Marker imputation with low-density marker panels in Dutch Holstein cattle

The availability of high-density bovine genotyping arrays made implementation of genomic selection possible in dairy cattle. Development of low-density single nucleotide polymorphism (SNP) panels will allow the extension of genomic selection to a larger portion of the population. Prediction of ungenotyped markers, called imputation, is a strategy that allows using the same low-density chips for all traits (and for different breeds). In the present study, we evaluated the accuracy of imputation with low-density genotyping arrays in the Dutch Holstein population. Five different sizes of genotyping arrays were tested, from 384 to 6,000 SNP. According to marker density, the overall allelic imputation error rate obtained with the program DAGPHASE, which relies on linkage disequilibrium and linkage, ranged from 11.7 to 2.0%, and that obtained with the program CHROMIBD, which relies on linkage and the set of all genotyped ancestors, ranged from 10.7 to 3.3%. However, imputation efficiency was influenced by the relationship between low-density and high-density genotyped animals. Animals with both parents genotyped had particularly low imputation error rates: <1% with 1,500 SNP or more. In summary, missing marker alleles can be predicted with 3 to 4% errors with approximately 1 SNP/Mb (approximately 3,000 markers). The CHROMIBD program proved more efficient than DAGPHASE only at lower marker densities or when several genotyped ancestors were available. Future studies are required to measure the effect of these imputation error rates on accuracy of genomic selection with low-density SNP panels.     

Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50,000-marker SNP panel. Data consisted of genotypes of 15,966 European Holstein bulls from the combined EuroGenomics reference population. Genotypes with the low-density chip were created by erasing markers from 50,000-marker data. The studies were performed in the Nordic countries (Denmark, Finland, and Sweden) using a BLUP model for prediction of DGV and in France using a genomic marker-assisted selection approach for prediction of GEBV. Imputation in both studies was done using a combination of the DAGPHASE 1.1 and Beagle 2.1.3 software. Traits considered were protein yield, fertility, somatic cell count, and udder depth. Imputation of missing markers and prediction of breeding values were performed using 2 different reference populations in each country: either a national reference population or a combined EuroGenomics reference population. Validation for accuracy of imputation and genomic prediction was done based on national test data. Mean imputation error rates when using national reference animals was 5.5 and 3.9% in the Nordic countries and France, respectively, whereas imputation based on the EuroGenomics reference data set gave mean error rates of 4.0 and 2.1%, respectively. Prediction of GEBV based on genotypes imputed with a national reference data set gave an absolute loss of 0.05 in mean reliability of GEBV in the French study, whereas a loss of 0.03 was obtained for reliability of DGV in the Nordic study. When genotypes were imputed using the EuroGenomics reference, a loss of 0.02 in mean reliability of GEBV was detected in the French study, and a loss of 0.06 was observed for the mean reliability of DGV in the Nordic study. Consequently, the reliability of DGV using the imputed SNP data was 0.38 based on national reference data, and 0.48 based on EuroGenomics reference data in the Nordic validation, and the reliability of GEBV using the imputed SNP data was 0.41 based on national reference data, and 0.44 based on EuroGenomics reference data in the French validation.     

Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle

The objective of the present study was to evaluate the predictive ability of direct genomic values for economically important dairy traits when genotypes at some single nucleotide polymorphism (SNP) loci were imputed rather than measured directly. Genotypic data consisted of 42,552 SNP genotypes for each of 1,762 Jersey sires. Phenotypic data consisted of predicted transmitting abilities (PTA) for milk yield, protein percentage, and daughter pregnancy rate from May 2006 for 1,446 sires in the training set and from April 2009 for 316 sires in the testing set. The SNP effects were estimated using the Bayesian least absolute selection and shrinkage operator (LASSO) method with data of sires in the training set, and direct genomic values (DGV) for sires in the testing set were computed by multiplying these estimates by corresponding genotype dosages for sires in the testing set. The mean correlation across traits between DGV (before progeny testing) and PTA (after progeny testing) for sires in the testing set was 70.6% when all 42,552 SNP genotypes were used. When genotypes for 93.1, 96.6, 98.3, or 99.1% of loci were masked and subsequently imputed in the testing set, mean correlations across traits between DGV and PTA were 68.5, 64.8, 54.8, or 43.5%, respectively. When genotypes were also masked and imputed for a random 50% of sires in the training set, mean correlations across traits between DGV and PTA were 65.7, 63.2, 53.9, or 49.5%, respectively. Results of this study indicate that if a suitable reference population with high-density genotypes is available, a low-density chip comprising 3,000 equally spaced SNP may provide approximately 95% of the predictive ability observed with the BovineSNP50 Beadchip (Illumina Inc., San Diego, CA) in Jersey cattle. However, if fewer than 1,500 SNP are genotyped, the accuracy of DGV may be limited by errors in the imputed genotypes of selection candidates.     

Reliability of genomic prediction for German Holsteins using imputed genotypes from low-density chips

With the availability of single nucleotide polymorphism (SNP) marker chips, such as the Illumina BovineSNP50 BeadChip (50K), genomic evaluation has been routinely implemented in dairy cattle breeding. However, for an average dairy producer, total costs associated with the 50K chip are still too high to have all the cows genotyped and genomically evaluated. To study the accuracy of cheaper low-density chips, genotypes were simulated for 2 low-density chips, the Illumina Bovine3K BeadChip (3K) and BovineLD BeadChip (6K), according to their original marker maps. Simulated missing genotypes of the 50K chip were imputed using the programs Beagle and Findhap. Three genotype data sets were used to study imputation accuracy: the EuroGenomics data set, with 14,405 reference bulls (data set I); the smaller EuroGenomics data set, with 11,670 older reference bulls (data set II); and the data set of all genotyped German Holsteins, with 31,597 reference animals (data set III). Imputed genotypes were compared with their original ones to calculate allele error rate for validation animals in the 3 data sets. To evaluate the loss in accuracy of genomic prediction when using imputed genotypes, a genomic evaluation was conducted only for EuroGenomics data set II. Furthermore, combined genome-enhanced breeding values calculated from the original and imputed genotypes were compared. Allele error rate for EuroGenomics data set II was highest for the Findhap program on the 3K chip (3.3%) and lowest for the Beagle program on the 6K chip (0.6%). Across the data sets, Beagle was shown to be about 2 times as accurate as Findhap. Compared with the real 50K genotypes, the reduction in reliability of the genomic prediction when using the imputed genotypes was highest for Findhap on the 3K chip (5.3%) and lowest for Beagle on the 6K chip (1%) when averaged over the 12 evaluated traits. Differences in genome-enhanced breeding values of the original and imputed genotypes were largest for Findhap on the 3K chip, whereas Beagle on the 6K chip had the smallest difference. The low-density chip, 6K, gave markedly higher imputation accuracy and more accurate genomic prediction than the 3K chip. On the basis of the relatively small reduction in accuracy of genomic prediction, we would recommend the BovineLD 6K chip for large-scale genotyping as long as its costs are acceptable to breeders.     

The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population

Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment.     

Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population

This study investigated the reliability of genomic estimated breeding values (GEBV) in the Danish Holstein population. The data in the analysis included 3,330 bulls with both published conventional EBV and single nucleotide polymorphism (SNP) markers. After data editing, 38,134 SNP markers were available. In the analysis, all SNP were fitted simultaneously as random effects in a Bayesian variable selection model, which allows heterogeneous variances for different SNP markers. The response variables were the official EBV. Direct GEBV were calculated as the sum of individual SNP effects. Initial analyses of 4 index traits were carried out to compare models with different intensities of shrinkage for SNP effects; that is, mixture prior distributions of scaling factors (standard deviation of SNP effects) assuming 5, 10, 20, or 50% of SNP having large effects and the others having very small or no effects, and a single prior distribution common for all SNP. It was found that, in general, the model with a common prior distribution of scaling factors had better predictive ability than any mixture prior models. Therefore, a common prior model was used to estimate SNP effects and breeding values for all 18 index traits. Reliability of GEBV was assessed by squared correlation between GEBV and conventional EBV (r²_{GEBV, EBV}), and expected reliability was obtained from prediction error variance using a 5-fold cross validation. Squared correlations between GEBV and published EBV (without any adjustment) ranged from 0.252 to 0.700, with an average of 0.418. Expected reliabilities ranged from 0.494 to 0.733, with an average of 0.546. Averaged over 18 traits, r²_{GEBV, EBV} was 0.13 higher and expected reliability was 0.26 higher than reliability of conventional parent average. The results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls compared with traditional selection based on parent average information.     

Accuracy of direct genomic values for functional traits in Brown Swiss cattle

In this study, direct genomic values for the functional traits general temperament, milking temperament, aggressiveness, rank order in herd, milking speed, udder depth, position of labia, and days to first heat in Brown Swiss dairy cattle were estimated based on ~777,000 (777K) single nucleotide polymorphism (SNP) information from 1,126 animals. Accuracy of direct genomic values was assessed by a 5-fold cross-validation with 10 replicates. Correlations between deregressed proofs and direct genomic values were 0.63 for general temperament, 0.73 for milking temperament, 0.69 for aggressiveness, 0.65 for rank order in herd, 0.69 for milking speed, 0.71 for udder depth, 0.66 for position of labia, and 0.74 for days to first heat. Using the information of ~54,000 (54K) SNP led to only marginal deviations in the observed accuracy. Trying to predict the 20% youngest bulls led to correlations of 0.55, 0.77, 0.73, 0.55, 0.64, 0.59, 0.67, and 0.77, respectively, for the traits listed above. Using a novel method to estimate the accuracy of a direct genomic value (defined as correlation between direct genomic value and true breeding value and accounting for the correlation between direct genomic values and conventional breeding values) revealed accuracies of 0.37, 0.20, 0.19, 0.27, 0.48, 0.45, 0.36, and 0.12, respectively, for the traits listed above. These values are much smaller but probably also more realistic than accuracies based on correlations, given the heritabilities and samples sizes in this study. Annotation of the largest estimated SNP effects revealed 2 candidate genes affecting the traits general temperament and days to first heat.     

Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle

Imputation of missing genotypes is important to join data from animals genotyped on different single nucleotide polymorphism (SNP) panels. Because of the evolution of available technologies, economical reasons, or coexistence of several products from competing organizations, animals might be genotyped for different SNP chips. Combined analysis of all the data increases accuracy of genomic selection or fine-mapping precision. In the present study, real data from 4,738 Dutch Holstein animals genotyped with custom-made 60K Illumina panels (Illumina, San Diego, CA) were used to mimic imputation of genotypes between 2 SNP panels of approximately 27,500 markers each and with 9,265 SNP markers in common. Imputation efficiency increased with number of reference animals (genotyped for both chips), when animals genotyped on a single chip were included in the training data, with regional higher marker densities, with greater distance to chromosome ends, and with a closer relationship between imputed and reference animals. With 0 to 2,000 animals genotyped for both chips, the mean imputation error rate ranged from 2.774 to 0.415% and accuracy ranged from 0.81 to 0.96. Then, imputation was applied in the Dutch Holstein population to predict alleles from markers of the Illumina Bovine SNP50 chip with markers from a custom-made 60K Illumina panel. A cross-validation study performed on 102 bulls indicated that the mean error rate per bull was approximately equal to 1.0%. This study showed the feasibility to impute markers in dairy cattle with the current marker panels and with error rates below 1%.     

Short communication: Imputation performances of 3 low-density marker panels in beef and dairy cattle

Low-density chips are appealing alternative tools contributing to the reduction of genotyping costs. Imputation enables researchers to predict missing genotypes to recreate the denser coverage of the standard 50K (～50,000) genotype. Two alternative in silico chips were defined in this study that included markers selected to optimize minor allele frequency and spacing. The objective of this study was to compare the imputation accuracy of these custom low-density chips with a commercially available 3K chip. Data consisted of genotypes of 4,037 Holstein bulls, 1,219 Montbéliarde bulls, and 991 Blonde d'Aquitaine bulls. Criteria to select markers to include in low-density marker panels are described. To mimic a low-density genotype, all markers except the markers present on the low-density panel were masked in the validation population. Imputation was performed using the Beagle software. Combining the directed acyclic graph obtained with Beagle with the PHASEBOOK algorithm provides fast and accurate imputation that is suitable for routine genomic evaluations based on imputed genotypes. Overall, 95 to 99% of alleles were correctly imputed depending on the breed and the low-density chip used. The alternative low-density chips gave better results than the commercially available 3K chip. A low-density chip with 6,000 markers is a valuable genotyping tool suitable for both dairy and beef breeds. Such a tool could be used for preselection of young animals or large-scale screening of the female population.     

Genetic parameters and genomic regions associated with horn fly resistance in organic Holstein cattle

Horn flies (Haematobia irritans [L.]) contribute to major economic losses of pastured cattle operations, particularly in organic herds because of limitations on control methods that can be used. The objectives of this research were to determine if resistance to horn flies is a heritable trait in organic Holstein cattle, determine associations with yield traits, and to detect genomic regions associated with fly infestation. Observations of fly load were recorded from 1,667 pastured Holstein cows, of which 640 were genotyped, on 13 organic dairies across the United States. Fly load score was determined using a 0 to 4 scale based on fly coverage from chine to loin on one side of the body, with 0 indicating few to no flies and 4 indicating high infestation. The scoring system was validated by counting flies from photographs taken at the time of scoring from 252 cows. To mitigate the effect of our data structure on potential selection bias effects on genetic parameter estimates, survival to subsequent lactations of scored animals and herd-mates that had been culled before the trial was accounted for as the trait stayability. Genetic parameters were estimated using single-step genomic analysis with 3-trait mixed models that included fly score, stayability, and a third phenotype. Model effects differed by variable, but fixed effects generally included a contemporary group, scorer, parity, and stage of lactation; random effects included animal, permanent environment, and residual error. A genome-wide association study was performed by decomposing estimated breeding values into marker effects to detect significant genomic regions associated with fly score. The rank correlation between the subjective fly score and the objective count was 0.79. The average heritability of fly score (± standard error) estimated across multiple models was 0.25 ± 0.04 when a known Holstein maternal grandsire was required and 0.19 ± 0.03 when only a known Holstein sire was required. Genetic correlation estimates with yield traits were moderately positive, but a greater fly load was associated with reduced yield after accounting for genetic merit. Lower fly loads were associated with white coat coloration; a significant genomic region on Bos taurus autosome 6 was identified that contains the gene KIT, which was the most plausible candidate gene for fly resistance because of its role in coat pattern and coloration. The magnitude of heritable variation in fly infestation is similar to other traits included in selection programs, suggesting that producers can select for resistance to horn flies.     

Correlations of feed intake predicted with milk infrared spectra and breeding values in the Dutch Holstein population

Feed is a major cost in dairy production, and substantial genetic variation in feed efficiency exists between cows. Therefore, breeders aim to improve feed efficiency of dairy cattle. However, phenotypic data on individual feed intake on commercial farms is scarce, and accurate measurements are very costly. Several studies have shown that information from Fourier-transformed infrared spectra of milk samples (milk infrared, milk IR) can be used to predict phenotypes such as energy balance and energy intake, but this is usually based on small data sets obtained under experimental circumstances. The added value of information from milk IR spectra for estimation of breeding values is unknown. The objectives of this study were (1) to develop prediction equations for dry matter intake (DMI) and residual DMI (rDMI) from milk IR spectra; (2) to apply these for a data set of milk IR spectra from commercial Dutch dairy farms; (3) to estimate genetic parameters for these traits; and (4) to estimate correlations between these predictions and other traits in the breeding goal. We used data from feeding trials where individual feed intake was recorded daily and for which milk IR spectra were determined weekly to develop prediction equations for DMI and rDMI with partial least squares regression. This data set contained over 7,600 weekly averaged DMI records linked with milk IR spectra from 271 cows. The equations were applied for a data set with test day information from 676 Dutch dairy herds with 621,567 records of 78,488 cows. Both milk IR-predicted DMI and rDMI were analyzed with an animal model to obtain genetic parameters and sire effect estimates that could be correlated with breeding values. A partial least squares regression model with 10 components from the milk IR spectra explained around 25% of DMI variation and less than 10% of rDMI variation in the validation set. Nearly all variation in the milk IR spectra was captured by 7 components; additional components contributed marginally to the spectral variation but decreased prediction errors for both traits. Accuracies of predictions of DMI and rDMI from milk IR spectra for a large feeding experiment were 0.47 and 0.26 on average, respectively, with small differences between ration treatments (ranging from 0.43 to 0.55 and from 0.21 to 0.34, respectively) and among lactation stages (ranging from 0.24 to 0.59 and from 0.13 to 0.36, respectively), with the highest prediction accuracies in early lactation. The estimated heritabilities for predicted DMI and rDMI were 0.3 and 0.4, respectively, which suggests genetic potential for both predicted traits. The correlations of sire estimates for milk IR-predicted DMI with official Dutch breeding values were strongest with milk production (0.33), longevity (0.26), and fertility (?0.27), indicating that cows that eat more produce more, live longer, and have poorer fertility. The correlations of sire estimates for predicted DMI and rDMI with the official breeding values for DMI were low (0.14 and 0.03, respectively). This implies that the added value of including milk IR-predicted DMI information in the estimation procedure of breeding values for DMI would be considered insufficient for practical application.     

Association between genomic daughter pregnancy rates and reproductive parameters in Holstein dairy cattle

The objective of this observational study was to evaluate the relationship between genomic daughter pregnancy rate (GDPR) with reproduction parameters such as pregnancy at first artificial insemination (AI), pregnancy per AI, and pregnancy losses (PL). A total of 12,949 events from 3,499 Holstein cows were included. Cows were enrolled as nulliparous (n = 1,220), primiparous (n = 1,314), or multiparous (n = 965). Cows were bred either after a timed AI protocol, timed embryo transfer (ET), or spontaneous estrus. Most lactating cows were bred following a timed AI protocol based on estradiol and progesterone, and most nulliparous were artificially inseminated following estrus detection. Hair samples were collected from the tail switch and cows were genotyped using a SNP platform (Clarifide, Zoetis). Cows that were bred by timed AI were evaluated for estrous behavior using tail chalk. Tail chalk was applied on the head of the tail 2 d before timed AI and the chalk was evaluated at AI (no estrus: 100% of chalk remaining or ≥50% of chalk remaining; Estrus: <50% of chalk remaining). Pregnancy diagnosis was performed at d 32 and 60 after AI using ultrasonography, and the presence of a heartbeat was considered a positive diagnosis. Pregnancy loss was defined as a pregnant cow on d 32 that was nonpregnant on d 60. As GDPR increased, the odds of pregnancy at first AI increased [odds ratio (OR) = 1.28, 95% CI = 1.20–1.35], the odds of pregnancy per AI increased (OR = 1.31, 95% CI = 1.25–1.36), and the odds of PL decreased (OR = 0.66, 95% CI = 0.60–0.72). Most cows that were bred on the day of the timed AI demonstrated estrus (n = 6,075; 92.9%). The odds of demonstrating estrus on the day of timed AI increased as GDPR increased (OR = 1.31, 95% CI = 1.17–1.48). There was no interaction between GDPR and parity or breeding management for pregnancy at first AI, pregnancy per AI, and PL. In conclusion, the odds of pregnancy at first AI and pregnancy per AI increased as GDPR increased. Moreover, the odds of PL increased as GDPR decreased. Greater GDPR was also associated with greater occurrence of estrus on the day of timed AI. These results suggest that selecting for higher GDPR could result in better reproductive performance, but this would need to be assessed with additional research.     

On the limited increase in validation reliability using high-density genotypes in genomic best linear unbiased prediction: Observations from Fleckvieh cattle

This study investigated reliability of genomic predictions using medium-density (40,089; 50K) or high-density (HD; 388,951) marker sets. We developed an approximate method to test differences in validation reliability for significance. Model-based reliability and the effect of HD genotypes on inflation of predictions were analyzed additionally. Genomic breeding values were predicted for at least 1,321 validation bulls based on phenotypes and genotypes of at least 5,324 calibration bulls by means of a linear model in milk, fat, and protein yield; somatic cell score; milkability; muscling; udder, feet, and legs score as well as stature. In total, 1,485 bulls were actually HD genotyped and HD genotypes of the other animals were imputed from 50K genotypes using FImpute software. Validation reliability was measured as the coefficient of determination of the weighted regression of daughter yield deviations on predicted breeding values divided by the reliability of daughter yield deviations and inflation was evaluated by the slope of this regression. Model-based reliability was calculated from the model. Distributions for validation reliability of 50K markers were derived by repeated sampling of 50,000-marker samples from HD to test differences in validation reliability statistically. Additionally, the benefit of HD genotypes in validation reliability was tested by repeated sampling of validation groups and calculation of the difference in validation reliability between HD and 50K genotypes for the sampled groups of bulls. The mean benefit in validation reliability of HD genotypes was 0.015 compared with real 50K genotypes and 0.028 compared with 50K samples from HD affected by imputation error and was significant for all traits. The model-based reliability was, on average, 0.036 lower and the regression coefficient was 0.036 closer to the expected value with HD genotypes. The observed gain in validation reliability with HD genotypes was similar to expectations based on the number of markers and the effective number of segregating chromosome segments. Sampling error in the marker-based relationship coefficients causing overestimation of the model-based reliability was smaller with HD genotypes. Inflation of the genomic predictions was reduced with HD genotypes, accordingly. Similar effects on model-based reliability and inflation, but not on the validation reliability, were obtained by shrinkage estimation of the realized relationship matrix from 50K genotypes.     

Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R²) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R² values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.     

Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle

The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach.     

A comparison of different algorithms for phasing haplotypes using Holstein cattle genotypes and pedigree data

Phasing genotypes to haplotypes is becoming increasingly important due to its applications in the study of diseases, population and evolutionary genetics, imputation, and so on. Several studies have focused on the development of computational methods that infer haplotype phase from population genotype data. The aim of this study was to compare phasing algorithms implemented in Beagle, Findhap, FImpute, Impute2, and ShapeIt2 software using 50k and 777k (HD) genotyping data. Six scenarios were considered: no-parents, sire-progeny pairs, sire-dam-progeny trios, each with and without pedigree information in Holstein cattle. Algorithms were compared with respect to their phasing accuracy and computational efficiency. In the studied population, Beagle and FImpute were more accurate than other phasing algorithms. Across scenarios, phasing accuracies for Beagle and FImpute were 99.49–99.90% and 99.44–99.99% for 50k, respectively, and 99.90–99.99% and 99.87–99.99% for HD, respectively. Generally, FImpute resulted in higher accuracy when genotypic information of at least one parent was available. In the absence of parental genotypes and pedigree information, Beagle and Impute2 (with double the default number of states) were slightly more accurate than FImpute. Findhap gave high phasing accuracy when parents’ genotypes and pedigree information were available. In terms of computing time, Findhap was the fastest algorithm followed by FImpute. FImpute was 30 to 131, 87 to 786, and 353 to 1,400 times faster across scenarios than Beagle, ShapeIt2, and Impute2, respectively. In summary, FImpute and Beagle were the most accurate phasing algorithms. Moreover, the low computational requirement of FImpute makes it an attractive algorithm for phasing genotypes of large livestock populations.     

Genetic parameters of direct and maternal effects for calving ease in Dutch Holstein-Friesian cattle

Genetic parameters of direct and maternal effects for calving ease in Dutch dairy cattle were estimated using 677,975 calving ease records from second calving. Particular emphasis was given to the presence and impact of environmental dam-offspring covariances on the estimated direct-maternal genetic correlation. Moreover, a measure of heritability for traits affected by maternal effects was developed. In contrast to previous parameters, this parameter reflects the amount of genetic variance that can be used to generate a response to selection in maternally affected traits. Estimated genetic correlations between direct and maternal effects on calving ease have often been moderately negative, particularly in beef cattle. Environmental dam-offspring covariances have been put forward as an explanation for such estimates. We investigated the impact of environmental dam-offspring covariances by fitting correlated residuals between dam and offspring records in the statistical model, and by comparing results of a sire-maternal grandsire model with those of an animal model. Results show that calving ease in Dutch dairy cattle has a direct heritability of approximately 0.08, a maternal heritability of approximately 0.04, a direct-maternal genetic correlation of approximately −0.20, and a total heritable variance equal to approximately 11% of phenotypic variance. Results of animal models and sire-maternal grandsire models were very similar. The direct-maternal environmental covariance was near zero, and consequently had very little impact on the estimated genetic parameters. Transformation of observations to a liability scale did not affect the estimated genetic parameters and yielded a nearly identical ranking of sires.     

Heifer fertility and its relationship with cow fertility and production traits in Holstein dairy cattle

Breeding receipts from three AI units were merged with Ontario Dairy Herd Improvement Corporation and Record of Performance production records. Data comprised 53,705 heifer, 41,253 lactation 1, 14,688 lactation 2, and 3054 lactation 3 records by daughters of 2150 sires represented in 15,877 herd-year-seasons of birth. Three measures of heifer fertility, three measures of cow fertility, and three measures of production were investigated. Measures of heifer fertility were ages at first and last breeding and number of inseminations per conception. Cow fertility traits were days from calving to first breeding, days open, and number of inseminations per conception. Production traits were breed class average milk, breed class average fat, and fat percentage. Relationships among these nine traits for the first three lactations were estimated using a maximum likelihood multiple-trait procedure. The linear mixed model for each trait included fixed effects of herd-year-season of birth and genetic groups of sire and the random effect of sire. Transformations of the data for nonnormality had no influence on the estimates of genetic and phenotypic parameters. The heritability of .12 for age at first insemination, which was higher than other heifer fertility traits, indicated that selection would result in genetic response. Genetic and phenotypic correlations between heifer fertility and cow fertility and production traits in all three lactations were not different from zero. There was no genetic antagonism between fertility and subsequent production traits.     

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUP_IM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUP_IM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.     

Reliability of direct genomic values for animals with different relationships within and to the reference population

Accuracy of genomic selection depends on the accuracy of prediction of single nucleotide polymorphism effects and the proportion of genetic variance explained by markers. Design of the reference population with respect to its family structure may influence the accuracy of genomic selection. The objective of this study was to investigate the effect of various relationship levels within the reference population and different level of relationship of evaluated animals to the reference population on the reliability of direct genomic breeding values (DGV). The DGV reliabilities, expressed as squared correlation between estimated and true breeding value, were calculated for evaluated animals at 3 heritability levels. To emulate a trait that is difficult or expensive to measure, such as methane emission, reference populations were kept small and consisted of females with own performance records. A population reflecting a dairy cattle population structure was simulated. Four chosen reference populations consisted of all females available in the first genotyped generation. They consisted of highly (HR), moderately (MR), or lowly (LR) related animals, by selecting paternal half-sib families of decreasing size, or consisted of randomly chosen animals (RND). Of those 4 reference populations, RND had the lowest average relationship. Three sets of evaluated animals were chosen from 3 consecutive generations of genotyped animals, starting from the same generation as the reference population. Reliabilities of DGV predictions were calculated deterministically using selection index theory. The randomly chosen reference population had the lowest average relationship within the reference population. Average reliabilities increased when average relationship within the reference population decreased and the highest average reliabilities were achieved for RND (e.g., from 0.53 in HR to 0.61 in RND for a heritability of 0.30). A higher relationship to the reference population resulted in higher reliability values. At the average squared relationship of evaluated animals to the reference population of 0.005, reliabilities were, on average, 0.49 (HR) and 0.63 (RND) for a heritability of 0.30; 0.20 (HR) and 0.27 (RND) for a heritability of 0.05; and 0.07 (HR) and 0.09 (RND) for a heritability of 0.01. Substantial decrease in the reliability was observed when the number of generations to the reference population increased [e.g., for heritability of 0.30, the decrease from evaluated set I (chosen from the same generation as the reference population) to II (one generation younger than the reference population) was 0.04 for HR, and 0.07 for RND]. In this study, the importance of the design of a reference population consisting of cows was shown and optimal designs of the reference population for genomic prediction were suggested.