Testing strategies of pigs for measurement of feed efficiency: relative improvement in feed efficiency from relying solely on indirect selection compared to indirect selection for faster growth rate and higher percent lean

The objective of this study was to compare different testing strategies of pigs involving direct measurement of feed efficiency in pig. Using simulated data software, we collected individual information for FCR and grouped trials into direct and indirect measurement. The direct FCR included measurement of FCR in both sexes (100M-100F), in boars only (100M), in 50% of boars and 50% of gilts (50M-50F), and in only 50% of boars (50M-0F). Direct measures of FCR (0M-0F) in indirect selection were not used. The highest and the lowest genetic regression responses in FCR were observed for 100M-100F and indirect selection scenarios (0M-0F). Among direct selection, the increase in genetic progress was directly related to the percent of pigs tested for FCR. Using FCRs records more than 50% (50M-50F or 100M) can raise response higher than 80%. Meanwhile, 0M-0F showed a loss in genetic gain more than 50% when compared with 100M-100F. These results suggest using direct measurements on at least 50% of all pigs for genetic evaluation in FCR.

Single and multitrait estimates of breeding values for survival using sire and animal models

Survival data were simulated under the Weibull model in a half-sib family design, and about 50% of the records were censored. The data were analysed using the proportional hazard model (PHM) and, after transformation to survival scores, using a linear and a binary (logit) model (LIN and BIN, respectively), where the survival scores are indicators of survival during time period t given survival up to period t – 1. Correlations between estimated and true breeding values of sires (accuracies of selection) were very similar for all three models (differences were smaller than 0·3%). Daughter effects were however less accurately predicted by the LIN model, i.e.taking proper account of the distribution of the survival data yields more accurate predictions of daughter effects. The estimated variance components and regressions of true on estimated breeding values were difficult to compare for the LIN models, because estimated breeding values were expressed as additive effects on survival scores while the simulated true breeding values were expressed on the underlying scale. Also the differences in accuracy of selection between sire and animal model breeding value estimates were small, probably due to the half-sib family design of the data. To estimate breeding values for functional survival, i.e. the component of survival that is genetically independent of production (here milk yield), two methods were compared: (i) breeding values were predicted by a single-trait linear model with a phenotypic regression on milk yield; and (ii) breeding values were predicted by a two-trait linear model for survival and milk yield, and breeding values for survival corrected for milk yield were obtained by a genetic regression on the milk yield breeding value estimates. Both methods yielded very similar accuracies of selection for functional survival, and are expected to be equivalent.

Response from marker-assisted selection when various proportions of animals are marker typed: a multiple trait simulation study relevant to the sheepmeat industry

A simulation study, based on a closed sheepmeat breeding nucleus and incorporating marker-assisted selection (MAS), was used to evaluate response when different proportions of animals were marker typed. Two traits were included in the simulation: trait 1, a production trait where phenotypes were available prior to selection, and trait 2, a carcass trait where phenotypic information was not available on breeding animals. Selection on an index which comprised estimated breeding values (EBVs) for both traits was possible by calculating EBVs for trait 2 as the sum of EBVs for a polygenic component, obtained from a genetic regression, and EBVs for a major gene component, obtained by inferring genotypes at a major gene locus from a linked marker locus. Different marker typing strategies were evaluated. These differed in the criteria used to select progeny for typing, and in the proportion of male and female progeny selected. Typing progeny that were likely to be used as a breeding animal, but excluding those whose marker genotype could be predicted with reasonable certainty, was an efficient genotyping strategy. Close to maximum gain at the major gene locus was achieved when only a proportion of animals were marker typed (for example 90% of maximum response was achieved with a little over one-third of the selection candidates marker typed). This indicates the potential for substantial savings in relation to the cost of marker typing in commercial breeding flocks.

Genetic and economic relationships between somatic cell count and clinical mastitis and their use in selection for mastitis resistance in dairy cattle

Clinical mastitis (CM) and monthly test-day somatic cell count (SCC) records on Holstein cows were used to investigate the genetic and economic relationship of lactation average (of natural logarithms of) monthly test-day SCC (LSCC) with CM. After editing, there were 23663 lactation records on 17937 cows from 257 herds. Three groups of herds were first identified as having low (L), medium (M) and high (H) incidences of CM from the original or pooled (P) data set. Genetic parameters were estimated for the original and three data sub-sets (derived from the three herd groups). Expected genetic responses to selection against CM were calculated using genetic parameters of each data set separately, with an adapted version of the UK national index (£PLI-profitable lifetime index). Indirect economic values of SCC (EVSCC) were calculated as the direct cost of CM per cow per lactation weighted by the genetic regression coefficient of CM lactation records on their sires’ predicted transmitting ability for SCC (PTASCC). All genetic regression analyses were based on linear and threshold-liability models. Heritabilities and repeatabilities, respectively, were 0034 and 0·111 for CM and 0120 and 0·347 for LSCC in the original data set. Genetic, permanent environmental, residual and phenotypic correlations between CM and LSCC for the original (pooled) data set were 0·70, 0·44, 013 and 0·20, respectively. Parameter estimates for the three herd groups differed, with magnitude of the estimates increasing with increase in incidence from L to H herd groups. The EVSCCper unit of PTASCCfor L, M, H and P herd groups, respectively, were £004, £0·15, £0·33 and £018 on the observed and £0·86, £0·96, £1·22 and £110 on the underlying-liability scales. Selection for mastitis resistance, using SCC as an indicator trait in an extended version of £PLI, resulted in a selection response of 0·9, 21, 1·7 and 1·9 more cases per 100 cows after 10 years of selection in L, M, H and P herd groups, respectively. These results suggest that genetic responses to selection for CM resistance as well as the EVSCCare specific to herd incidence and hence would be appropriate for customized selection indexes. The increase in CM cases was greater when CM was excluded from the £PLI (2·8v1·9), hence it is recommended that CM should be included in the breeding goal in order to arrest further decline or to make improvement in genetic resistance to clinical mastitis.

Effect of relationship between mastitis and somatic cell count on genetic selection for mastitis resistance in dairy cattle

Differences in banding scales for milk quality penalties, as determined by bulk tank somatic cell count (SCC), prevent the use of a single economic value for SCC in an overall economic-genetic selection index (Veerkamp et al., 1998) such as, Profitable Lifetime Index or £PLI. But SCC could be used as a predictor of mastitis as genetic correlation estimates between mastitis and SCC are medium to high (review of Mrode and Swanson, 1996). This suggests that, although deriving a direct single economic value (EV) for SCC based on bulk tank SCC is difficult, a single financial value could still be assigned to SCC based on its relationship with mastitis. Here we use a genetic regression method to calculate the EV of SCC (EVSCC) as a predictor of mastitis. However, the dependency of regression coefficients on mastitis incidence (p) could make such EVSCC variable. The main objective of this study was to evaluate the impact of such relationship on EVSCC and genetic selection in dairy cattle using predicted transmitting abilities of SCC (PTASCC).