Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

Background

The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.

Methods

The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.

Results

Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.     

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat–protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.     

Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle

Background

The major obstacles for the implementation of genomic selection in Australian beef cattle are the variety of breeds and in general, small numbers of genotyped and phenotyped individuals per breed. The Australian Beef Cooperative Research Center (Beef CRC) investigated these issues by deriving genomic prediction equations (PE) from a training set of animals that covers a range of breeds and crosses including Angus, Murray Grey, Shorthorn, Hereford, Brahman, Belmont Red, Santa Gertrudis and Tropical Composite. This paper presents accuracies of genomically estimated breeding values (GEBV) that were calculated from these PE in the commercial pure-breed beef cattle seed stock sector.

Methods

PE derived by the Beef CRC from multi-breed and pure-breed training populations were applied to genotyped Angus, Limousin and Brahman sires and young animals, but with no pure-breed Limousin in the training population. The accuracy of the resulting GEBV was assessed by their genetic correlation to their phenotypic target trait in a bi-variate REML approach that models GEBV as trait observations.

Results

Accuracies of most GEBV for Angus and Brahman were between 0.1 and 0.4, with accuracies for abattoir carcass traits generally greater than for live animal body composition traits and reproduction traits. Estimated accuracies greater than 0.5 were only observed for Brahman abattoir carcass traits and for Angus carcass rib fat. Averaged across traits within breeds, accuracies of GEBV were highest when PE from the pooled across-breed training population were used. However, for the Angus and Brahman breeds the difference in accuracy from using pure-breed PE was small. For the Limousin breed no reasonable results could be achieved for any trait.

Conclusion

Although accuracies were generally low compared to published accuracies estimated within breeds, they are in line with those derived in other multi-breed populations. Thus PE developed by the Beef CRC can contribute to the implementation of genomic selection in Australian beef cattle breeding.     

Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle

Background

In national evaluations, direct genomic breeding values can be considered as correlated traits to those for which phenotypes are available for traditional estimation of breeding values. For this purpose, estimates of the accuracy of direct genomic breeding values expressed as genetic correlations between traits and their respective direct genomic breeding values are required.

Methods

We derived direct genomic breeding values for 2239 registered Limousin and 2703 registered Simmental beef cattle genotyped with either the Illumina BovineSNP50 BeadChip or the Illumina BovineHD BeadChip. For the 264 Simmental animals that were genotyped with the BovineHD BeadChip, genotypes for markers present on the BovineSNP50 BeadChip were extracted. Deregressed estimated breeding values were used as observations in weighted analyses that estimated marker effects to derive direct genomic breeding values for each breed. For each breed, genotyped individuals were clustered into five groups using K-means clustering, with the aim of increasing within-group and decreasing between-group pedigree relationships. Cross-validation was performed five times for each breed, using four groups for training and the fifth group for validation. For each trait, we then applied a weighted bivariate analysis of the direct genomic breeding values of genotyped animals from all five validation sets and their corresponding deregressed estimated breeding values to estimate variance and covariance components.

Results

After minimizing relationships between training and validation groups, estimated genetic correlations between each trait and its direct genomic breeding values ranged from 0.39 to 0.76 in Limousin and from 0.29 to 0.65 in Simmental. The efficiency of selection based on direct genomic breeding values relative to selection based on parent average information ranged from 0.68 to 1.28 in genotyped Limousin and from 0.51 to 1.44 in genotyped Simmental animals. The efficiencies were higher for 323 non-genotyped young Simmental animals, born after January 2012, and ranged from 0.60 to 2.04.

Conclusions

Direct genomic breeding values show promise for routine use by Limousin and Simmental breeders to improve the accuracy of predicted genetic merit of their animals at a young age and increase response to selection. Benefits from selecting on direct genomic breeding values are greater for breeders who use natural mating sires in their herds than for those who use artificial insemination sires. Producers with unregistered commercial Limousin and Simmental cattle could also benefit from being able to identify genetically superior animals in their herds, an opportunity that has in the past been limited to seed stock animals.     

Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.     

Predictive ability of selected subsets of single nucleotide polymorphisms (SNPs) in a moderately sized dairy cattle population

Several studies have shown that computation of genomic estimated breeding values (GEBV) with accuracies significantly greater than parent average (PA) estimated breeding values (EBVs) requires genotyping of at least several thousand progeny-tested bulls. For all published analyses, GEBV computed from the selected samples of markers have lower or equal accuracy than GEBV derived on the basis of all valid single nucleotide polymorphisms (SNPs). In the current study, we report on four new methods for selection of markers. Milk, fat, protein, somatic cell score, fertility, persistency, herd life and the Israeli selection index were analyzed. The 972 Israeli Holstein bulls genotyped with EBV for milk production traits computed from daughter records in 2012 were assigned into a training set of 844 bulls with progeny test EBV in 2008, and a validation set of 128 young bulls. Numbers of bulls in the two sets varied slightly among the nonproduction traits. In EFF₁₂, SNPs were first selected for each trait based on the effects of each marker on the bulls’ 2012 EBV corrected for effective relationships, as determined by the SNP matrix. EFF₀₈ was the same as EFF₁₂, except that the SNPs were selected on the basis of the 2008 EBV. In DIF_max, the SNPs with the greatest differences in allelic frequency between the bulls in the training and validation sets were selected, whereas in DIF_min the SNPs with the smallest differences were selected. For all methods, the numbers of SNPs retained varied over the range of 300 to 6000. For each trait, except fertility, an optimum number of markers between 800 and 5000 was obtained for EFF₁₂, based on the correlation between the GEBV and current EBV of the validation bulls. For all traits, the difference between the correlation of GEBV and current EBV and the correlation of the PA and current EBV was >0.25. EFF₀₈ was inferior to EFF₁₂, and was generally no better than PA EBV. DIF_max always outperformed DIF_min and generally outperformed EFF₀₈ and PA. Furthermore, GEBV based on DIF_max were generally less biased than PA. It is likely that other methods of SNP selection could improve upon these results.     

Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes

The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a “bottleneck effect” and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b_(Yc,DGV)), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b_(Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile.     

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

This study evaluated the dependence of reliability and prediction bias on the prediction method, the contribution of including animals (bulls or cows), and the genetic relatedness, when including genotyped cows in the progeny-tested bull reference population. We performed genomic evaluation using a Japanese Holstein population, and assessed the accuracy of genomic enhanced breeding value (GEBV) for three production traits and 13 linear conformation traits. A total of 4564 animals for production traits and 4172 animals for conformation traits were genotyped using Illumina BovineSNP50 array. Single- and multi-step methods were compared for predicting GEBV in genotyped bull-only and genotyped bull-cow reference populations. No large differences in realized reliability and regression coefficient were found between the two reference populations; however, a slight difference was found between the two methods for production traits. The accuracy of GEBV determined by single-step method increased slightly when genotyped cows were included in the bull reference population, but decreased slightly by multi-step method. A validation study was used to evaluate the accuracy of GEBV when 800 additional genotyped bulls (POPbull) or cows (POPcow) were included in the base reference population composed of 2000 genotyped bulls. The realized reliabilities of POPbull were higher than those of POPcow for all traits. For the gain of realized reliability over the base reference population, the average ratios of POPbull gain to POPcow gain for production traits and conformation traits were 2.6 and 7.2, respectively, and the ratios depended on heritabilities of the traits. For regression coefficient, no large differences were found between the results for POPbull and POPcow. Another validation study was performed to investigate the effect of genetic relatedness between cows and bulls in the reference and test populations. The effect of genetic relationship among bulls in the reference population was also assessed. The results showed that it is important to account for relatedness among bulls in the reference population. Our studies indicate that the prediction method, the contribution ratio of including animals, and genetic relatedness could affect the prediction accuracy in genomic evaluation of Holstein cattle, when including genotyped cows in the reference population.     

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.Subject terms: Genetic markers, Animal breeding     

Genomic prediction of reproduction traits for Merino sheep

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross‐validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross‐validations. The accuracies of GEBVs from random cross‐validations (range 0.17–0.61) were higher than were those from sire family cross‐validations (range 0.00–0.51). The GEBV accuracies of 0.41–0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.     

Accuracy of estimated breeding values with genomic information on males,females, or both: an example on broiler chicken

Background

As more and more genotypes become available, accuracy of genomic evaluations can potentially increase. However, the impact of genotype data on accuracy depends on the structure of the genotyped cohort. For populations such as dairy cattle, the greatest benefit has come from genotyping sires with high accuracy, whereas the benefit due to adding genotypes from cows was smaller. In broiler chicken breeding programs, males have less progeny than dairy bulls, females have more progeny than dairy cows, and most production traits are recorded for both sexes. Consequently, genotyping both sexes in broiler chickens may be more advantageous than in dairy cattle.

Methods

We studied the contribution of genotypes from males and females using a real dataset with genotypes on 15 723 broiler chickens. Genomic evaluations used three training sets that included only males (4648), only females (8100), and both sexes (12 748). Realized accuracies of genomic estimated breeding values (GEBV) were used to evaluate the benefit of including genotypes for different training populations on genomic predictions of young genotyped chickens.

Results

Using genotypes on males, the average increase in accuracy of GEBV over pedigree-based EBV for males and females was 12 and 1 percentage points, respectively. Using female genotypes, this increase was 1 and 18 percentage points, respectively. Using genotypes of both sexes increased accuracies by 19 points for males and 20 points for females. For two traits with similar heritabilities and amounts of information, realized accuracies from cross-validation were lower for the trait that was under strong selection.

Conclusions

Overall, genotyping males and females improves predictions of all young genotyped chickens, regardless of sex. Therefore, when males and females both contribute to genetic progress of the population, genotyping both sexes may be the best option.     

Estimating myostatin gene effect on milk performance traits using estimated gene content for a large number of non-genotyped cows

The objective of this study was to estimate the myostatin (mh) gene's effect on milk, protein and fat yield in a large heterogeneous cow population, of which only a small portion was genotyped. For this purpose, a total of 13 992 889 test-day records derived from 799 778 cows were available. The mh gene effect was estimated via BLUP using a multi-lactation, multi-trait random regression test-day model with an additional fixed regression on mh gene content. As only 1416 animals, (of which 1183 cows had test-day records) were genotyped, more animals of additional breeds with assumed known genotype were added to estimate the genotype (gene content) of the remaining cows more reliably. This was carried out using the conventional pedigree information between genotyped animals and their non-genotyped relatives. Applying this rule, mean estimated gene content over all cows with test-day records was 0.104, showing that most cows were homozygous +/+. In contrast, when gene content estimation was only based on genotyped animals, mean estimated gene content over all cows with test-day records was with 1.349 overestimated. Therefore, the applied method for gene content estimation in large populations needs additional genotype assumptions about additional animals representing genetic diversity when the breed composition in the complete population is heterogeneous and only a few animals from predominantly one breed are genotyped. Concerning allele substitution effects for one copy of the 'mh' gene variant, significant decreases of -76.1 kg milk, -3.6 kg fat and -2.8 kg protein/lactation were obtained on average when gene content estimation was additionally based on animals with assumed known genotype. Based on this result, knowledge of the mh genotypes and their effects has the potential to improve milk performance traits in cattle.     

Accuracy of genomic prediction within and across populations for nematode resistance and body weight traits in sheep

Genomic prediction utilizes single nucleotide polymorphism (SNP) chip data to predict animal genetic merit. It has the advantage of potentially capturing the effects of the majority of loci that contribute to genetic variation in a trait, even when the effects of the individual loci are very small. To implement genomic prediction, marker effects are estimated with a training set, including individuals with marker genotypes and trait phenotypes; subsequently, genomic estimated breeding values (GEBV) for any genotyped individual in the population can be calculated using the estimated marker effects. In this study, we aimed to: (i) evaluate the potential of genomic prediction to predict GEBV for nematode resistance traits and BW in sheep, within and across populations; (ii) evaluate the accuracy of these predictions through within-population cross-validation; and (iii) explore the impact of population structure on the accuracy of prediction. Four data sets comprising 752 lambs from a Scottish Blackface population, 2371 from a Sarda×Lacaune backcross population, 1000 from a Martinik Black-Belly×Romane backcross population and 64 from a British Texel population were used in this study. Traits available for the analysis were faecal egg count for Nematodirus and Strongyles and BW at different ages or as average effect, depending on the population. Moreover, immunoglobulin A was also available for the Scottish Blackface population. Results show that GEBV had moderate to good within-population predictive accuracy, whereas across-population predictions had accuracies close to zero. This can be explained by our finding that in most cases the accuracy estimates were mostly because of additive genetic relatedness between animals, rather than linkage disequilibrium between SNP and quantitative trait loci. Therefore, our results suggest that genomic prediction for nematode resistance and BW may be of value in closely related animals, but that with the current SNP chip genomic predictions are unlikely to work across breeds.     

Genetic and phenotypic correlations among feed efficiency,production and selected conformation traits in dairy cows

The difficulties and costs of measuring individual feed intake in dairy cattle are the primary factors limiting the genetic study of feed intake and utilisation, and hence the potential of their subsequent industry-wide applications. However, indirect selection based on heritable, easily measurable, and genetically correlated traits, such as conformation traits, may be an alternative approach to improve feed efficiency. The aim of this study was to estimate genetic and phenotypic correlations among feed intake, production, and feed efficiency traits (particularly residual feed intake; RFI) with routinely recorded conformation traits. A total of 496 repeated records from 260 Holstein dairy cows in different lactations (260, 159 and 77 from first, second and third lactation, respectively) were considered in this study. Individual daily feed intake and monthly BW and body condition scores of these animals were recorded from 5 to 305 days in milk within each lactation from June 2007 to July 2013. Milk yield and composition data of all animals within each lactation were retrieved, and the first lactation conformation traits for primiparous animals were extracted from databases. Individual RFI over 301 days was estimated using linear regression of total 301 days actual energy intake on a total of 301 days estimated traits of metabolic BW, milk production energy requirement, and empty BW change. Pair-wise bivariate animal models were used to estimate genetic and phenotypic parameters among the studied traits. Estimated heritabilities of total intake and production traits ranged from 0.27±0.07 for lactation actual energy intake to 0.45±0.08 for average body condition score over 301 days of the lactation period. RFI showed a moderate heritability estimate (0.20±0.03) and non-significant phenotypic and genetic correlations with lactation 3.5 % fat-corrected milk and average BW over lactation. Among the conformation traits, dairy strength, stature, rear attachment width, chest width and pin width had significant (P<0.05) moderate to strong genetic correlations with RFI. Combinations of these conformation traits could be used as RFI indicators in the dairy genetic improvement programmes to increase the accuracy of the genetic evaluation of feed intake and utilisation included in the index.     

Comparison of joint versus purebred genomic evaluation in the French multi-breed dairy goat population

Background

All progeny-tested bucks from the two main French dairy goat breeds (Alpine and Saanen) were genotyped with the Illumina goat SNP50 BeadChip. The reference population consisted of 677 bucks and 148 selection candidates. With the two-step approach based on genomic best linear unbiased prediction (GBLUP), prediction accuracy of candidates did not outperform that of the parental average. We investigated a GBLUP method based on a single-step approach, with or without blending of the two breeds in the reference population.

Methods

Three models were used: (1) a multi-breed model, in which Alpine and Saanen breeds were considered as a single breed; (2) a within-breed model, with separate genomic evaluation per breed; and (3) a multiple-trait model, in which a trait in the Alpine was assumed to be correlated to the same trait in the Saanen breed, using three levels of between-breed genetic correlations (ρ): ρ = 0, ρ = 0.99, or estimated ρ. Quality of genomic predictions was assessed on progeny-tested bucks, by cross-validation of the Pearson correlation coefficients for validation accuracy and the regression coefficients of daughter yield deviations (DYD) on genomic breeding values (GEBV). Model-based estimates of average accuracy were calculated on the 148 candidates.

Results

The genetic correlations between Alpine and Saanen breeds were highest for udder type traits, ranging from 0.45 to 0.76. Pearson correlations with the single-step approach were higher than previously reported with a two-step approach. Correlations between GEBV and DYD were similar for the three models (within-breed, multi-breed and multiple traits). Regression coefficients of DYD on GEBV were greater with the within-breed model and multiple-trait model with ρ = 0.99 than with the other models. The single-step approach improved prediction accuracy of candidates from 22 to 37% for both breeds compared to the two-step method.

Conclusions

Using a single-step approach with GBLUP, prediction accuracy of candidates was greater than that based on parent average of official evaluations and accuracies obtained with a two-step approach. Except for regression coefficients of DYD on GEBV, there were no significant differences between the three models.     

Assessing the impact of natural service bulls and genotype by environment interactions on genetic gain and inbreeding in organic dairy cattle genomic breeding programs

The objective of the present study was to compare genetic gain and inbreeding coefficients of dairy cattle in organic breeding program designs by applying stochastic simulations. Evaluated breeding strategies were: (i) selecting bulls from conventional breeding programs, and taking into account genotype by environment (G×E) interactions, (ii) selecting genotyped bulls within the organic environment for artificial insemination (AI) programs and (iii) selecting genotyped natural service bulls within organic herds. The simulated conventional population comprised 148 800 cows from 2976 herds with an average herd size of 50 cows per herd, and 1200 cows were assigned to 60 organic herds. In a young bull program, selection criteria of young bulls in both production systems (conventional and organic) were either ‘conventional’ estimated breeding values (EBV) or genomic estimated breeding values (GEBV) for two traits with low (h²=0.05) and moderate heritability (h²=0.30). GEBV were calculated for different accuracies (r_mg), and G×E interactions were considered by modifying originally simulated true breeding values in the range from r_g=0.5 to 1.0. For both traits (h²=0.05 and 0.30) and r_mg⩾0.8, genomic selection of bulls directly in the organic population and using selected bulls via AI revealed higher genetic gain than selecting young bulls in the larger conventional population based on EBV; also without the existence of G×E interactions. Only for pronounced G×E interactions (r_g=0.5), and for highly accurate GEBV for natural service bulls (r_mg>0.9), results suggests the use of genotyped organic natural service bulls instead of implementing an AI program. Inbreeding coefficients of selected bulls and their offspring were generally lower when basing selection decisions for young bulls on GEBV compared with selection strategies based on pedigree indices.     

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.     

Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.     

Genetic parameters for production,health, fertility and longevity traits in dairy cows

Milk production, fertility, longevity and health records, were extracted from databases of two milk recording organisations in the United Kingdom for the first three lactations of the Holstein–Friesian breed. These included data related to health events (mastitis and lameness), voluntarily recorded on a proportion of farms. The data were analysed to calculate disease incidence levels and to estimate genetic parameters for health traits and their relationships with production and other functional traits. The resulting dataset consisted of 124 793 lactations from 75 137 animals of 1586 sires, recorded in 2434 herds. Incidence of health events increased with parity. The overall incidence of mastitis (MAS) and lameness (LAM), defined as binary traits, were 17% and 16%, respectively. Heritability estimates for MAS and LAM were 0.04 and 0.02, respectively, obtained from repeatability linear sire models. Heritability estimates of mastitis and lameness as count traits were slightly higher, 0.05 and 0.03, respectively. Genetic correlations were obtained by bivariate analyses of all pair-wise combinations between milk 305-day yield (MY), protein 305-day yield (PY), fat 305-day yield (FY), lactation average log_e transformed lactation average somatic cell count (SCS), calving interval (CI), days to first service (DFS), non-return at 56 days (NR56), number of inseminations (NINS), mastitis (MAS), number of mastitis episodes (NMAS), lameness (LAM), number of lameness episodes (NLAM) and lifespan score (LS). As expected, MAS was correlated most strongly with SCS (0.69), which supports the use of SCS as an indicator trait for mastitis. Genetic correlations between MAS and yield and fertility traits were of similar magnitude ranging from 0.27 to 0.33. Genetic correlations between MAS with LAM and LS were 0.38 and −0.59, respectively. Not all genetic correlations between LAM and other traits were significant because of fewer numbers of lameness records. LAM had significant genetic correlations with MY (0.38), PY (0.28), CI (0.35), NINS (0.38) and LS (−0.53). The heritability estimates of mastitis and lameness were low; therefore, genetic gain through direct selection alone would be slow, yet still positive and cumulative. Direct selection against mastitis and lameness as additional traits should reduce incidence of both diseases, and simultaneously improve fertility and longevity. However, both health traits had antagonistic relationships with production traits, thus genetic gain in production would be slower.     

Genetic structure of susceptibility traits for hip dysplasia and microsatellite informativeness of an outcrossed canine pedigree

An outcrossed canine pedigree was developed for quantitative trait locus (QTL) mapping of hip dysplasia by breeding dysplastic Labrador retrievers to trait-free greyhounds. Measured susceptibility traits included age at onset of femoral capital chondroepiphyseal ossification (OSS), maximum hip distraction (laxity) index (DI), and the dorsolateral subluxation (DLS) score. The pedigrees consisted of 147 dogs representing four generations. For 59 dogs genotyped with 65 microsatellites, the median heterozygosity and polymorphic information content (PIC) values of the F(1) generation were 0.82 and 0.68, respectively. Seventy-seven percent of microsatellites had a PIC greater than 0.59 in the F(1)s. Ninety-six percent of alleles showed Mendelian inheritance. Based on marker informativeness, approximately 350 randomly selected markers would be required for genome-wide screening to obtain an average interval between informative markers of 10 cM. Heritability was estimated as 0.43, 0.5, and 0.61 for OSS, DI, and the DLS score, respectively. Biometric estimates of the mean (+/- variance) effective number of segregating QTLs was 1.2 (+/- 0.05), 0.8 (+/- 0.02), and 1.0 (+/- 0.03) for OSS, DI, and the DLS score, respectively. The distributions of simulated backcross trait data suggested that the loci controlling these traits acted additively and that the DI may be controlled by a major locus. When combined with previous power and quantitative genetic analyses, these estimates indicate that this pedigree is informative for QTL mapping of hip dysplasia traits.