Search

The objective of this study was to investigate the feasibility of genomic evaluation for cow mortality and milk production using a single-step methodology. Genomic relationships between cow mortality and milk production were also analyzed. Data included 883,887 (866,700) first-parity, 733,904 (711,211) second-parity, and 516,256 (492,026) third-parity records on cow mortality (305-d milk yields) of Holsteins from Northeast states in the United States. The pedigree consisted of up to 1,690,481 animals including 34,481 bulls genotyped with 36,951 SNP markers. Analyses were conducted with a bivariate threshold-linear model for each parity separately. Genomic information was incorporated as a genomic relationship matrix in the single-step BLUP. Traditional and genomic estimated breeding values (GEBV) were obtained with Gibbs sampling using fixed variances, whereas reliabilities were calculated from variances of GEBV samples. Genomic EBV were then converted into single nucleotide polymorphism (SNP) marker effects. Those SNP effects were categorized according to values corresponding to 1 to 4 standard deviations. Moving averages and variances of SNP effects were calculated for windows of 30 adjacent SNP, and Manhattan plots were created for SNP variances with the same window size. Using Gibbs sampling, the reliability for genotyped bulls for cow mortality was 28 to 30% in EBV and 70 to 72% in GEBV. The reliability for genotyped bulls for 305-d milk yields was 53 to 65% to 81 to 85% in GEBV. Correlations of SNP effects between mortality and 305-d milk yields within categories were the highest with the largest SNP effects and reached >0.7 at 4 standard deviations. All SNP regions explained less than 0.6% of the genetic variance for both traits, except regions close to the DGAT1 gene, which explained up to 2.5% for cow mortality and 4% for 305-d milk yields. Reliability for GEBV with a moderate number of genotyped animals can be calculated by Gibbs samples. Genomic information can greatly increase the reliability of predictions not only for milk but also for mortality. The existence of a common region on Bos taurus autosome 14 affecting both traits may indicate a major gene with a pleiotropic effect on milk and mortality. [ABSTRACT FROM AUTHOR]

Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population ( PL) with >20 generations was simulated, and a small subpopulation (PS) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for PS; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from PL as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for PL; and (S4) single nucleotide polymorphism (SNP) effects calculated based on PL data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from PL did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for PS and PL was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from PL were used to predict GEBV for young genotyped animals from PS, accuracy was greatest (0.56). With 6,000 genotyped animal in PS, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population. [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]

The purpose of this study was to evaluate the accuracy of genomic selection in single-step genomic BLUP (ssGBLUP) when the inverse of the genomic relationship matrix (G) is derived by the "algorithm for proven and young animals" (APY). This algorithm implements genomic recursions on a subset of "proven" animals. Only a relationship matrix for animals treated as "proven" needs to be inverted, and the extra costs of adding animals treated as "young" are linear. Analyses involved 10,102,702 final scores on 6,930,618 Holstein cows. Final score, which is a composite of type traits, is popular trait in the United States and was easily available for this study. A total of 100,000 animals with genotypes were used in the analyses and included 23,000 sires (16,000 with >5 progeny), 27,000 cows, and 50,000 young animals. Genomic EBV (GEBV) were calculated with a regular inverse of G, and with the G inverse approximated by APY. Animals in the proven subset included only sires (23,000), sires + cows (50,000), only cows (27,000), or sires with >5 progeny (16,000). The correlations of GEBV with APY and regular GEBV for young genotyped animals were 0.994, 0.995, 0.992, and 0.992, respectively Later, animals in the proven subset were randomly sampled from all genotyped animals in sets of 2,000, 5,000, 10,000, 15,000, and 20,000; each sample was replicated 4 times. Respective correlations were 0.97 (5,000 sample), 0.98 (10,000 sample), and 0.99 (20,000 sample), with minimal difference between samples of the same size. Genomic EBV with APY were accurate when the number of animals used in the subset is between 10,000 and 20,000, with little difference between the ways of creating the subset. Due to the approximately linear cost of APY, ssGBLUP with APY could support any number of genotyped animals without affecting accuracy. [ABSTRACT FROM AUTHOR]

Data sets of US Holsteins, Israeli Holsteins, and pigs from PIC (a Genus company, Hendersonville, TN) were used to evaluate the effect of different numbers of generations on ability to predict genomic breeding values of young genotyped animals. The influence of including only 2 generations of ancestors (A2) or all ancestors (Af) was also investigated. A total of 34,506 US Holsteins, 1,305 Israeli Holsteins, and 5,236 pigs were genotyped. The evaluations were computed by traditional BLUP and single-step genomic BLUP, and computing performance was assessed for the latter method. For the 2 Holstein data sets, coefficients of determination (R2) and regression (δ) of deregressed evaluations from a full data set with records up to 2011 on estimated breeding values and genomic estimated breeding values from the truncated data sets were computed. The thresholds for data deletion were set by intervals of 5 yr, based on the average generation interval in dairy cattle. For the PIC data set, correlations between corrected phenotypes and estimated or genomic estimated breeding values were used to evaluate predictive ability on young animals born in 2010 and 2011. The reduced data set contained data up to 2009, and the thresholds were set based on an average generation interval of 3 yr. The number of generations that could be deleted without a reduction in accuracy depended on data structure and trait. For US Holsteins, removing 3 and 4 generations of data did not reduce accuracy of evaluations for final score in Af and A2 scenarios, respectively. For Israeli Holsteins, the accuracies for milk, fat, and protein yields were the highest when only phenotypes recorded in 2000 and later were included and full pedigrees were applied. Of the 135 Israeli bulls with genotypes (validation set) and daughter records only in the complete data set, 38 and 97 were sons of Israeli and foreign bulls, respectively. Although more phenotypic data increased the prediction accuracy for sons of Israeli bulls, the reverse was true for sons of foreign bulls. Also, more phenotypic data caused large inflation of genomic estimated breeding values for sons of foreign bulls, whereas the opposite was true with the deletion of all but the most recent phenotypic data. Results for protein and fat percentage were different from those for milk, fat, and protein yields; however, relatively, the changes in coefficients of determination and regression were smaller for percentage traits. For PIC data set, removing data from up to 5 generations did not erode predictive ability for genotyped animals for the 2 reproductive traits used in validation. Given the data used in this study, truncating old data reduces computation requirements but does not decrease the accuracy. For small populations that include local and imported animals, truncation may be beneficial for one group of animals and detrimental to another group. [ABSTRACT FROM AUTHOR]

Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R²) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R² and regression coefficients was observed from parities 1 through 3. Fat and protein yields had the lowest R for all the methods. On average, R² was lowest for parent averages, followed by GBLUP, BayesC, ssGBLUP, and WssGBLUP. For some traits, R² for direct genomic values from BayesC and GBLUP were lower than those for parent averages. Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations. Weighted ssGBLUP has the potential for higher evaluation accuracy. [ABSTRACT FROM AUTHOR]

Flavobacterium Columnare, the causative agent of columnaris disease (CD), is distributed around the world in fresh water sources, infecting freshwater finfish species. Recently, it has been identified as an emerging problem for the rainbow trout aquaculture industry in the U.S. Two live-attenuated vaccines have been commercialized, but their efficacy in rainbow trout is still not clear. The purpose of this study was to prospect genomic regions that explain large portion of the additive genetic variance of CD resistance in rainbow trout. Two important aquaculture populations were investigated: The National Center for Cool and Cold Water Aquaculture (NCCCWA) odd-year line, with resistance to bacterial cold water disease (BCWD), and the Troutlodge, Inc., May odd-year (TLUM) nucleus breeding population, which provided 54,350 and 36,265 pedigree records, in which 8,453 and 3,986 fish had CD resistance phenotype records, respectively. Fish that survived to 21 days post immersion challenge were recorded as resistant (phenotype = 2), and those that did not were rated as susceptible (phenotype = 1). Genotypes for 57k SNP (Affymetrix Axiom ®) were available for 1,185 and 1,137 fish from NCCCWA and TLUM, respectively. The SNP effects and variances were estimated using the weighted single-step genomic BLUP approach for genome-wide association (WssGBLUP), which uses pedigree, genotypes, and phenotypes from genotyped and ungenotyped animals. The weighting strategy accounted for 1 Mb moving SNP-windows. Genomic regions that explained more than 1% of the additive genetic variance were considered associated with CD resistance. A total of 13 windows located on six chromosomes were found to be associated with CD resistance in the NCCCWA population: two windows, located at 59 to 60 Mb and 61 to 62 Mb on chromosome Omy17, explained 12% and 11.33% of the genetic variance for CD resistance, respectively. In the TLUM population, a total of 16 windows located on nine chromosomes were detected. Only three similar windows (located on two chromosomes) were detected in both populations. The results suggest that CD resistance has an oligogenic architecture, and the SNP windows found to be associated with CD are not informative enough for selection decisions across populations. A moderate positive genetic correlation has been previously shown between CD and BCWD resistance in the NCCCWA population. One factor that might have contributed to detecting different QTL for CD resistance in the two populations is the five generations of selective breeding for BCWD resistance applied to the NCCCWA population in contrast to no selection pressure for disease resistance in the TLUM population. [ABSTRACT FROM AUTHOR]

The objective was to determine the effect of using core animals from different generations in single-step genomic BLUP with the Algorithm for Proven and Young (APY). Effective population size and number of independent chromosome segments (ICS) are limited in livestock populations, indicating limited dimensionality of genomic information. The APY takes advantage of this dimensionality and assumes that breeding vales (BV) for noncore animals are functions of the BV for core animals. The core animals represent the same information as the ICS. Simulations comprised a moderately heritable trait for 95,010 animals and 50,000 genotypes for animals across 5 generations. Genotypes consisted of 25,500 SNP distributed across 15 chromosomes. Core animals were defined based on individual generations, equal representation across generations, and at random. For a sufficiently large core size, core definitions had the same accuracies (r² = 0.90 ± 0.01) and biases (β1 = 1.02 ± 0.01) for young animals, even if the core animals had imperfect genotypes because of imputation. Using the youngest generations as core caused an increase in the number of rounds to convergence indicating some numerical instability with these core definitions. When 80% of genotyped animals had unknown parents, accuracy and bias were significantly better (P ≤ 0.05) for random and across-generation core definitions (r² = 0.71 ± 0.01; β = 0.75 ± 0.01) than for single-generation core definitions (r² = 0.61 ± 0.01; β = 0.53 ± 0.01). This difference could result from improved relationship estimates between animals in different generations because all generations were represented in the core partition that was directly inverted in APY. Thus, any subset of genotyped animals can be used to approximate the ICS when pedigrees are complete, but core animals should represent all generations when pedigrees are incomplete. [ABSTRACT FROM AUTHOR]

Methods for SNP selection can improve prediction accuracy over genomic BLUP, but in practice, the improvement is trait and population specific. This study investigates the importance of SNP selection in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with effective population sizes (Ne) of 20 or 100 and assuming that 10, 50, or 500 QTL were affecting a trait with heritability of 0.3. Pedigree information was available for 6 generations; phenotypes were recorded for the 4 middle generations. Animals from the last 3 generations were genotyped for 45,000 SNP. Single-step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV (GEBV). For WssGBLUP, 2 iterations of weights were calculated and were used to derive SNP variances and to construct a weighted genomic relationship matrix (G). Improved prediction accuracies are expected in WssGBLUP because more weight is placed on important SNP. Prediction accuracies were calculated for 1,000 genotyped animals in the last generation. Reference populations included 2,000, 5,000, and 25,000 genotyped animals. The latter genotyped set was used to assess the dimensionality of genomic information (number of effective SNP or effective chromosome segments - Me). This was calculated as the number of the largest eigenvalues explaining 98% of the variation in the genomic relationship matrix with and without the weights. For the data sets with Ne = 20 and 10 QTL, the accuracy gain from WssGBLUP was 12, 9, and 4 points for 2,000, 5,000, and 25,000 genotyped animals, respectively. With Ne = 100, this gain was 8, 10, and 7 points, respectively. For both an Ne of 20 and 100, the gain assuming 50 QTL was halved, and no gain was observed assuming 500 QTL. The number of effective SNP was about 4-fold less in weighted G (~1,512) than in unweighted G (~5,790), explaining the greater gain in accuracy with fewer genotyped animals. The impact of SNP selection decreases with increasing size of the reference population and number of QTL. In large populations, the detection of chromosome segments is more difficult, requiring more genotyped animals. [ABSTRACT FROM AUTHOR]

Nile Tilapia breeding programs have been focused on growth improvement by selecting for either body weight (BW) or daily weight gain (DWG). Along with growth traits, yield and quality traits are also of great importance in livestock breeding. Our objective was to evaluate the feasibility of including fillet yield and quality traits as selection criteria for improving performance in Nile tilapia. The fish used in this study came from a population of 3 generations undergoing selection (Aqua America Company, Brazil). Pedigree information was available for 5,263 fish. Phenotypes for body weight at 290 days (BW290) and daily weight gain (DWG) were measured on 2,585 males and females, fillet weight (FW) and fillet yield (FY) were measured on 1,198 males, and fillet fat content (FAT) was measured on 1,136 males. Variance components were estimated in single and two-trait models using GIBBS1f90, and the post-analyses were carried out using POSTGIBBSF90, both from the BLUPF90 family of programs. For all analyses, spawning was considered as a random common environmental effect; harvest weight, weight at tagging, and age were used as covariables for DWG and BW; body weight at slaughter was used as a covariable for FW, FY, and FAT; floating cage and sex were included as fixed effects for all traits and for BW290 and DWG, respectively. Heritability estimates for DWG, BW, and FW were close to 0.23, whereas FY had the highest heritability (0.32) and FAT had the lowest (0.20). Genetic correlations of DWG with FY and FAT were -0.09 and -0.4, respectively; BW290 with FY and FAT were -0.1 and -0.32, respectively. The only positive correlation was between FY and FAT (0.6). Negative correlations between growth and fillet traits indicate an increase in guts and carcass weight in bigger fish, which is not desirable. Based on the estimated heritabilities, the genetic improvement of fillet yield and quality traits can be effective and should be included in the selection criteria. These results are particularly important for FW and FY since there is a market interest on increasing fillet yield. In addition, fillet quality traits such as FAT are also of interest to enhance meat quality. [ABSTRACT FROM AUTHOR]

Assigning unknown parent groups (UPG) in mixed-model equations using single-step genomic BLUP was investigated to reduce bias and to increase accuracy in genomic estimated breeding values (GEBV). The original UPG were defined based on the animal's birth year and the sex of the animal's unknown parents. Combining the last 2 UPG for the animals' birth years and separating the UPG for US and non-US Holsteins were considered in the redefinition. A full data set in the 2011 national genetic evaluation of final score in US Holsteins was used to calculate estimated breeding values (EBV) for validation, and a subset of the 2011 data, which excluded phenotypes recorded after 2007, was used to calculate GEBV for all animals, including 34,500 genotyped bulls. The EBV and GEBV in 2007 were compared with EBV in the 2011 full data. The last group effects for unknown sires and dams were overestimated with the GEBV model using the reduced 2007 data. The genetic trends from EBV in 2011 and GEBV in 2007 with the original UPG in the last few years demonstrated inflation, whereas GEBV with the redefined UPG by combining the last 2 groups showed deflation. On the other hand, the redefined UPG by separating for US and non-US Holsteins reduced the bias in GEBV. Regression coefficients smaller than unity for GEBV for young genotyped bulls with no daughters in 2007 on progeny deviations in 2011 also indicated inflation. The redefining of UPG reduced bias and slightly increased accuracy in GEBV for both US and non-US genotyped bulls. Rank correlations between GEBV in 2007 and in 2011 with the redefined UPG were higher than those with no UPG and the original UPG, especially for non-US bulls. Redefining of UPG in genomic evaluation could improve reliability of GEBV and provide correct genetic trends. [ABSTRACT FROM AUTHOR]

Four performance-related traits [growth trait (GROW), feed efficiency trait 1 (FE1) and trait 2 (FE2), and dissection trait (DT)] and 4 categorical traits [mortality (MORT) and 3 disorder traits (DIS1, DIS2, and DIS3)] were analyzed using linear and threshold single- and multi-trait models. Field data included 186,596 records of commercial broilers from Cobb-Vantress, Inc. Average-information restricted maximum likelihood and Gibbs sampling-based methods were used to obtain estimates of the (co)variance components, heritabilities, and genetic correlations in a traditional approach using best linear unbiased prediction (BLUP). The ability to predict future breeding values (measured as realized accuracy) was checked in the last generation when traditional BLUP and single-step genomic BLUP were used. Heritability estimates for GROW, FE1, and FE2 in single- and multi-trait models were similar and moderate (0.22 to 0.26) but high for DT (0.48 to 0.50). For MORT, DIS1, and DIS2, heritabilities were 0.13, 0.24, and 0.34, respectively. Estimates from single- and multi-trait models were also very similar. However, heritability for DIS3 was higher from the single-trait threshold model than for the multi-trait linear-threshold model (0.29 vs. 0.19). Genetic correlations between growth traits and MORT were weak, except for maternal GROW, which had a moderate negative correlation (−0.50) with MORT. The genetic correlation between MORT and DIS1 was strong and positive (0.77). Feed efficiency 1, which was moderately heritable (0.25) and is highly selected for, was not genetically related to MORT of broilers and other disorders. Broiler MORT also had moderate heritability (0.13), which suggests that MORT and FE1 can be improved through selection without negatively impacting other important traits. Selection of heavier maternal GROW also may decrease offspring MORT. [ABSTRACT FROM AUTHOR]

The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with singlestep genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix (GAPY-1) based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up GAPY-1 for 569,404 genotyped animals with 10,000 core animals took 1.3 h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Singlestep genomic BLUP with APY is applicable to millions of genotyped animals. [ABSTRACT FROM AUTHOR]

High relatedness in the US Holstein breed can be attributed to the increased rate of inbreeding that resulted from strong selection and the extensive use of a few bulls via reproductive biotechnology. The objectives of this study were to determine whether clustering could separate selected candidates into genetically different groups and whether such clustering could reduce the expected inbreeding of the next generation. A genomic relationship matrix composed of 1,145 sires with the most registered progeny in the breed born after 1985 was used for principal component analysis and k-means clustering. The 5 clusters reduced the variance by 25% and contained 171 (C1), 252 (C2), 200 (C3), 244 (C4), and 278 (C5) animals, respectively. The 2 most predominant families were C1 and C2, while C4 contained the most international animals. On average, C1 and C5 contained older animals; the average birth year per cluster was 1988 (C1), 1996 (C2 and C3), 1999 (C4), and 1990 (C5). Increasing to 10 clusters allowed the separation of the predominant sons. Statistically significant differences were observed for indices (net merit index, cheese merit index, and fluid merit index), daughter pregnancy rate, and production traits (milk, fat, and protein), with older clusters having lower merit for production but higher for reproduction. K-means clustering was also used for 20,099 animals considered as selection candidates. Based on the reduction in variance achieved by clustering, 5 to 7 clusters were appropriate. The number of animals in each cluster was 3,577 (C1), 3,073 (C2), 3,302 (C3), 5,931 (C4), and 4,216 (C5). The expected inbreeding from within or across cluster mating was calculated using the complete pedigree, assuming the mean inbreeding of animals born in the same year when parents are unknown. Generally, inbreeding was highest within cluster mating and lowest across cluster mating. Even when 10 clusters were used, one cluster always gave low inbreeding in all scenarios. This suggests that this cluster contains animals that differ from all other groups but still contains enough diversity within itself. Based on lower across cluster inbreeding, up to 7 clusters were appropriate. Statistically significant differences in genomic estimated breeding values were found between clusters. The rankings of clusters for different traits were mostly the same except for reproduction and fat. Results show that diversity within the population exists and clustering of selection candidates can reduce the expected inbreeding of the next generations., (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/).)

Several types of multibreed genomic evaluation are in use. These include evaluation of crossbreds based on purebred SNP effects, joint evaluation of all purebreds and crossbreds with a single additive effect, and treating each purebred and crossbred group as a separate trait. Additionally, putative quantitative trait nucleotides can be exploited to increase the accuracy of prediction. Existing studies indicate that the prediction of crossbreds based on purebred data has low accuracy, that a joint evaluation can potentially provide accurate evaluations for crossbreds but could lower accuracy for purebreds compared with single-breed evaluations, and that the use of putative quantitative trait nucleotides only marginally increases the accuracy. One hypothesis is that genomic selection is based on estimation of clusters of independent chromosome segments. Subsequently, predicting a particular group type would require a reference population of the same type, and crosses with same breed percentage but different type (F 1 vs. F 2 ) would, at best, use separate reference populations. The genomic selection of multibreed population is still an active research topic., (© 2022.)

Over half a million Holsteins are being genotyped annually in the United States. The computational cost of including all genotypes in single-step genomic (ssG)BLUP is high, although it is feasible to conduct large-scale genomic prediction using an efficient algorithm such as APY (algorithm for proven and young). An effective method to further reduce the computing cost could be the use of indirect genomic predictions (IGP) for genotyped animals when they have neither progeny nor phenotypes. These young genotyped animals have no effect on the other genotyped animals and could have their genomic prediction done indirectly. The main objective of this study was to calculate IGP for various groups of genotyped animals and investigate the reduction in computing time as well as bias and accuracy of the IGP. We compared IGP with genomic (G)EBV for 18 linear type traits in US Holsteins, including 2.3 million (M) genotyped animals. The full data set consisted of 10.9M records for 18 linear type traits up to 2018 calving, 13.6M animals in the pedigree, and 2.3M animals genotyped for 79K SNP. For IGP, ssGBLUP included all genotyped animals except those with neither progeny nor phenotypes by year from 2014 to 2018 (i.e., the target animals). The SNP marker effects were computed based on GEBV for genotyped animals that had progeny, or phenotypes, or both. Further, IGP were calculated for target genotyped animals in each year group. For all genotyped animal groups from 2014 to 2018, the coefficients of determination (R 2 ) of a linear regression of GEBV on IGP were 0.960 for males and 0.954 for females for 18 traits on average. To reduce computing costs, the SNP marker effects were calculated based on GEBV from randomly selected genotyped animals from 15K to 60K. By randomly selecting a small number of genotyped animals, the computing time was dramatically reduced. As more genotyped animals were randomly selected to calculate SNP effects, R 2 was higher (more accurate) and the regression coefficient was lower (more inflated IGP). In a practical genomic evaluation in US Holsteins, to get sufficient contributions from GEBV, 25K to 35K is a rational number of genotyped animals that can be randomly selected to compute SNP effects and obtain accurate and unbiased IGP. Considering the computing time and both unbiasedness and accuracy of IGP, genomic evaluation can be conducted separately in GEBV for genotyped animals with phenotypes or progeny and in IGP for young genotyped animals. This can be a practical solution when conducting a large-scale genomic evaluation and would enable more frequent evaluation at lower cost, especially when many genotyped animals have neither phenotypes nor progeny., (© 2021.)

The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using single-step GBLUP under a scenario that includes purebred genotypes., (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-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

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 (Δh 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 Δh 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., (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-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The widespread use of sexed semen on US dairy cows and heifers has led to an excess of replacement heifers' calves, and the sale prices for those calves are much lower than in the past. Females not selected to produce the next generation of replacement heifers are increasingly being bred to beef bulls to produce crossbred calves for beef production. The purpose of this study was to investigate the use of beef service sires bred to dairy cows and heifers and to provide a tool for dairy producers to evaluate beef service sires' conception. Sire conception rate (SCR) is a phenotypic evaluation of service sire fertility that is routinely calculated for US dairy bulls. A total of 268,174 breedings were available, which included 36 recognized beef breeds and 7 dairy breeds. Most of the beef-on-dairy inseminations (95.4%) were to Angus (AN) bulls. Because of the limited number of records among other breeds, we restricted our final evaluations to AN service sires bred to Holstein (HO) cows. Service-sire inbreeding and expected inbreeding of resulting embryo were set to zero because pedigree data for AN bulls were unavailable. There were 233,379 breedings from 1,344 AN service sire to 163,919 HO cows. A mean (SD) conception rate of 33.8% (47.3%) was observed compared with 34.3% (47.5%) for breedings with HO sires mated to HO cows. Publishable AN bulls were required to have ≥100 total matings, ≥10 matings in the most recent 12 mo, and breedings in at least 5 herds. Mean SCR reliability was 64.5% for 116 publishable bulls, with a maximum reliability of 99% based on 25,217 breedings. Average SCR was near zero (on AN base) with a range of -5.1 to 4.4. Breedings to HO heifers were also examined, which included 19,437 breedings (443 AN service sire and 15,971 HO heifers). A mean (SD) conception rate of 53.0% (49.9%) was observed, compared with 55.3% (49.7%) for breedings with a HO sire mated to a HO heifer. Beef sires were used more frequently in cows known to be problem breeders, which explains some of the difference in conception rate. Mean service number was 1.92 and 2.87 for HO heifers and 2.13 and 3.04 for HO cows mated to HO and AN sires, respectively. Mating dairy cows and heifers to beef bulls may be profitable if calf prices are higher, fertility is improved, or if practices such as sexed semen, genomic testing, and improved cow productive life allow herd owners to produce both higher quality dairy replacement and increased income from market calves., (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-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)