The value of cows in reference populations for genomic selection of new functional traits

Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic prediction model to test the hypothesis that the value of cows in reference populations depends on the availability of phenotypic records. To test the hypothesis, we investigated different strategies of building a reference population for a new functional trait over a 10-year period. The trait was either recorded on a large scale (30 000 cows per year) or on a small scale (2000 cows per year). For large-scale recording, we compared four scenarios where the reference population consisted of 30 sires; 30 sires and 170 test bulls; 30 sires and 2000 cows; or 30 sires, 2000 cows and 170 test bulls in the first year with measurements of the new functional trait. In addition to varying the make-up of the reference population, we also varied the heritability of the trait (h2 = 0.05 v. 0.15). The results showed that a reference population of test bulls, cows and sires results in the highest accuracy of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new functional trait. The results showed that a reference population of cows results in the highest accuracy of the DGV whether the heritability is 0.05 or 0.15, because variation is lost when phenotypic data on cows are summarized in EBV of their sires. The main conclusions from this study are: (i) the fewer phenotypic records, the larger effect of including cows in the reference population; (ii) for small-scale recording, the accuracy of the DGV will continue to increase for several years, whereas the increases in the accuracy of the DGV quickly decrease with large-scale recording; (iii) it is possible to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls.     

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (r_TI) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of r_mg = 0.5. For a low heritability trait (h² = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles r_TI from 0.27 to 0.54. Including the conventional information source ‘own performance’ into the before mentioned index, additional SNP information increases r_TI by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.     

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.     

The effect of dry cow winter management system on feed intake, performance and estimated energy demand

This research compared three wood-chip out-wintering pad (OWP; an unsheltered OWP; a sheltered OWP (both with a concrete feed apron); and an unsheltered OWP with silage provided directly on top of the wood-chip bedding (self-feed OWP)) designs and cubicle housing with regard to dairy cow performance during the pre-partum period, and for 8 weeks post partum. Data were compared during 2 years. In Year 1, the unsheltered (space allowance = 12 m2 per cow) and sheltered (6 m2 per cow) OWPs were compared with cubicle housing (n = 49 cows per treatment). In Year 2, all three OWP designs (12 m2 per cow) were compared with cubicle housing (n = 24 cows per treatment, split into two replicates). Animals were dried off and assigned to treatment in the autumn, and remained there until calving in spring. Subsequently, they were managed at pasture during lactation. Outcome measures for analysis during the pre-partum period were feed intake, live weight, body condition score (BCS), heat production and heat loss, and post-partum were live weight, BCS, milk yield and milk composition. In Year 1, all cows had a similar live weight, but both pre-partum and at calving cows on the unsheltered OWP had a lower BCS than cows in cubicles (P < 0.05). However, in Year 2, there were no differences in either live weight or BCS. In Year 1, cows in the unsheltered OWP produced less heat than in cubicles (P < 0.05), but in Year 2, there was no treatment effect. In both years, cows in unsheltered OWPs lost more heat than cows in the sheltered OWP (P < 0.001). Treatment had no effect on milk composition either year. However, in Year 2, cows in the self-feed OWP had higher milk yields than the other treatments (P < 0.05). The lower BCS and heat production values in unsheltered treatments during Year 1 were probably because of higher rainfall and wind-speed values of that year. However, in both years, live weight in all treatments increased pre partum, and BCS did not decrease, indicating that unsheltered cows did not need to mobilise body reserves. Thus, OWPs could be a suitable pre-partum alternative to cubicle housing for dry dairy cows with regard to some aspects of dairy cow productive performance. However, further research should be carried out to investigate longer-term effects.     

Effect of cow reference group on validation reliability of genomic evaluation

We studied the effect of including genomic data for cows in the reference population of single-step evaluations. Deregressed individual cow genetic evaluations (DRP) from milk production evaluations of Nordic Red Dairy cattle were used to estimate the single-step breeding values. Validation reliability and bias of the evaluations were calculated with four data sets including different amount of DRP record information from genotyped cows in the reference population. The gain in reliability was from 2% to 4% units for the production traits, depending on the used DRP data and the amount of genomic data. Moreover, inclusion of genotyped bull dams and their genotyped daughters seemed to create some bias in the single-step evaluation. Still, genotyping cows and their inclusion in the reference population is advantageous and should be encouraged.     

Predicted accuracy of and response to genomic selection for new traits in dairy cattle

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.     

Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F₂ population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F₂ population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q₂) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F₂ chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2^nd to 10^th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F₂ chickens when compared with conventional methods.     

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.     

Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle

Accuracy of predicting genomic breeding values for carcass merit traits including hot carcass weight, longissimus muscle area (REA), carcass average backfat thickness (AFAT), lean meat yield (LMY) and carcass marbling score (CMAR) was evaluated based on 543 Angus and 400 Charolais steers genotyped on the Illumina BovineSNP50 Beadchip. For the genomic prediction within Angus, the average accuracy was 0.35 with a range from 0.32 (LMY) to 0.37 (CMAR) across different training/validation data‐splitting strategies and statistical methods. The within‐breed genomic prediction for Charolais yielded an average accuracy of 0.36 with a range from 0.24 (REA) to 0.46 (AFAT). The across‐breed prediction had the lowest accuracy, which was on average near zero. When the data from the two breeds were combined to predict the breeding values of either breed, the prediction accuracy averaged 0.35 for Angus with a range from 0.33 (REA) to 0.39 (CMAR) and averaged 0.33 for Charolais with a range from 0.18 (REA) to 0.46 (AFAT). The prediction accuracy was slightly higher on average when the data were split by animal's birth year than when the data were split by sire family. These results demonstrate that the genetic relationship or relatedness of selection candidates with the training population has a great impact on the accuracy of predicting genomic breeding values under the density of the marker panel used in this study.     

Effect of widespread and limited use of sexed semen on genetic progress and reproductive performance of dairy cows

Stochastic simulation was used for studying the impacts of sexed semen on genetic progress and reproductive performance of dairy cows. Three strategies were compared: WSS (use unsexed semen in cows and heifers), SSH (use sexed semen in heifers and unsexed semen in cows) and SSCH (use sexed semen in both cows and heifers). Conception rate (CR) of unsexed semen was considered to be 35% and 65% in cows and heifers, respectively. CR of sexed semen was considered to be 15 (20% in cows and 50% in heifers), 10, 5 and 0 percentage points lower than unsexed semen. Thus, four subschemes were compared under SSCH (SSCH15, SSCH10, SSCH5, SSCH0) and SSH (SSH15, SSH10, SSH5, SSH0). Moreover, the effect was studied in four distinct paths of selection: active sires (AS), young bulls (YB), bull dams (BD) and milking cows (CW). The average genetic superiority of CW was 12% and 9.5% in SSCH15 and SSH15 strategies relative to a base scheme, respectively. The average genetic superiority of CW was 19% and 10.5% in SSCH0 and SSH0, respectively. Regression analysis showed that genetic superiority of CW increased significantly, that is, 0.5% and 0.1% per every 1% increase in CR in SSCH and SSH, respectively. The result showed that there is a significant difference between genetic superiority of cows in SSCH and SSH schemes. Widespread and limited use of sexed semen in commercial dairy herds resulted in a large genetic advantage in CW. The genetic advantage of gender control was minimal in the selection paths of AS, YB and BD. Open days and services per conception reached to 153 v. 125 days and 5 v. 2.86 under SSCH15 compared with WSS. The age at first calving increased from 774 to 790 days in SSH15 and SSCH15 strategies. Mean of parities decreased to 2.26 v. 2.42 by using sexed semen. The widespread use of sexed semen increased the age average of cows in all parities. Sexed semen increased selection intensity in the CW path, and this contributed to the genetic merit of future cows. On the other hand, sexed semen had a negative effect on the reproductive performance of dairy cows. Generally, although the effect of widespread use of sexed semen (SSCH) on genetic progress is significantly more than limited use of sexed semen (SSH), SSCH decreased reproductive performance of dairy herds dramatically, and this suggests that SSH scenarios might be more appropriate in animal breeding programs. Finally, to make a decision of which schemes are more convenient, it is necessary to compare the economic aspects of schemes.     

Evaluation of the potential use of a meta-population for genomic selection in autochthonous beef cattle populations

This study investigated the potential application of genomic selection under a multi-breed scheme in the Spanish autochthonous beef cattle populations using a simulation study that replicates the structure of linkage disequilibrium obtained from a sample of 25 triplets of sire/dam/offspring per population and using the BovineHD Beadchip. Purebred and combined reference sets were used for the genomic evaluation and several scenarios of different genetic architecture of the trait were investigated. The single-breed evaluations yielded the highest within-breed accuracies. Across breed accuracies were found low but positive on average confirming the genetic connectedness between the populations. If the same genotyping effort is split in several populations, the accuracies were lower when compared with single-breed evaluation, but showed a small advantage over small-sized purebred reference sets over the accuracies of subsequent generations. Besides, the genetic architecture of the trait did not show any relevant effect on the accuracy with the exception of rare variants, which yielded slightly lower results and higher loss of predictive ability over the generations.     

A stochastic simulation study on using different models for prediction of breeding values while changing the breeding goal

In a stochastic simulation study the effect of simultaneously changing the model for prediction of breeding values and changing the breeding goal was studied. A population of 100 000 cows with registrations on seven traits was simulated in two steps. In the first step of 15 years the population was selected for production and mastitis occurrence using a univariate model for prediction of breeding values for production and a trivariate model using information on mastitis treatments, udder depth and somatic cell score for prediction of breeding values for mastitis occurrence. In the second step six different scenarios were set up and simulated for 15 years combining two different breeding goals and three different models for prediction of breeding values in 20 replicates. Breeding goal 1 had relative economic value per genetic standard deviation on production (19.4) and mastitis occurrence ( − 50) whereas breeding goal 2 had a economic value on production (19.4), udder depth (4.2), mastitis occurrence ( − 50), non return rate (13.0) and days open ( − 16.75). Model 1 was a model similar to the one used in the first 15 years. Model 2 was an approximate multitrait model where solutions for fixed effects from a model corresponding to model 1 were subtracted from the phenotypes and a multitrait model with an overall mean, a year effect, an additive genetic and a residual effect were applied. Model 3 was a full multitrait model. Average genetic trends for total merit and each individual trait over 20 replicates were compared for each scenario. With the number of replicates the genetic responses using model 2 and 3 were not significant different. With a broad breeding goal using, model 2 or model 3 gave a significantly higher response in total merit than using model 1. Using a narrow breeding goal there was no significant difference between models used for prediction of breeding values. Results showed that with a breeding goal with a lot of emphasis on low heritable traits with a high economic value using a multitrait methodology for prediction of breeding values will redistribute the genetic progress in the total merit index. More gain will come from the low heritable traits in the breeding goal and less from traits with higher heritability. With a broad breeding goal and exploiting the available information in the data the inbreeding coefficient increased though not significantly.     

Effect of grazing on the cow welfare of dairy herds evaluated by a multidimensional welfare index

Structural development in the prime sector has led to increasing herd sizes and new barn systems, followed by less summer grazing for dairy cows in Denmark. Effects of grazing on single welfare measures in dairy cows – for example, the presence of integument alterations or mortality – have been studied under different conditions. However, the effect of grazing on welfare, conceptualised as the multidimensional physical and mental state of the animal, has not yet been studied in contemporary cubicle loose-housing systems. The aim of our study was to investigate, based on a Welfare Quality® inspired multidimensional dairy cow welfare assessment protocol, the within-herd effect of summer grazing compared with winter barn housing in Danish dairy herds with cubicle free-stall systems for the lactating cows. Our hypothesis was that cow welfare in dairy herds was better during summer grazing than during full-time winter housing. Furthermore, we expected improved welfare with an increase in daily summer grazing hours. In total, 41 herds have been visited once in the winter and once in the summer of 2010 to assess their welfare status with 17 different animal- and resource-based welfare measures. A panel of 20 experts on cattle welfare and husbandry evaluated the relative weight of the 17 welfare measures in a multidimensional assessment scheme. They estimated exact weights for a priori constituted severe compared with moderate scores of welfare impairment concerning each measure, as well as relevance of the measures in relation to each other. A welfare index (WI; possible range 0 to 5400) was calculated for each herd and season with a higher index indicating poorer welfare. The within-herd comparison of summer grazing v. winter housing considered all the 17 measures. The mean WI in summer was significantly lower (better) than in winter (mean 2926 v. 3330; paired t-test P = 0.0001) based on a better state of the integument, claw conformation and better access to water and food. Body condition and faeces consistence were worse in summer. Many daily grazing hours (range average above 3 to 9 h) turned out to be more beneficial than few daily grazing hours (range average above 9 to 21 h) for the welfare of the dairy herds. In conclusion, this study reports a positive within-herd effect of summer grazing on dairy cow welfare, where many daily grazing hours were more beneficial than few daily grazing hours.     

Comparison of breeding value prediction for two traits in a Nellore-Angus crossbred population using different Bayesian modeling methodologies

The objectives of this study were to 1) compare four models for breeding value prediction using genomic or pedigree information and 2) evaluate the impact of fixed effects that account for family structure. Comparisons were made in a Nellore-Angus population comprising F₂, F₃ and half-siblings to embryo transfer F₂ calves with records for overall temperament at weaning (TEMP; n = 769) and Warner-Bratzler shear force (WBSF; n = 387). After quality control, there were 34,913 whole genome SNP markers remaining. Bayesian methods employed were BayesB ($\tilde{\pi }$ = 0.995 or 0.997 for WBSF or TEMP, respectively) and BayesC (π = 0 and $\tilde{\pi }$), where $\tilde{\pi }$ is the ideal proportion of markers not included. Direct genomic values (DGV) from single trait Bayesian analyses were compared to conventional pedigree-based animal model breeding values. Numerically, BayesC procedures (using $\tilde{\pi }$) had the highest accuracy of all models for WBSF and TEMP ($\widehat{\rho }$_gĝ = 0.843 and 0.923, respectively), but BayesB had the least bias (regression of performance on prediction closest to 1, $\widehat{\beta }$_y,x = 2.886 and 1.755, respectively). Accounting for family structure decreased accuracy and increased bias in prediction of DGV indicating a detrimental impact when used in these prediction methods that simultaneously fit many markers.     

Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population

Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic. However, the expected higher accuracy of multi-population genomic prediction has not been realized. This may be due to an inconsistent linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) across populations, and the weak genetic relationships across populations. In this study, we determined the impact of different genomic relationship matrices, SNP density and pre-selected variants on prediction accuracy using a combined Yorkshire pig population. Our objective was to provide useful strategies for improving the accuracy of genomic prediction within a combined population. Results showed that the accuracy of genomic best linear unbiased prediction (GBLUP) using imputed whole-genome sequencing (WGS) data in the combined population was always higher than that within populations. Furthermore, the use of imputed WGS data always resulted in a higher accuracy of GBLUP than the use of 80K chip data for the combined population. Additionally, the accuracy of GBLUP with a non-linear genomic relationship matrix was markedly increased (0.87% to 15.17% for 80K chip data, and 0.43% to 4.01% for imputed WGS data) compared with that obtained with a linear genomic relationship matrix, except for the prediction of XD population in the combined population using imputed WGS data. More importantly, the application of pre-selected variants based on fixation index (Fst) scores improved the accuracy of multi-population genomic prediction, especially for 80K chip data. For BLUP|GA (BLUP approach given the genetic architecture), the use of a linear method with an appropriate weight to build a weight-relatedness matrix led to a higher prediction accuracy compared with the use of only pre-selected SNPs for genomic evaluations, especially for the total number of piglets born. However, for the non-linear method, BLUP|GA showed only a small increase or even a decrease in prediction accuracy compared with the use of only pre-selected SNPs. Overall, the best genomic evaluation strategy for reproduction-related traits for a combined population was found to be GBLUP performed with a non-linear genomic relationship matrix using variants pre-selected from the 80K chip data based on Fst scores.     

Tool for genomic selection and breeding to evolutionary adaptation: Development of a 100K single nucleotide polymorphism array for the honey bee

High‐throughput high‐density genotyping arrays continue to be a fast, accurate, and cost‐effective method for genotyping thousands of polymorphisms in high numbers of individuals. Here, we have developed a new high‐density SNP genotyping array (103,270 SNPs) for honey bees, one of the most ecologically and economically important pollinators worldwide. SNPs were detected by conducting whole‐genome resequencing of 61 honey bee drones (haploid males) from throughout Europe. Selection of SNPs for the chip was done in multiple steps using several criteria. The majority of SNPs were selected based on their location within known candidate regions or genes underlying a range of honey bee traits, including hygienic behavior against pathogens, foraging, and subspecies. Additionally, markers from a GWAS of hygienic behavior against the major honey bee parasite Varroa destructor were brought over. The chip also includes SNPs associated with each of three major breeding objectives—honey yield, gentleness, and Varroa resistance. We validated the chip and make recommendations for its use by determining error rates in repeat genotypings, examining the genotyping performance of different tissues, and by testing how well different sample types represent the queen's genotype. The latter is a key test because it is highly beneficial to be able to determine the queen's genotype by nonlethal means. The array is now publicly available and we suggest it will be a useful tool in genomic selection and honey bee breeding, as well as for GWAS of different traits, and for population genomic, adaptation, and conservation questions.     

Design of a low‐density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy

Genotyping sheep for genome‐wide SNPs at lower density and imputing to a higher density would enable cost‐effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low‐density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50–475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single‐breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.     

Effect of the early social environment on behavioural and genomic responses to a social challenge in a cooperatively breeding vertebrate

The early social environment can have substantial, lifelong effects on vertebrate social behaviour, which can be mediated by developmental plasticity of brain gene expression. Early‐life effects can influence immediate behavioural responses towards later‐life social challenges and can activate different gene expression responses. However, while genomic responses to social challenges have been reported frequently, how developmental experience influences the shape of these genomic reaction norms remains largely unexplored. We tested how manipulating the early social environment of juvenile cooperatively breeding cichlids, Neolamprologus pulcher, affects their behavioural and brain genomic responses when competing over a resource. Juveniles were reared either with or without a breeder pair and a helper. Fish reared with family members behaved more appropriately in the competition than when reared without. We investigated whether the different social rearing environments also affected the genomic responses to the social challenge. A set of candidate genes, coding for hormones and receptors influencing social behaviour, were measured in the telencephalon and hypothalamus. Social environment and social challenge both influenced gene expression of egr‐1 (early growth response 1) and gr1 (glucocorticoid receptor 1) in the telencephalon and of bdnf (brain‐derived neurotrophic factor) in the hypothalamus. A global analysis of the 11 expression patterns in the two brain areas showed that neurogenomic states diverged more strongly between intruder fish and control fish when they had been reared in a natural social setting. Our results show that same molecular pathways may be used differently in response to a social challenge depending on early‐life experiences.     

Effect of partially replacing a barley‐based concentrate with flaxseed‐based products on the rumen bacterial population of lactating Holstein dairy cows

Aims

The effects of partial replacement of a barley‐based concentrate with flaxseed‐based products on the rumen bacterial population of lactating Holstein dairy cows were evaluated.

Methods and Results

Treatments fed were CONT, a normal diet that included barley silage, alfalfa hay and a barley‐based concentrate that contained no flaxseed or faba beans; FLAX, inclusion of a nonextruded flaxseed‐based product containing 55·0% flaxseed, 37·8% field peas and 6·9% alfalfa; EXT, similar to FLAX, but the product was extruded and EXTT, similar to FLAX, but product was extruded and field peas were replaced by high‐tannin faba beans. The rumen bacterial population was evaluated by utilizing 16S rRNA gene sequencing. Most abundant phyla, families and genera were unaffected. However, some taxa were affected; for example, unsaturated fatty acid content was negatively correlated with Clostridiaceae, and tannin content was negatively correlated with BS11 and Paraprevotellaceae.

Conclusions

Predominant rumen bacterial taxa were not affected, but the abundance of some taxa found in lower proportions shifted, possibly due to sensitivity to unsaturated fatty acids or tannins.

Significance and Impact of the Study

Flaxseed‐based products were effective for partially replacing barley‐based concentrate in rations of lactating dairy cows. No negative effects of these products were observed on the abundance of predominant rumen bacterial taxa, with only minor shifts in less abundant bacteria.     

Effect of <Emphasis Type="Italic">Lr34/Yr18</Emphasis> on agronomic and quality traits in a spring wheat mapping population and implications for breeding

The locus Lr34/Yr18 plays an important role in conferring resistance to a number of fungal diseases and is thus an important component of global wheat breeding efforts. We investigated the differences in disease response and agronomic traits of the ‘CDC Teal’ × ‘CDC Go’ spring wheat population of 187 recombinant inbred lines (RILs) in relation to the presence/absence of the rust resistance gene Lr34/Yr18. Lines carrying the resistant allele of Lr34/Yr18 were taller, matured earlier, and yielded less grain with lower test weights than lines without Lr34/Yr18. Lines with or without the resistant allele of Lr34/Yr18 did not differ for grain protein content, SDS sedimentation volume, and for resistance to leaf spotting and common bunt. Lines with Lr34/Yr18 exhibited lower leaf and stripe rust infection than lines without it. We selected superior lines from the population based on high yield, protein content, SDS sedimentation, and the presence of the resistant allele of Lr34/Yr18 and grew them with continued selection in replicated yield trials over nine site-years. We attempted to combine Lr34/Yr18 with high yield, protein content, and SDS sedimentation suitable for the Canadian western red spring wheat class. Our results suggested that the population size we used was not large enough to obtain recombinants with high yield potential, high grain protein, and acceptable quality attributes. Moreover, selection for Lr34/Yr18 resulted in the elimination of lines with high yield potential. We therefore suggest using a population size of at least 310 to increase the potential of pooling Lr34/Yr18 with high grain yield and desirable agronomic and end-use quality attributes.