Progeny Testing Scheme Research Articles

Optimization Strategies to Adapt Sheep Breeding Programs to Pasture-Based Production Environments: A Simulation Study.

Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.

PSVI-5 Optimizing Limited Deep Phenotyping Capacity for Genetic Studies and Routine Genetic Evaluations: Application to Boar Taint

Abstract In the context of genetic research, but even more for routine genetic evaluations, a recurrent problem is the difficulty to obtain measurements for certain required phenotypes. This can be caused by limited facilities, difficulties in sampling, expensive analyses, limited staff relative to number of animals, and many other reasons. It may therefore be necessary to select which animals to phenotype while maintaining the phenotyping quality and avoiding bias or omitting some animals of interest. This implies developing strategies to do the phenotyping in a non-biased way using a priori available minimum knowledge. An example could be predicting post weaning growth of cattle based on earlier information as birth weight and weaning weight to optimize the use of feed intake or methane measurement facilities. In pigs, similar situations might occur for growth or emissions. However, another important example is boar taint (BT) which shows that not only sequential weighing, but also partial measurements of BT may require attention. BT is defined as urinary and fecal and associated with 2 major compounds, androstenone (AND) and skatole (SKA). The classical method of odor discrimination, “Human Nose Score” (HNS), is notoriously unreliable. Even if odor is known to be complex, adding AND and SKA to HNS would help reveal better the boar taint condition (i.e., phenotype). However, the measurements of AND and SKA or other compounds in fat are expensive. Therefore, it would be important to optimize these measurements by selecting relevant fat samples taken at slaughterhouses. We tested the following optimization strategy. The objective was to obtain a good distribution of a posteriori measured AND and SKA in the selected samples given initial asymmetric populations with positive HNS (3 to 10% declared positives). In a first step, the strategy pooled 2 available HNS for 997 across 22 contemporary groups (CG) and added limited chemical measurements (218/997) available for older samples. The data were obtained from a boar progeny testing scheme; therefore, CG were linked through connecting sires from artificial insemination having sons in different CG. A multi-trait BLUP model was used adjusting for metabolic weight using a 4-generation pedigree. A total of 15% of the animals in the last 6 contemporary groups were selected using first positive HNS (17/300), then keeping the highest and lowest predictions and maintaining diversity by not repeating boars from the same sire-family (i.e., when a family was already present, the next animal was taken). A posteriori chemical analyses were performed on selected samples to compare predicted and observed classifications in tainted and untainted. Research showed an accuracy of 0.73 (Table 1), leading to 58% tainted and 42% untainted compared with the expected ratio. Improvements of this strategy are currently in development, including using genomics.

Whole blood transcriptome analysis of lactating Murrah buffaloes divergent to contrasting genetic merits for milk yield

Functional genome profiling of Murrah buffaloes (Bubalus bubalis) was performed for milk-production trait by whole blood transcriptome analysis comparing RNA-seq data assembled from high and low milk producing multiparous (5 -6 parity) animals. These buffaloes reflected the genetic merit inherited as daughters born to extremely high- and low-end bulls evaluated under a progeny testing scheme and ranked by the estimated breeding value. The average standard milk yield (SMY) over the 305 d during the parity was recorded as 2909.50L ± 492.63 and 1869.57 ± 189.36L in high- and low-performance buffaloes, respectively. The “reference” assembly data was assembled from transcriptome libraries of a group of buffaloes (n=16), comprising of animals in different physiological states. Replicates selected within each category of the high and low genetic merit animals showed a correlation coefficient of high order (R2=0.98) while comparing with the `reference' assembly. The sequence data of selected buffaloes, mapped over the Mediterranean water buffalo genome, revealed differentially expressed genes (DEGs) distinctly depicted via heat maps and volcano plots obtained for two categories of animals, determining more than 25,000 genes via the Cufflink analysis. DEGs included 83 down-regulating and 142 up-regulating genes (p<0.05, FDR<0.05). Functional classification of the DEGs revealed a fine networking of biological processes, primarily cell signaling, cell proliferation, cell differentiation, RNA splicing, fat metabolism, and inflammasome generation. These processes are regulated by transcription factors and binding proteins covered under the network of TNF alpha signaling, NF-kappa B signaling and MAPK PI3K-AKT signaling pathways/ cascade emerged as main biological pathways. Emerged pathways revealed remarkably intricate tuning of metabolic and cell development processes converging into milk production in buffaloes. Segregated patterns of gene expression obtained for high and low milk producing buffaloes using the non-invasive method of whole blood transcriptome analysis has emerged as a promising resource comprising gene network and protein -protein interactions, primarily involved in lactation. Synergism of transcription factors and binding proteins promoting epigenetic regulation at all development stages of mammary tissue induce mammogenic and lactogenic responses for subsequent milk secretion under optimum feeding management. These findings may help improve breeding strategies to achieve the desired milk yield in Murrah buffaloes.

The contribution of a MOET nucleus scheme for the improvement of Guzerá (Bos indicus) cattle for milk traits in Brazil.

The Guzerá breed evolved from the introduction of breeds from India, mainly the Kankrej breed, into Brazilian livestock at the end of the 19th century. Guzerá adapted well to the climatic conditions of Brazil, where it is considered a dual-purpose breed and has been used for pasture-based beef, milk or dual-purpose production systems with the use of low-medium inputs. The importance of this genetic resource for milk production in tropical regions moved breeders to implement the National Breeding Program for the Improvement of Guzerá in 1994, based on both progeny testing and MOET nucleus schemes. We sought to evaluate the role of the MOET nucleus scheme in the phenotypic and genetic progress for milk traits in this breed. The initial database used in the present study consisted of 6,513 cows, daughters of 761 bulls. We performed genetic evaluations with different datasets using a linear mixed model in a single trait analysis, including the relationship matrix, in order to estimate breeding values. Inbreeding coefficients were also calculated using the relationship of descent between two parents. Annual phenotypic, genetic and inbreeding trends were obtained for each dataset, considering the genetic pathways of both the bull and the cow. The low genetic progress found for milk yield in the whole population (5.27 ± 0.30 kg/year) partially accounted for the dual-purpose selection goal, despite the higher genetic progress in the MOET nucleus (9.39 ± 0.79 kg/year). The inbreeding coefficient was minimized at the beginning of the breeding program based on the use of new lineages. Posteriorly, it started increasing again from 0.002 in 1991 to 0.008 in 2019. The results provided evidence of the significant contribution of the MOET nucleus scheme for the phenotypic and genetic progress of Guzerá breed for milk traits, as well as of the impact of the breeding program on the inbreeding coefficient rate in the early years. New strategies need to be designed for the Guzerá breed, to allow for greater improvement of milk traits and minimizing the rate of the inbreeding coefficient.

Polymorphism of bovine STAT5A gene and its association with milk production traits in crossbred cattle of Kerala, India

Signal transducer and activator of transcription 5A (STAT5A) is a transcription factor that mediates signals fromprolactin and growth hormone, the corresponding main regulators of lactation and growth of mammals. STAT5A issuggested to be candidate marker gene for quantitative traits in dairy cattle with respect to milk production traits. The objectives of present study were to investigate polymorphism of bovine STAT5A gene and its probable association with milk yield and milk-constituent traits in crossbred cattle. Crossbred cattle (250) in different farms of Kerala Veterinary and Animal Sciences University and different field centres of ICAR-Field progeny testing scheme,Thrissur were screened for polymorphism in exon-8 region of STAT5A gene using PCR-single-strand conformationpolymorphism analysis. A synonymous polymorphism (C/T) was found at position 1071 in exon 8. All three possiblegenotypes were identified with estimated frequencies of 0.584 (CC), 0.332 (CT) and 0.084 (TT); the estimated allelefrequencies were 0.75 (C) and 0.25 (T). The C/T genotypes demonstrated significant impact on milk fat per centbeing significantly higher in CC genotype cattle in comparison to other genotypes. It is evident by this investigationthat STAT5A polymorphism could be a promising indirect marker to improve milk production in crossbred cattle ofKerala.

Estimation of co-variance components and genetic parameters of fertility and production traits in crossbred cattle of Kerala

Estimation of co-variance components and genetic parameters of fertility and production traits will help to find out the relative importance of genetic and environmental components of each trait and to develop a genetic evaluation system for overall improvement in performances of crossbred cattle of Kerala. In the present study, major fertility trait considered was daughters pregnancy rate (DPR), measures the percentage of non-pregnant animals that become pregnant during each oestrous cycle. Data pertaining to 1180 crossbred cattle sired by 208 Frieswal bulls, spread over a period of 16 years from 2003 to 2019, maintained at different farms of Kerala Veterinary and Animal Sciences University and field centres of ICAR- Filed Progeny Testing Scheme were analysed in the study. Estimates of covariance components and genetic parameters were obtained using restricted maximum likelihood (REML) approach using average information (AI) algorithm. It was observed that DPR had low heritability (0.092 ± 0.03), compared to 305 days milk yield (MY) (0.170 ± 0.094) and fat percent (FP) (0.173 ± 0.072). Phenotypic (rp), genetic (rg) and residual (re) correlation indicated unfavourable association of fertility with production traits. The estimates of variance and (co)variance components were computed by multivariate animal model. The results indicated that DPR was having lower direct additive (σ2a) 0.046 and environmental variance (σ2c) 0.063 compared to other traits. Highest additive genetic variance (σ2a) 27035.8 was obtained for MY. The study estimates the magnitude of correlations and covariances of fertility and production traits. Since DPR had lower additive genetic variance and negative association with milk production traits in cattle, it would be included as an indirect measure in the evaluation and breeding programs of crossbred cattle of Kerala.

Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattle Case.

In accordance with the infinitesimal model for quantitative traits, a very large number of genes affect nearly all economic traits. In only two cases has the causative polymorphism been determined for genes affecting economic traits in dairy cattle. Most current methods for genomic evaluation are based on the "two-step" method. Genetic evaluations are computed by the individual animal model, and functions of the evaluations of progeny-tested sires are the dependent variable for estimation of marker effects. With the adoption of genomic evaluation in 2008, annual rates of genetic gain in the US increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits, including female fertility, herd-life and somatic cell concentration. Gradual elimination of the progeny test scheme has led to a reduction in the number of sires with daughter records and less genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and "environmentally friendly" production. Genetic variance for economic traits is maintained by increase in frequency of rare alleles, new mutations, and changes in selection goals and management.

Pedigree relatedness and pseudo-phenotypes as a first approach to assess and maintain genetic diversity of the Walloon Piétrain pig population

The breeding of pure Piétrain animals is currently performed in two different contexts: industrial lines and individual breeders. As one of the four main pig breeds worldwide, the Piétrain breed might not be considered to be endangered. However, in Wallonia (southern Belgium), even though the Belgian Piétrain programme aims to preserve the Walloon Piétrain population through cryopreservation of semen of relevant boars, only 10 pure Piétrain breeders remain and produce traditional breeding stock. Current breeders are retiring and no new breeders are replacing them. Moreover, the genetic diversity of the pigs from these individual breeders may highly contribute to the global gene pool of the breed, therefore it is important to assess this diversity. This was done on a local level by using pedigree relatedness but also differences in phenotypes. Pedigree parameters such as effective population size (Ne), genetic diversity and inbreeding coefficients were estimated for 219 boars from which offspring performances were recorded at the Walloon test station. A multi-dimensional scaling (MDS) was performed based on genetic distances. Considering the current owners of the boars, a principal component analysis (PCA) was made on deregressed breeding values (pseudo-phenotypes) based on the performances of their crossbred offspring at the test station. The Ne was 223, the genetic diversity parameter was 97.96%, while the mean inbreeding coefficient was 2.74%. The MDS identified four main clusters of boars. Two principal components indicated two major directions of selection: growth or meat traits. Genetically close boars did not necessarily show similar performances in their offspring. Different performances for genetically linked animals should reflect the breeding objectives of their owner, a practice that was confirmed by most owners during interviews. Pedigree, phenotypes and genotypes provide complementary information and therefore should be used simultaneously in the implementation of conservation programmes. These first results also showed that the genetic diversity of the Walloon Piétrain population is so far well preserved. However, recommendations need to be developed in order to maintain it. For example, boars provided to the progeny-testing scheme should come from equally contributing breeders, allowing the Belgian Piétrain programme to sample boars from a larger variety of animals taking into account genetic and phenotypic diversity. Finally, in-situ preservation of Piétrain diversity will require the development of new tools and mating schemes.

189 Choices ahead for dairy cattle breeding programs

Abstract Dairy cattle breeding programs have been transformed from conventional progeny-testing schemes to genomic selection paired with nucleus herd breeding in the span of one decade. This was spurred by the simultaneous advances in low-cost SNP genotyping, genomic selection methodology and reproductive biotechnologies. The rates of genetic progress have approximately doubled in this time but so have increases in inbreeding levels. This was driven by intense competition between AI studs and farmer adherence to common selection indices which has concentrated selection on very elite segments of juvenile age groups. This has led to speculation on the need for alternative indices and selection for novel traits in order to differentiate breeding programs and customize selection for unique farm conditions. This will be made more possible by the advent of on-farm sensor technology and artificial intelligence algorithms. Large commercial dairies are increasingly experimenting with crossbreeding with varying levels of success and this will require a new approach by breeding programs to focus both on purebred and crossbred performance. In addition, the potential exists for use of gene-editing to further enable value-added traits to be added into breeding programs. In parallel with breeding program advancements, consumer trends are also changing to include more interest in specialty dairy products with implied differences in digestibility, health or environmental impacts. Identifying technologies and traits that will add value either on the farm as well as at the consumer level will be a challenge for today’s breeders and producers. Some new technologies, such as gene editing, can pose consumer acceptance challenges if they are perceived to be used carelessly or for the wrong reasons. Careful choices will need to be made to continue to improve profitability, functionality and health of dairy cattle while also meeting higher consumer standards for animal welfare, health and the environment.

Genotyping more cows increases genetic gain and reduces rate of true inbreeding in a dairy cattle breeding scheme using female reproductive technologies

Both small dairy cattle populations and dairy cattle populations with a low level of linkage disequilibrium (LD) suffer from low reliability of genomic prediction. In this study, we investigated whether adding more genotyped cows to the reference population influences the rate of genetic gain and rate of inbreeding by affecting the reliability. A standard breeding program with a large reference population and high LD, which mimicked a breeding program for Danish Holstein population, was simulated as a reference. A Danish Jersey population with a small reference population and high LD and a Red Dairy Cattle population with a large reference population and low LD were also simulated. Two additional breeding programs were simulated for Danish Jersey and Red Dairy Cattle populations, where 2,000 additional genotyped cows were included in the population for genomic selection. All 5 simulated breeding programs were initiated by a founder population to generate LD resembling the real LD pattern, followed by a 20-yr conventional progeny-testing scheme with 1,000 or 10,000 genotyped progeny-tested bulls and a 10-yr genomic selection scheme with or without 2,000 additional genotyped cows. Evaluation criteria were annual monetary genetic gain and rate of true inbreeding. Our results showed that adding more genotyped cows to the reference in dairy cattle populations has the potential to increase genetic gain and reduce the rate of inbreeding, regardless of reference population size and level of LD. However, it is still not possible to reach the same genetic gain as in the simulated Danish Holstein population with either a small reference population or low LD. Our results also showed that in a small reference population with high LD, it is difficult to manage inbreeding because of lower accuracy compared with the simulated Danish Holstein population and a smaller number of relevant families to select from. Therefore, breeding strategies need to be chosen to match population size and structure. The rate of true inbreeding is always underestimated by pedigree inbreeding and even more in genomic breeding programs, indicating that some forms of genome-wide inbreeding, instead of pedigree-based inbreeding, should be used to monitor inbreeding when genomic selection is implemented.

Reliability of breeding values for feed intake and feed efficiency traits in dairy cattle: When dry matter intake recordings are sparse under different scenarios

Currently, routine recordings of dry matter intake (DMI) in commercial herds are practically nonexistent. Recording DMI from commercial herds is a prerequisite for the inclusion of feed efficiency (FE) traits in dairy cattle breeding goals. To develop future on-farm phenotyping strategies, recording strategies that are low cost and less demanding logistically and that give relatively accurate estimates of the animal's genetic merit are therefore needed. The objectives of this study were (1) to estimate genetic parameters for daily DMI and FE traits and use the estimated parameters to simulate daily DMI phenotypes under different DMI recording scenarios (SCN) and (2) to use the simulated data to estimate for different scenarios the associated reliability of estimated breeding value and accuracies of genomic prediction for varying sizes of reference populations. Five on-farm daily DMI recording scenarios were simulated: once weekly (SCN1), once monthly (SCN2), every 2 mo (SCN3), every 3 mo (SCN4), and every 4 mo (SCN5). To estimate reliability of estimated breeding values, DMI and FE observations and true breeding values were simulated based on variance components estimated for daily observations of Nordic Red cows. To emulate realistic on-farm recording, 5 data set replicates, each with 36,037 DMI and FE records, were simulated for real pedigree and data structure of 789 Holstein cows. Observations for the 5 DMI recording scenarios were generated by discarding data in a step-wise manner from the full simulated data per the scenario's definitions. For each of these scenarios, reliabilities were calculated as correlation between the true and estimated breeding values. Variance components and genetic parameters were estimated for daily DMI, residual feed intake (RFI), and energy conversion efficiency (ECE) fitting the random regression model. Data for variance components were from 227 primiparous Nordic Red dairy cows covering 8 to 280 d in milk. Lactation-wise heritability for DMI, RFI, and ECE was 0.33, 0.12, and 0.32, respectively, and daily heritability estimates during lactation ranged from 0.18 to 0.45, 0.08 to 0.32, and 0.08 to 0.45 for DMI, RFI, and ECE, respectively. Genetic correlations for DMI between different stages of lactation ranged from -0.50 to 0.99. The comparison of different on-farm DMI recording scenarios indicated that adopting a less-frequent recording scenario (SCN3) gave a similar level of accuracy as SCN1 when 17 more daughters are recorded per sire over the 46 needed for SCN1. Such a strategy is less demanding logistically and is low cost because fewer observations need to be collected per animal. The accuracy of genomic predictions associated with the 5 recording scenarios indicated that setting up a relatively larger reference population and adopting a less-frequent DMI sampling scenario (e.g., SCN3) is promising. When the same reference population size was considered, the genomic prediction accuracy of SCN3 was only 5.0 to 7.0 percentage points lower than that for the most expensive DMI recording strategy (SCN1). We concluded that DMI recording strategies that are sparse in terms of records per cow but with slightly more cows recorded per sire are advantageous both in genomic selection and in traditional progeny testing schemes when accuracy, logistics, and cost implications are considered.

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.

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2- and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.

Genomic Selection Improves Response to Selection in Resilience by Exploiting Genotype by Environment Interactions.

Genotype by environment interactions (GxE) are very common in livestock and hamper genetic improvement. On the other hand, GxE is a source of genetic variation: genetic variation in response to environment, e.g., environmental perturbations such as heat stress or disease. In livestock breeding, there is tendency to ignore GxE because of increased complexity of models for genetic evaluations and lack of accuracy in extreme environments. GxE, however, creates opportunities to increase resilience of animals toward environmental perturbations. The main aim of the paper is to investigate to which extent GxE can be exploited with traditional and genomic selection methods. Furthermore, we investigated the benefit of reaction norm (RN) models compared to conventional methods ignoring GxE. The questions were addressed with selection index theory. GxE was modeled according to a linear RN model in which the environmental gradient is the contemporary group mean. Economic values were based on linear and non-linear profit equations. Accuracies of environment-specific (G)EBV were highest in intermediate environments and lowest in extreme environments. RN models had higher accuracies of (G)EBV in extreme environments than conventional models ignoring GxE. Genomic selection always resulted in higher response to selection in all environments than sib or progeny testing schemes. The increase in response was with genomic selection between 9 and 140% compared to sib testing and between 11 and 114% compared to progeny testing when the reference population consisted of 1 million animals across all environments. When the aim was to decrease environmental sensitivity, the response in slope of the RN model with genomic selection was between 1.09 and 319 times larger than with sib or progeny testing and in the right direction in contrast to sib and progeny testing that still increased environmental sensitivity. This shows that genomic selection with large reference populations offers great opportunities to exploit GxE to increase resilience of animals.

Progeny tested bull production for dairy cattle development in Bangladesh

The Department of Livestock Services has been implementing “Breed Up-gradation Through Progeny Test" Project since 2003, for selecting dairy breeding bulls based on their progeny performance. This paper describes the procedures followed for setting up field based progeny testing scheme involving private farmers and reports the data of progeny testing in Bangladesh from 2007 to 2013. Until now more than 5000 farmers, having at least one Friesian crossbred cow, were registered under this program in 22 districts of the country. Five Friesian crossbred candidate bulls were evaluated on their estimated breeding values for 100-day milk yields of 199 first calving daughters during 2007 to 2013. Out of five bulls, two bulls were found promising due to their positive breeding values compared to the others. By 2015, 100 day lactation milk yield data from more than 450 daughters of 6 candidate bulls (used in 2009) of the second batch are expected to be available for evaluation, and about 2452 daughters (heifers) of different ages produced out of 29 candidate bulls are available under field recording in different geographical areas of the country. So, proven dairy bull production through field-based progeny testing program in Bangladesh is underway.Bang. J. Anim. Sci. 2015. 44(2): 106-112

Optimizing the design of small-sized nucleus breeding programs for dairy cattle with minimal performance recording

Dairy cattle breeding programs in developing countries are constrained by minimal and erratic pedigree and performance recording on cows on commercial farms. Small-sized nucleus breeding programs offer a viable alternative. Deterministic simulations using selection index theory were performed to determine the optimum design for small-sized nucleus schemes for dairy cattle. The nucleus was made up of 197 bulls and 243 cows distributed in 8 non-overlapping age classes. Each year 10 sires and 100 dams were selected to produce the next generation of male and female selection candidates. Conception rates and sex ratio were fixed at 0.90 and 0.50, respectively, translating to 45 male and 45 female candidates joining the nucleus per year. Commercial recorded dams provided information for genetic evaluation of selection candidates (bulls) in the nucleus. Five strategies were defined: nucleus records only [within-nucleus dam performance (DP)], progeny records in addition to nucleus records [progeny testing (PT)], genomic information only [genomic selection (GS)], dam performance records in addition to genomic information (GS+DP), and progeny records in addition to genomic information (GS+PT). Alternative PT, GS, GS+DP, and GS+PT schemes differed in the number of progeny per sire and size of reference population. The maximum number of progeny records per sire was 30, and the maximum size of the reference population was 5,000. Results show that GS schemes had higher responses and lower accuracies compared with other strategies, with the higher response being due to shorter generation intervals. Compared with similar sized progeny-testing schemes, genomic-selection schemes would have lower accuracies but these are offset by higher responses per year, which might provide additional incentive for farmers to participate in recording.

Genetic and crossbreeding parameters for incidence of recorded clinical lameness in New Zealand dairy cattle

AIM: To estimate genetic and crossbreeding parameters for the incidence of recorded clinical lameness in New Zealand dairy cattle. METHODS: Herd records from 76,357 cows, collected during the 2005/06 to 2008/09 milking seasons from 155 herds in the Livestock Improvement Corporation young sire progeny test scheme, were used to estimate genetic parameters and breed effects for incidence of recorded clinical lameness in HolsteinFriesian, Jersey and crossbred dairy cattle. Recorded clinical lameness was coded “1” for cows that presented at least one event of clinical lameness at any day during the season and “0” for unaffected cows. Genetic parameters were estimated using an animal model across breeds considering all and then only first lactation records. Heritability and repeatability of recorded clinical lameness were calculated from the variance component estimates both with and without logit transformation. RESULTS: The mean incidence of recorded clinical lameness per herd was 6.3 (min 2, max 34)%. The incidence of recorded clinical lameness in Holstein Friesian cows (mean 6.8, SE 0.24%) was higher than the incidence of recorded clinical lameness in crossbred (mean 6.1, SE 0.19%) and Jersey cows (mean 6.0, SE 0.28%) (p=0.0002). There was no difference in incidence between crossbred and Jersey cows (p=0.96). Estimates of the heritability of recorded clinical lameness as an untransformed trait were 0.053 (SE 0.014) for first lactation records and 0.016 (SE 0.003) for all lactation records. As a transformed (logit) trait heritabilities were 0.067 (SE 0.024) and 0.044 (SE 0.016) for first and all lactation records, respectively. The repeatability estimates of recorded clinical lameness were 0.071 (SE 0.005) and 0.107 (SE 0.011) for untransformed and logit transformed lactation records, respectively. Sire estimated breeding values for recorded clinical lameness showed the lowest values in Jersey sires, and ranged between -5 and 8%. CONCLUSIONS: Despite the low heritability of recorded clinical lameness, this study provided evidence that there is significant exploitable animal genetic variation. Selection of specific sires across and within breeds could be an option for increasing genetic resistance to lameness in New Zealand dairy cattle.

Analyzes of genome-wide association follow-up study for calving traits in dairy cattle.

BackgroundThere is often a pronounced disagreement between results obtained from different genome-wide association studies in cattle. There are multiple reasons for this disagreement. Particularly the presence of false positives leads to a need to validate detected QTL before they are optimally incorporated or weighted in selection decisions or further studied for causal gene. In dairy cattle progeny testing scheme new data is routinely accumulated which can be used to validate previously discovered associations. However, the data is not an independent sample and the sample size may not be sufficient to have enough power to validate previous discoveries. Here we compared two strategies to validate previously detected QTL when new data is added from the same study population. We compare analyzing a combined dataset (COMB) including all data presently available to only analyzing a validation dataset (VAL) i.e. a new dataset not previously analyzed as an independent replication. Secondly, we confirm SNP detected in the Reference population (REF) (i.e. previously analyzed dataset consists of older bulls) in the VAL dataset.ResultsClearly the results from the combined (COMB) dataset which had nearly twice the sample size of other two subsets allowed the detection of far more significant associations than the two smaller subsets. The number of significant SNPs in REF (older bulls) was about four times higher compare to VAL (younger bulls) though both had similar sample sizes, 2,219 and 2,039 respectively. A total of 424 SNP-trait combinations on 22 chromosomes showed genome-wide significant association involving 284 unique SNPs in the COMB dataset. In the REF data set 101 associations (73 unique SNPs) and in the VAL 24 associations (18 unique SNPs) were found genome-wide significant. Sixty-eight percent of the SNPs in the REF dataset could be confirmed in the VAL dataset. Out of 469 unique SNPs showing chromosome-wide significant association with calving traits in the REF dataset 321 could be confirmed in the VAL dataset at P < 0.05.ConclusionsThe follow-up study for GWAS in cattle will depend on the aim of the study. If the aim is to discover novel QTL, analyses of the COMB dataset is recommended, while in case of identification of the causal mutation underlying a QTL, confirmation of the discovered SNPs are necessary to avoid following a false positive.

Response to genomic selection: The Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting

BackgroundOver the last ten years, genomic selection has developed enormously. Simulations and results on real data suggest that breeding values can be predicted with high accuracy using genetic markers alone. However, to reach high accuracies, large reference populations are needed. In many livestock populations or even species, such populations cannot be established when traits are difficult or expensive to record, or when the population size is small. The value of genomic selection is then questionable.MethodsIn this study, we compare traditional breeding schemes based on own performance or progeny information to genomic selection schemes, for which the number of phenotypic records is limiting. Deterministic simulations were performed using selection index theory. Our focus was on the equilibrium response obtained after a few generations of selection. Therefore, we first investigated the magnitude of the Bulmer effect with genomic selection.ResultsResults showed that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP estimated breeding values, and is independent of the accuracy of selection. The reduction in response with genomic selection is greater than with selection based directly on phenotypes without the use of pedigree information, such as mass selection. To maximize the accuracy of genomic estimated breeding values when the number of phenotypic records is limiting, the same individuals should be phenotyped and genotyped, rather than genotyping parents and phenotyping their progeny. When the generation interval cannot be reduced with genomic selection, large reference populations are required to obtain a similar response to that with selection based on BLUP estimated breeding values based on own performance or progeny information. However, when a genomic selection scheme has a moderate decrease in generation interval, relatively small reference population sizes are needed to obtain a similar response to that with selection on traditional BLUP estimated breeding values.ConclusionsWhen the trait of interest cannot be recorded on the selection candidate, genomic selection schemes are very attractive even when the number of phenotypic records is limited, because traditional breeding requires progeny testing schemes with long generation intervals in those cases.

Hot topic: Effect of breeding strategies using genomic information on fitness and health

A complex deterministic approach was used to model the breeding goal and breeding structure for the Austrian Fleckvieh (dual-purpose Simmental) breed. The reference breeding goal corresponded to the current total merit index (TMI-R), where dairy traits have a relative weight of 37.9% and fitness traits of 43.7% (beef traits 16.5%; milkability 2%). The breeding program was characterized by 280,000 cows under performance recording, 3,200 bull dams, 100 test bulls with a test capacity of 25%, and 15 proven bulls and 8 bull sires per year. The annual monetary genetic gain (AMGG) was generated mainly by increases in milk fat and milk protein yield (80.6%) and only to a small extent by fitness traits (6.6%). The inclusion of direct health traits (early reproductive disorders, cystic ovaries, and mastitis) with their economic weights increased the relative AMGG for fitness traits from 6.6 to 11.2%. The presently slightly negative AMGG for fertility index and udder health changed in a positive direction. Increasing the weight on the direct health traits by 50% resulted in a further shift toward fitness and health. The effect of strategies using genomic information in a total merit index (TMI) with varying weights on fitness and health traits was also analyzed. The conventional progeny-testing scheme was defined as the reference breeding program. A breeding program was considered to be genomically enhanced (GS50) when 50% of inseminations of herdbook cows and of bull dams were from young bulls with a genomic TMI, and a second program (GS100) did not rely on progeny-tested bulls at all. For GS50, a clear shift of the relative gain in AMGG toward fitness and health traits was observed for all 3 TMI scenarios, as a result of larger progeny groups and a shorter generation interval. For GS100, where no gene flow from progeny-tested bulls was assumed, the genetic gain per generation was lower for the fertility and udder health index but higher per year. The results based on natural genetic gain per year showed that no positive genetic response for fertility and udder health index were achieved for TMI-R (without the inclusion of direct health traits) in GS50 and GS100. The direction of the genetic trend was determined by the weights given to fertility and udder health indices within the TMI. When appropriate weights generated a clear positive trend, GS50 and GS100 reinforced this trend.