Breeding Goal Traits Research Articles

Using selection index theory to estimate consistency of multi-locus linkage disequilibrium across populations.

BackgroundThe potential of combining multiple populations in genomic prediction is depending on the consistency of linkage disequilibrium (LD) between SNPs and QTL across populations. We investigated consistency of multi-locus LD across populations using selection index theory and investigated the relationship between consistency of multi-locus LD and accuracy of genomic prediction across different simulated scenarios. In the selection index, QTL genotypes were considered as breeding goal traits and SNP genotypes as index traits, based on LD among SNPs and between SNPs and QTL. The consistency of multi-locus LD across populations was computed as the accuracy of predicting QTL genotypes in selection candidates using a selection index derived in the reference population. Different scenarios of within and across population genomic prediction were evaluated, using all SNPs or only the four neighboring SNPs of a simulated QTL. Phenotypes were simulated using different numbers of QTL underlying the trait. The relationship between the calculated consistency of multi-locus LD and accuracy of genomic prediction using a GBLUP type of model was investigated.ResultsThe accuracy of predicting QTL genotypes, i.e. the measure describing consistency of multi-locus LD, was much lower for across population scenarios compared to within population scenarios, and was lower when QTL had a low MAF compared to QTL randomly selected from the SNPs. Consistency of multi-locus LD was highly correlated with the realized accuracy of genomic prediction across different scenarios and the correlation was higher when QTL were weighted according to their effects in the selection index instead of weighting QTL equally. By only considering neighboring SNPs of QTL, accuracy of predicting QTL genotypes within population decreased, but it substantially increased the accuracy across populations.ConclusionsConsistency of multi-locus LD across populations is a characteristic of the properties of the QTL in the investigated populations and can provide more insight in underlying reasons for a low empirical accuracy of across population genomic prediction. By focusing in genomic prediction models only on neighboring SNPs of QTL, multi-locus LD is more consistent across populations since only short-range LD is considered, and accuracy of predicting QTL genotypes of individuals from another population is increased.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0252-6) contains supplementary material, which is available to authorized users.

Preferences for pig breeding goals among organic and conventional farmers in Sweden

Organic and conventional pig farmers’ preferences regarding breeding goal traits important in pigs were investigated using an advanced internet-based questionnaire. Farmers were asked what pig traits they spontaneously considered important, to rank 15 given traits and to weigh traits against each other given the estimated genetic change (calculated based on selection index theory). Organic and conventional farmers ranked both functional and production traits highly. For example, more than 70 % of farmers from both production systems ranked piglet survival, fertility, sow longevity, leg health, feed conversion, piglet birth weight, piglet and slaughter pig growth as being of high or moderate importance (1–10/15). Litter size and shoulder ulcers were ranked higher by respondents from conventional farms, while parasite resistance and roughage consumption were ranked higher by respondents from organic farms. Regarding estimated desired genetic change, respondents from organic farms favoured health traits while conventional farmers favoured growth traits. In order to obtain genetic improvement in those traits, farmers gave low priority to carcass quality and litter size. Due to the variations in farmers’ breeding goal trait preferences demonstrated here, it is clearly important to take farmers’ preferences into consideration when developing future pig breeding strategies.

Economic values of growth and feed efficiency for fish farming in recirculating aquaculture system with density and nitrogen output limitations: a case study with African catfish (Clarias gariepinus).

In fish farming, economic values (EV) of breeding goal traits are lacking, even though they are key parameters when defining selection objectives. The aim of this study was to develop a bioeconomic model to estimate EV of 2 traits representing production performances in fish farming: the thermal growth coefficient (TGC) and the feed conversion ratio (FCR). This approach was applied to a farm producing African catfish (Clarias gariepinus) in a recirculating aquaculture system (RAS). In the RAS, 2 factors could limit production level: the nitrogen treatment capacity of the biofilter or the fish density in rearing tanks at harvest. Profit calculation includes revenue from fish sales, cost of juveniles, cost of feed, cost of waste water treatment, and fixed costs. In the reference scenario, profit was modeled to zero. EV were calculated as the difference in profit per kilogram of fish between the current population mean for both traits (µt) and the next generation of selective breeding (µt+Δt) for either TGC or FCR. EV of TGC and FCR were calculated for three generations of hypothetical selection on either TGC or FCR (respectively 6.8% and 7.6% improvement per generation). The results show that changes in TGC and FCR can affect both the number of fish that can be stocked (number of batches per year and number of fish per batch) and the factor limiting production. The EV of TGC and FCR vary and depend on the limiting factors. When dissolved NH3-N is the limiting factor for both µt and µt+Δt, increasing TGC decreases the number of fish that can be stocked but increases the number of batches that can be grown. As a result, profit remains constant and EVTGC is zero. Increasing FCR, however, increases the number of fish stocked and the ratio of fish produced per kilogram of feed consumed ("economic efficiency"). The EVFCR is 0.14 €/kg of fish, and profit per kilogram of fish increases by about 10%. When density is the limiting factor for both µt and µt+Δt, the number of fish stocked per batch is fixed; therefore, extra profit is obtained by increasing either TGC, which increases the annual number of batches, or by decreasing FCR, which decreases annual feed consumption. EVTGC is 0.03 €/kg of fish and EVFCR is 0.05-0.06 €/kg of fish. These results emphasize the importance of calculating economic values in the right context to develop efficient future breeding programs in aquaculture.

Breeding objectives for pigs in Kenya. I: bio-economic model development and application to smallholder production systems.

A deterministic bio-economic model was developed and applied to evaluate biological and economic variables that characterize smallholder pig production systems in Kenya. Two pig production systems were considered namely, semi-intensive (SI) and extensive (EX). The input variables were categorized into biological variables including production and functional traits, nutritional variables, management variables and economic variables. The model factored the various sow physiological systems including gestation, farrowing, lactation, growth and development. The model was developed to evaluate a farrow to finish operation, but the results were customized to account for a farrow to weaner operation for a comparative analysis. The operations were defined as semi-intensive farrow to finish (SIFF), semi-intensive farrow to weaner (SIFW), extensive farrow to finish (EXFF) and extensive farrow to weaner (EXFW). In SI, the profits were the highest at KES. 74,268.20 per sow per year for SIFF against KES. 4026.12 for SIFW. The corresponding profits for EX were KES. 925.25 and KES. 626.73. Feed costs contributed the major part of the total costs accounting for 67.0, 50.7, 60.5 and 44.5% in the SIFF, SIFW, EXFF and EXFW operations, respectively. The bio-economic model developed could be extended with modifications for use in deriving economic values for breeding goal traits for pigs under smallholder production systems in other parts of the tropics.

Economic basis for the Nordic Total Merit Index

Within a group of cooperating countries, all breeding animals are judged according to the same criteria if a joint breeding goal is applied in these countries. This makes it easier for dairy farmers to compare national and foreign elite bulls and may lead to more selection across borders. However, a joint breeding goal is only an advantage if the countries share the same production environment. In this study, we investigated whether the development of a joint breeding goal for each of the major dairy cattle breeds across Denmark, Finland, and Sweden would be an advantage compared with national breeding goals. For that purpose, economic values for all breeding goal traits in the 3 countries were derived, and estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were compared. The economic values within country were derived by means of an objective bio-economic model, and the basic situation in each of the 3 production environments was based on an average dairy cattle herd with regard to production system, production level, and management strategy. The common Nordic economic values for each trait were calculated as the average of that specific trait in each of the 3 production environments. Balanced breeding goals were obtained in all situations because the derived economic values for traits related to health, fertility, milk production, and longevity were sizeable. For both Nordic Red Dairy Cattle and Nordic Holstein, the estimated rank correlations between bulls selected for a national breeding goal and a joint breeding goal were very high. Thus, a joint breeding goal within breed is feasible for Denmark, Finland, and Sweden.

Varying pasture growth and commodity prices change the value of traits in sheep breeding objectives

Breeding programs for livestock require economic weights for traits that reflect the most profitable animal in a given production system. Economic weights are commonly based on average conditions. In pasture based livestock production systems the cost of feed is an important profit driver, but availability of feed from pasture can vary greatly within and between years. Additionally, the price of supplementary feed during periods of feed shortage and the prices for meat and wool vary between years. Varying prices and pasture growth can change the optimal management of the flock affecting profitability. This paper investigates how variation in commodity prices and pasture growth affect the economic values of traits in the breeding objective. We modelled a sheep farm with a self-replacing Merino flock bred for wool and meat in a Mediterranean environment. We optimised management decisions across 5years using dynamic recursive analysis to maximise profit when commodity prices and pasture growth varied annually. Actual pasture growth and wool, meat, and grain prices from 2005 to 2009 were used. Management could adapt to varying pasture growth and commodity prices by changing sheep numbers, age structure of the flock and amount of grain fed to sheep. The economic value of seven traits in the breeding objective were compared for a scenario with average pasture growth and commodity prices over years and a scenario with varying pasture growth and commodity prices over years. Variation in pasture growth and commodity prices decreased average profit and increased the economic value of all breeding goal traits compared to the average scenario. The order of importance of traits stayed the same between varying and average scenarios but the relative importance of traits changed. The economic values that increased the most were for traits that had increased profit with the smallest impact on energy requirements such as yearling live weight, longevity and fibre diameter. Our results showed that it is important to account for variation in feed availability and commodity prices when determining the expected profit and economic values for traits. The results also suggest that whereas variation in pasture growth and commodity prices between years makes the farming operations less profitable, these changing conditions increase the genetic variation in profitability of sheep. Therefore, genetic improvement has more value relative to scenarios where pasture feed supply and prices are constant.

Breakeven prices for recording of indicator traits to reduce the environmental impact of milk production.

A breeding scheme using genomic selection and an indicator trait for environmental impact (EI) was studied to find the most effective recording strategy in terms of annual monetary genetic gain and breakeven price for the recording of indicator traits. The breakeven price shows the investment space for developing a recording system for an indicator trait. The breeding goal consisted of three traits – milk production, functional trait and environmental impact – with economic values of €83, €82 and €-83, respectively. The first scenario included only breeding goal traits and no indicator traits (NoIT). The other scenarios included all three breeding goal traits and one indicator trait (IT) for EI. The indicator traits were recorded on a large scale (stayability after first lactation and stature), medium scale (live weight and greenhouse gases (GHG) measured in the breath of the cow during milking) or small scale (residual feed intake and total enteric methane measured in a respiration chamber). In the scenario with stayability, the genetic gain in EI was over 11% higher than it was in NoIT. The breakeven price of recording stayability was €8 per record. Stayability is easy to record in the national milk recording system, and its use as an indicator trait for EI would not generate any additional recording costs. Therefore, stayability would be a good indicator trait to use to mitigate EI. The highest genetic gain in EI (23% higher compared to NoIT) was achieved when the GHG measured in the breath of the cow was used as indicator trait. The breakeven price for this indicator trait was €29 per record in the reference population. Ideally the recording of a specific indicator trait for EI would take place when: (i) the genetic correlation between the IT and EI is high; and (ii) the number of phenotypic records for the indicator trait is high enough to achieve a moderately high reliability of direct genomic values.

Economic values of production and functional traits, including residual feed intake, in Finnish milk production

Improving the feed efficiency of dairy cattle has a substantial effect on the economic efficiency and on the reduction of harmful environmental effects of dairy production through lower feeding costs and emissions from dairy farming. To assess the economic importance of feed efficiency in the breeding goal for dairy cattle, the economic values for the current breeding goal traits and the additional feed efficiency traits for Finnish Ayrshire cattle under production circumstances in 2011 were determined. The derivation of economic values was based on a bioeconomic model in which the profit of the production system was calculated, using the generated steady state herd structure. Considering beef production from dairy farms, 2 marketing strategies for surplus calves were investigated: (A) surplus calves were sold at a young age and (B) surplus calves were fattened on dairy farms. Both marketing strategies were unprofitable when subsidies were not included in the revenues. When subsidies were taken into account, a positive profitability was observed in both marketing strategies. The marginal economic values for residual feed intake (RFI) of breeding heifers and cows were −25.5 and −55.8 €/kg of dry matter per day per cow and year, respectively. The marginal economic value for RFI of animals in fattening was −29.5 €/kg of dry matter per day per cow and year. To compare the economic importance among traits, the standardized economic weight of each trait was calculated as the product of the marginal economic value and the genetic standard deviation; the standardized economic weight expressed as a percentage of the sum of all standardized economic weights was called relative economic weight. When not accounting for subsidies, the highest relative economic weight was found for 305-d milk yield (34% in strategy A and 29% in strategy B), which was followed by protein percentage (13% in strategy A and 11% in strategy B). The third most important traits were calving interval (9%) and mature weight of cows (11%) in strategy A and B, respectively. The sums of the relative economic weights over categories for RFI were 6 and 7% in strategy A and B, respectively. Under production conditions in 2011, the relative economic weights for the studied feed efficiency traits were low. However, it is possible that the relative importance of feed efficiency traits in the breeding goal will increase in the future due to increasing requirements to mitigate the environmental impact of milk production.

Effect of incorporating greenhouse gas emission costs into economic values of traits for intensive and extensive beef cattle breeds

Ruminants contribute considerably to the Greenhouse Gas (GHG) emissions from agriculture. Genetic improvements have a large potential through permanente and cumulative reductions in emissions. Currently, indirect selection through correlated traits considered in broad breeding goals is the best option for reducing emissions. Breeding goal traits are weighed by their respective economic value (EV). The emission of GHG may be included in the bio-economic model, and the costs of GHG emissions may be estimated and included in the calculation of economic values using a shadow price. In this study emission costs were included in the calculations of economic values for two breed group under three production conditions; (1) semi-intensive (2) completely roughage based (RB) and (3) minimum use of concentrates (MC). Three harvested roughage qualities (early, medium and late cut) were included in the two latter situations, giving a total of 14 situations. EV were estimated for seven functional traits: herd life of cow (HL), age at first calving (AFC), calving interval, stillbirth (S), twinning rate (T), calving difficulty, limb and claw disorders, and for seven production traits: birth weight, carcass weight, carcass conformation, carcass fatness, growth rate from birth to 200 days (weaning), growth rate from 200 to 365 days and growth rate from 365 days to slaughter. Including GHG emissions into calculation of economic values (EV) decreased the relative economic importance of the functional traits HL, AFC, S and T, while increasing the importance of the production traits. However, the overall effect of including GHG emission was small and little reranking between the traits was observed. A sensitivity analysis for increased shadow price showed small effects on the EV. The results suggest that the economic values are robust towards the inclusion of GHG emission costs into the profit equation and also towards increased shadow price. Thus, broad breeding goals for beef cattle including both production and functional traits do not need to be changed considerably to take the emission of GHG into account.

Genomic Selection: Status in Different Species and Challenges for Breeding

Technical advances and development in the market for genomic tools have facilitated access to whole-genome data across species. Building-up on the acquired knowledge of the genome sequences, large-scale genotyping has been optimized for broad use, so genotype information can be routinely used to predict genetic merit. Genomic selection (GS) refers to the use of aggregates of estimated marker effects as predictors which allow improved individual differentiation at young age. Realizable benefits of GS are influenced by several factors and vary in quantity and quality between species. General characteristics and challenges of GS in implementation and routine application are described, followed by an overview over the current status of its use, prospects and challenges in important animal species. Genetic gain for a particular trait can be enhanced by shortening of the generation interval, increased selection accuracy and increased selection intensity, with species- and breed-specific relevance of the determinants. Reliable predictions based on genetic marker effects require assembly of a reference for linking of phenotype and genotype data to allow estimation and regular re-estimation. Experiences from dairy breeding have shown that international collaboration can set the course for fast and successful implementation of innovative selection tools, so genomics may significantly impact the structures of future breeding and breeding programmes. Traits of great and increasing importance, which were difficult to improve in the conventional systems, could be emphasized, if continuous availability of high-quality phenotype data can be assured. Equally elaborate strategies for genotyping and phenotyping will allow tailored approaches to balance efficient animal production, sustainability, animal health and welfare in future.

Evaluating breeding objectives for sow productivity and production traits in Large White Pigs

the objective of the study was to develop and evaluate different breeding objectives for sow productivity and for production traits, using economic selection indices. Genetic parameters were generated using a repeated records model for sow productivity traits and a maternal effects model for production traits, in ASREML. Stochastic simulation models based on a hypothetical 100-sow model were performed for each line, i.e., a dam line and a terminal sire line, respectively, to derive economic values. The traits included in the study were number born alive (NBA), 21-day litter size (D21LS), 21-day litter weight (D21LWT), average daily gain (ADG), feed conversion ratio (FCR), age at slaughter (AGES), dressing percentage (DRESS), lean content (LEAN) and backfat thickness (BFAT). The economic values for LEAN and BFAT were derived using the partial differentiation of the profit function, while those for the other traits were derived using the partial budget approach. An economic value of a trait was the change in profit per unit genetic change in that trait. Breeding objectives were developed with a corresponding selection index, for improvement of that objective. Three combinations of breeding objectives and selection indices were developed for sow productivity traits, while there were 15 combinations for production traits. Responses to selection and economic return were computed for each combination to determine the most appropriate combination for the improvement of the breeding objective traits. The most appropriate index to improve sow productivity consisted of NBA and D21LWT. For production traits, the combination that consisted of a selection index with AGES, DRESS and BFAT, and the breeding objective ADG, DRESS, FCR and LEAN, was considered the most appropriate. Age at slaughter and BFAT were, respectively, included as indicator traits for ADG and LEAN. The recommended breeding objectives were sensitive to changes in economic values, indicating that economic values for breeding goal traits should be updated periodically to ensure proper weighting of traits, hence maximization of economic return.

Reducing methane emissions by including methane production or feed intake in genetic selection programmes for Suffolk sheep

SUMMARYThe use of selective breeding to reduce methane (CH4) emissions is an option for reducing carbon emissions from livestock farming systems. The current study models UK lowland terminal sire (meat) sheep production systems to study the impacts of including CH4 emissions and/or feed intake as breeding objective and selection criteria traits in sheep breeding systems, on the predicted genetic responses of production traits. Nine breeding goal traits and 15 selection index traits were modelled in a Suffolk breeding flock with a deterministic model of trait economic values (EVs). Methane was given an EV equivalent to a carbon price varying from £0 to £538/t CO2-e. When currently used selection indices added feed intake as a breeding objective, CH4 reductions of 0·15 and 0·05 kg CO2-e/sheep/year were predicted when intake was, or was not, measured, respectively, with a zero carbon price. These reductions were relatively insensitive to carbon price. Overall economic (index) response to selection was insensitive to carbon price and increased with higher feed costs, when neither CH4 nor feed intake was measured. When CH4 and/or intake were measured, overall economic responses increased with higher carbon prices, when feed costs were zero. Methane and intake responses were only sensitive to carbon price (whether CH4 and intake were measured or not) when feed costs were zero. To achieve a desired reduction of 0·1 kg CH4/head/year (cumulative 30% reduction in 20 years) when feed costs were zero, CH4 and/or intake needed to be measured. If CH4 was measured, carbon price needed to be >£50/t CO2-e; if intake was measured carbon price needed to be >£100/t CO2-e. Including feed intake as a breeding objective trait with non-zero feed costs should assist in reducing CH4 in breeding programmes. Selective breeding of terminal sheep by index selection has the potential to contribute a reduction of up to 0·27 kg CO2-e per ewe per annum, depending on the traits measured, feed costs and carbon price. This would help meet the UK Government's greenhouse gas reduction targets for farming systems.

Turning science on robust cattle into improved genetic selection decisions

More robust cattle have the potential to increase farm profitability, improve animal welfare, reduce the contribution of ruminant livestock to greenhouse gas emissions and decrease the risk of food shortages in the face of increased variability in the farm environment. Breeding is a powerful tool for changing the robustness of cattle; however, insufficient recording of breeding goal traits and selection of animals at younger ages tend to favour genetic change in productivity traits relative to robustness traits. This paper has extended a previously proposed theory of artificial evolution to demonstrate, using deterministic simulation, how choice of breeding scheme design can be used as a tool to manipulate the direction of genetic progress, whereas the breeding goal remains focussed on the factors motivating individual farm decision makers. Particular focus was placed on the transition from progeny testing or mass selection to genomic selection breeding strategies. Transition to genomic selection from a breeding strategy where candidates are selected before records from progeny being available was shown to be highly likely to favour genetic progress in robustness traits relative to productivity traits. This was shown even with modest numbers of animals available for training and when heritability for robustness traits was only slightly lower than that for productivity traits. When transitioning from progeny testing to a genomic selection strategy without progeny testing, it was shown that there is a significant risk that robustness traits could become less influential in selection relative to productivity traits. Augmentations of training populations using genotyped cows and support for industry-wide improvements in phenotypic recording of robustness traits were put forward as investment opportunities for stakeholders wishing to facilitate the application of science on robust cattle into improved genetic selection schemes.

Genomic selection strategies in dairy cattle: Strong positive interaction between use of genotypic information and intensive use of young bulls on genetic gain

We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.

Genetic improvement of the Kenya Dual Purpose Goat: Influence of economic values and prospects for a practical breeding programme

AbstractThis study determined the influence of economic values on genetic gain and explored the prospects for a practical breeding programme for the Kenya Dual Purpose Goat. The economic values for production traits (live weight (direct), live eight (maternal), milk yield and consumable meat) and functional traits (doe weight, number of kids weaned, kidding frequency and rate, doe weaning and survival rate, pre‐ and post‐weaning survival rate, and residual feed intake for yearlings and does) in the breeding goal addressed the smallholder production system. There was a potential for genetic progress in the breeding‐goal traits even by using simple selection criteria. In the breeding programme, the predicted genetic gains per year for milk yield were 0.737 and 0.047 kg for the sires of sires and dams of dams pathways, respectively. The required population size of 2500 does in the nucleus would be attained within ten years after the start of the breeding programme. The availability of a simple breeding programme for the Kenya Dual Purpose Goat offers enormous opportunities for private large‐scale flocks or for flocks owned by parastatal or research organisations to exploit genetic and economic benefits from the breeding programme. Copyright © 2007 John Wiley & Sons, Ltd

Genetic Progress in Multistage Dairy Cattle Breeding Schemes Using Genetic Markers

The aim of this paper was to explore general characteristics of multistage breeding schemes and to evaluate multistage dairy cattle breeding schemes that use information on quantitative trait loci (QTL). Evaluation was either for additional genetic response or for reduction in number of progeny-tested bulls while maintaining the same response. The reduction in response in multistage breeding schemes relative to comparable single-stage breeding schemes (i.e., with the same overall selection intensity and the same amount of information in the final stage of selection) depended on the overall selection intensity, the selection intensity in the various stages of the breeding scheme, and the ratio of the accuracies of selection in the various stages of the breeding scheme. When overall selection intensity was constant, reduction in response increased with increasing selection intensity in the first stage. The decrease in response was highest in schemes with lower overall selection intensity. Reduction in response was limited in schemes with low to average emphasis on first-stage selection, especially if the accuracy of selection in the first stage was relatively high compared with the accuracy in the final stage.Closed nucleus breeding schemes in dairy cattle that use information on QTL were evaluated by deterministic simulation. In the base scheme, the selection index consisted of pedigree information and own performance (dams), or pedigree information and performance of 100 daughters (sires). In alternative breeding schemes, information on a QTL was accounted for by simulating an additional index trait. The fraction of the variance explained by the QTL determined the correlation between the additional index trait and the breeding goal trait. Response in progeny test schemes relative to a base breeding scheme without QTL information ranged from +4.5% (QTL explaining 5% of the additive genetic variance) to +21.2% (QTL explaining 50% of the additive genetic variance). A QTL explaining 5% of the additive genetic variance allowed a 35% reduction in the number of progeny tested bulls, while maintaining genetic response at the level of the base scheme. Genetic progress was up to 31.3% higher for schemes with increased embryo production and selection of embryos based on QTL information. The challenge for breeding organizations is to find the optimum breeding program with regard to additional genetic progress and additional (or reduced) cost.

A bio-economic approach to estimating economic values for UK hill sheep

Profitability of sheep production systems depends on several different animal characteristics rather than a single trait. Economic selection indexes combine information from more than one trait into an overall score, to maximise genetic gain. Economic values (EVs) are required for each trait in the breeding goal so that selection emphasis is proportional to the economic importance of each trait. Defining clear breeding goals is more complex for hill breeds than for other sectors of the sheep industry because they provide breeding females in addition to lambs for slaughter. The aims of this paper are to i) describe how EVs for breeding goal traits suitable for UK hill sheep were derived for a combination of carcass, maternal and ‘sustainability’ traits using a bio-economic model, and ii) show how these EVs vary between different production systems as a result of the differences in the physical constraints of farm size, pasture availability and the biological limits of sheep in extensive rearing environments.

Economic values in dairy cattle breeding, with special reference to functional traits. Report of an EAAP-working group

Abstract The aims of this working group report are: (a) to present the definition of a set of breeding goal traits, (b) to discuss the methodology in deriving economic values, (c) to present a summary of literature on economic values of functional traits, (d) to discuss a possible justification of differences in breeding goal definition for countries, regions, and/or individual farms, and (e) to discuss possible future trends potentially influencing breeding goals by changing economic values. A genetically and socio-economically balanced selection on production (milk and beef) and functional traits (health, fertility, efficiency of feed utilisation and milkability) in dairy cattle requires correct economic values. The derivation of economic values requires a good theoretical basis, proper methodology in terms of models including physiological modelling of animal production, farm economics and social aspects, and appropriate assumptions on future production circumstances. Breeding goal definition is a research area of ongoing interest; knowledge on modelling is improving, and (uncertain future) production circumstances are continuously changing. Although research has been undertaken for a long period, the practical integration of functional traits in dairy cattle breeding goals is still a major challenge for animal breeders. The working group considers the inclusion of social aspects in deriving economic values for functional traits to be a major challenge for animal breeders in the near future.

Optimization of selection for food intake capacity in pigs

AbstractOptimum selection directions for averagead libitumfood intake capacity (FIC) in growing pigs were derived by means of a biological growth model based on the linear/plateau relationship between protein deposition and food intake. Breeding goal traits were minimum fat to protein deposition ratio (R), maximum protein deposition rate (Pdmax) and FIC. The selection index included FIC, daily weight gain (DG), backfat thickness and proportion of lean meat in the carcass of a full-sib.Selection indexes were developed for three alternative levels of FIC. If FIC was too low to realize Pdmax, the economic weight of FIC was positive and optimal selection emphasis should be mainly on FIC, resulting in a rapid increase of DG. If FIC was higher than necessary to realize Pdmax, the economic weight of FIC was negative, and short-term selection resulted in increase of carcass leanness but decrease of FIC and DG. If FIC was just sufficient to realize Pdmax, selection should be for R and Pdmax. In this third alternative, the gain in FIC should follow the gain in R and Pdmaxin an optimal way and selection should best be carried out with a desired gains index, which results in improvement of DG and carcass leanness.It was shown that, in cases where FIC was higher than necessary to realize Pdmax, selection with a desired gainsindex should be preferred because this was more profitable in the long term. From the model calculations, it followed that future profit from selection of growing pigs for production traits is likely to decline because of the necessity to increase FIC.

Lifetime Performance in Dairy Cattle. Genetic Parameters and Expected Improvement from Selection

Abstract The aims of this study were (1) to estimate genetic parameters for some biological and economic measures taken early in life, and subsequent lifetime economic performance traits, and (2) to calculate the expected genetic gain in lifetime performance from selection based on various selection indices. The breeding goal traits were: net income per year of productive life and lifetime income divided by cost. The early economic index traits were: net income per year and income divided by cost, measured during the first lactation or the first 15 months of productive life. Economic traits were calculated on the basis of production, fertility, and disease data on around 10000 cows having an opportunity to produce for at least 5 years. Variance-covariance components were estimated by applying a multiple-trait restricted maximum likelihood procedure to a sire model. The heritability estimates were around 40% for yield in 1st lactation, 20–30% for the early economic measures, and 10–15% for lifetime income/cost. Genetic correlation estimates with lifetime income/cost were 0.75–0.9 for early economic measures, and 0.6–0.7 for 1st and 2nd lactation yields. The annual genetic gain in lifetime economic performance was greater with selection on only 1st lactation yields than when the generation interval was extended to include 120-day yields in 2nd lactation. However, selection on the various early economic measures was up to 12% better than selection on only 1st lactation yields. A potential problem with a positive bias in the estimated correlation between early and lifetime performance exists. This bias is caused by the effect of milk yield on longevity, mediated through the management system.