluntary waiting period to breed the cows.

The influence of various editing criteria for days open (DO) records on genetic parameter estimates of DO and pregnancy rates (PR) in US Holsteins was investigated. Data included first parity 305-d milk yield and DO records from 8 states: Georgia (GA), Florida (FL), North Carolina (NC), Texas (TX), Arizona (AZ), California (CA), New York (NY), and Wisconsin (WI). The pregnancy rate was computed as 1/[(DO - VWP)/HI + 1)], where VWP was the approximate voluntary waiting period and HI was the heat interval set as 21 d. The upper limit for PR was set to 1.0. A bivariate animal model for DO (or PR) and 305-d milk yield was fit separately for each state. The model included fixed effects of herd-year, month of calving, and age of cow, as well as random animal and residual effects. In separate analyses, maximum DO records were limited to 150, 200, 250, 300, and 365 d. Analyses for PR used values of 50, 80, and 120 d for the VWP. Genetic and residual variances for DO were strongly dependent on the upper limit; both variances were 8 times larger as the upper bound increased from 150 to 365 d. Estimates of heritability for DO varied between 0.03 and 0.06. There was a 30% increase in the heritability estimate as the upper limit increased from 150 to 250 d for FL and NC, and small or no increases for the other states. The increase of the upper limit from 250 to 365 d resulted in little change. The genetic correlation between milk and DO was the highest for FL (0.6) and the lowest for GA (0.12 to 0.23). For PR with VWP=50, the heritability was higher than the corresponding estimate for DO in GA, equal to that in AZ, and lower in the remaining states. Heritabilities of PR also varied by the length of VWP; highest heritabilities were obtained at VWP=50 d for GA and AZ; at VWP=80 d for NY and WI; at VWP=120 d for FL, NC, and CA. Increase of genetic variation for records of DO < 250 d was small. Days open and PR are strongly influenced by differences in management protocols among states.

The objective of this study was to investigate the possibility of genotype x environment interactions for weaning weight (WWT) between different regions of the United States (US) and between Canada (CA), Uruguay (UY), and US for populations of Hereford cattle. Original data were composed of 487,661, 102,986, and 2,322,722 edited weaning weight records from CA, UY, and US, respectively. A total of 359 sires were identified as having progeny across all three countries; 240 of them had at least one progeny with a record in each environment. The data sets within each country were reduced by retaining records from herds with more than 500 WWT records, with an average contemporary group size of greater than nine animals, and that contained WWT records from progeny or maternal grand-progeny of the across-country sires. Data sets within each country were further reduced by randomly selecting among remaining herds. Four regions within US were defined: Upper Plains (UP), Cornbelt (CB), South (S), and Gulf Coast (GC). Similar sampling criteria and common international sires were used to form the within-US regional data sets. A pairwise analysis was done between countries and regions within US (UP-CB vs S-GC, UP vs CB, and S vs GC) for the estimation of (co)variance components and genetic correlation between environments. An accelerated EM-REML algorithm and a multiple-trait animal model that considered WWT as a different trait in each environment were used to estimate parameters in each pairwise analysis. Direct and maternal (in parentheses) estimated genetic correlations for CA vs UY, CA vs US, US vs UY, UP-CB vs S-GC, UP vs CB, and S vs GC were .88 (.84), .86 (.82), .90 (.85), .88 (.87), .88 (.84), and .87 (.85), respectively. The general absence of genotype x country interactions observed in this study, together with a prior study that showed the similarity of genetic and environmental parameters across the three countries, strongly indicates that a joint WWT genetic evaluation for Hereford cattle could be conducted using a model that treated the information from CA, UY, and US as a single population using single population-wide genetic parameters.

Records from the Hereford Associations of the United States (USA), Canada, and Uruguay were used to estimate genetic and phenotypic variances and covariances for weaning weight. Estimation was done using a complete animal model, relatively large data sets, and the same methodology for the three countries in order to determine whether genetic parameters for weaning weight were homogeneous across environments. Data were composed of 2,322,722, 487,661, and 102,986 edited weaning weight records for USA, Canada, and Uruguay, respectively. Ten samples were obtained from each country by eliminating data from small herds with fewer than 500 records, selecting herds at random from the entire data set after removing the small herds, and then retaining the direct-sire-connected contemporary groups within each sample. The final sample sizes ranged from 9,832 to 46,377 records. An accelerated EM-REML algorithm was used in estimating the (co)variance components in each sample. The estimates were pooled by calculating the arithmetic mean of the 10 samples from within each country. Direct and maternal (in parentheses) heritability estimates were .24 (.16), .20 (.16), and .23 (.18) for USA, Canada, and Uruguay, respectively. Maternal heritabilities reported here are nearly 50% smaller than the values currently used in national genetic evaluation for the breed, which were estimated using sire-maternal grandsire models. Covariance between direct and maternal was negative in all countries, accounting for 6, 8, and 10% of the total phenotypic variation, and the total dam effect was 32.5, 37.0, and 34.0% in USA, Canada, and Uruguay, respectively. Total heritabilities were similar among the countries, with values of .19, .19, and .17 for the three respective countries. The similarity of genetic and environmental parameters across the three countries suggests that joint genetic evaluation is feasible across environments provided that the genotype x environment interaction is negligible and can be ignored.

Differences in breeding values between dominance and additive models were examined theoretically and with field data. Data included 5.2 million records on stature from 3.0 million US Holsteins. The largest full-sib family had 29 animals, and 7% of all animals had at least one full sib. The dominance model, which accounted for dominance covariances, included the following effects: management, age, stage of lactation, permanent environment, animal additive, and parental dominance (one-quarter of dominance variance) as well as a regression coefficient for inbreeding percentage. Two reduced models were also assumed; in the first, the parental dominance effect was removed, and, in the second, the inbreeding regression coefficient was also removed. The correlations between breeding values in the three models were > 0.999, but breeding values of some animals from full-sib families changed > 5 standard deviations of parental dominance. The largest changes were observed for parents with large numbers of full-sib progeny, with limited information from parents, and without individual performance records. On average, the differences were up to four times larger for cows than for bulls and up to five times larger for dams than for sires. The greatest differences in breeding values between the dominance and the additive models were observed for dams with full-sib progeny, female full sibs, and low reliability bulls with full sibs in the extended family. Animals with large amounts of additive information as progeny-tested bulls were influenced little by the inclusion of dominance. Animals with a large proportion of information coming from animals with dominance relationships, such as cows originating via embryo transfer changed the most.

We used 179,485 Yorkshire reproductive and 239,354 Yorkshire growth records to estimate additive and dominance variances by Method Fraktur R. Estimates were obtained for number born alive (NBA), 21-d litter weight (LWT), days to 104.5 kg (DAYS), and backfat at 104.5 kg (BF). The single-trait models for NBA and LWT included the fixed effects of contemporary group and regression on inbreeding percentage and the random effects mate within contemporary group, animal permanent environment, animal additive, and parental dominance. The single-trait models for DAYS and BF included the fixed effects of contemporary group, sex, and regression on inbreeding percentage and the random effects litter of birth, dam permanent environment, animal additive, and parental dominance. Final estimates were obtained from six samples for each trait. Regression coefficients for 10% inbreeding were found to be -.23 for NBA, -.52 kg for LWT, 2.1 d for DAYS, and 0 mm for BF. Estimates of additive and dominance variances expressed as a percentage of phenotypic variances were, respectively, 8.8 +/- .5 and 2.2 +/- .7 for NBA, 8.1 +/- 1.1 and 6.3 +/- .9 for LWT, 33.2 +/- .4 and 10.3 +/- 1.5 for DAYS, and 43.6 +/- .9 and 4.8 +/- .7 for BF. The ratio of dominance to additive variances ranged from .78 to .11.

Two data sets from the USDA Livestock and Range Research Laboratory were analyzed to study dominance variance and the influence of dominance relationships. The first consisted of 4,155 birth weight (3,884 weaning weight) records of inbred USDA Line 1 Herefords. The second consisted of 8,065 birth weight (7,380 weaning weight) records from a line-cross experiment with five lines. Two models were used. Both included fixed effects of year-sex of calf and age of dam, and covariates for calving date, inbreeding of animal, and inbreeding of dam. For the second set, additional covariates were line composition and heterozygosity coefficients. Random effects were direct and maternal additive genetic, maternal permanent environment, sire-dam subclass, and residual. Model 1 considered sire-dam subclasses unrelated. Model 2 related sire-dam subclasses with a parental dominance relationship matrix. Variance components were estimated using REML. Differences between estimates with Model 1 and 2 were unimportant except for dominance variance. For the first data set, estimates with Model 2 of relative genetic direct and maternal variances, direct-maternal correlation, permanent environment, and dominance variances for birth weight were .35, .13, -.02, .03, and .25, respectively, and they were .39, .11, .04, .06 and .14 for the second data set. For weaning weight, the first data set estimates were .20, .15, -.37, .19, and .11, respectively, and they were .16, .20, -.07, .18, and .18 for the second data set. Changes, decreases and increases, in estimates of dominance variances may be due to increased information from relationships and family types other than full-sibs. The assumption of unrelated sire-dam subclasses might not be appropriate for estimation of dominance variance in populations with many dominance relationships among sire-dam classes.