Genetic and phenotypic associations of feed efficiency with growth and carcass traits in Australian Angus cattle
Genetic and phenotypic parameters for feed efficiency, growth, and carcass traits for Australian Angus beef cattle were estimated. Growth traits included birth weight (BWT), 200-d weight (200dWT), 400-d weight (400dWT), and 600-d weight (600dWT). Traits associated with feed efficiency were average daily weight gain (ADG), metabolic midweight, average of daily feed intake (FI), feed conversion ratio (FCR), residual feed intake (RFI), and residual gain (RG). Carcass traits involved were carcass eye muscle area (CEMA), carcass intramuscular fat (IMF), subcutaneous fat depths at the 12th/13th rib (CRIB), rump P8 fat depth (P8FAT), and carcass weight (CWT). For growth traits, heritability estimates ranged from 0.14 +/- 0.03 for 200dWT to 0.48 +/- 0.06 for 600dWT. For feed efficiency traits, direct heritability estimates for FI, FCR, RFI, and RG were 0.55 +/- 0.08, 0.20 +/- 0.06, 0.40 +/- 0.07, and 0.19 +/- 0.06, respectively. High heritability estimates were observed for CEMA, IMF, P8FAT, and CWT of 0.52 +/- 0.09, 0.61 +/- 0.09, 0.55 +/- 0.09, and 0.66 +/- 0.09, respectively. Strong positive genetic correlations were found for FI with 200dWT, 400dWT, and 600dWT of 0.68 +/- 0.09, 0.42 +/- 0.11, and 0.61 +/- 0.07, respectively. Weak genetic correlations were observed between RFI and growth traits. For carcass traits, genetic correlations between RFI and CEMA, IMF, CRIB, P8FAT, CWT were -0.19 +/- 0.14, 0.31 +/- 0.14, 0.18 +/- 0.16, 0.24 +/- 0.13, and 0.40 +/- 0.12, respectively. There was a tendency for low to moderate unfavorable genetic associations between feed efficiency traits, evaluated as RFI and RG, with growth and carcass traits. This implies that selection for RFI would have slight negative impacts on growth and reduce carcass quality. To avoid this, it would be necessary to build selection indices to select feed efficient animals without compromising growth and meat quality.
Remodelling the genetic evaluation of NFI in beef cattle - Part 2: Shortening the length of the feed intake test
BREEDPLAN net feed intake (NFI) EBV is derived from a phenotypic regression based on a 70-day feed intake test. Genetic NFI (NFIg) EBVs have been proposed as an alternative EBV and this recent development may also allow for a shortened feed intake test period. This study used feed intake records of 3,088 Angus steers from the full 70-day test and compared them to daily feed intake (DFI) from shortened test periods. Results showed DFI from shortened test periods had similar means but increased phenotypic variation. Phenotypic correlation with DFI from the full test period decreased as the test period decreased in weekly intervals and ranged between 0.75 and 0.99. NFIg EBVs were predicted using DFI from different length tests. The mean of all NFIg EBVs was close to zero, but the EBV standard deviation increased as the test period decreased. Pearson correlations between NFIg EBVs from a full test period and reduced test periods ranged between 0.73 and 0.99, the regression slope of NFIg from reduced test periods on NFIg from the full test period ranged between 0.73 and 0.95, and the bias ranged between 0.00 and 0.02. These results indicate that as the test period decreases, the spread of EBVs increases, resulting in extreme animals having overestimated NFIg EBVs. A shortened DFI test period could be used to estimate NFIg EBVs.
Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle
The objectives of this study were to compare different models for analysing body weight (BW) and average daily feed intake (ADFI) data collected during a 70‐day feedlot test period and to explore whether genetic parameters change over time to evaluate the implications of selection response. (Co)variance components were estimated using repeatability and random regression models in 2,071 Angus steers. Models included fixed effects of contemporary group, defined as herd-year-observation_date-age, with additive genetic and permanent environmental components as random effects. Models were assessed based on the log likelihood, Akaike's information criterion and the Bayesian information criterion. For both traits, random regression models (RRMs) presented a better fit, indicating that genetic parameters change over the test period. Using a two-trait RRM, the heritability from day 1 up to day 70 for BW increased from 0.40 to 0.50, while for ADFI, it decreased from 0.44 to 0.33. The genetic correlation increased from 0.53 at day 1 up to 0.79 at day 70. Selection based on an index assuming no change in genetic parameters would yield a 2.78%-3.13% lower selection response compared to an index using parameters estimated with RRMs and assuming these genetic parameters are correct. Results imply that it may be beneficial to implement RRMs to account for the change of parameters across the feedlot period in feed efficiency traits.
Determination of optimum weighting factors for single-step genetic evaluation via genetic variance partitioning
It is important in single-step genetic evaluations to use appropriate lambdas (λ) for calculating weighted average of NRM (numerator relationship matrix) and GRM (genomic relationship matrix) in joint relationship matrix. λ is usually estimated using a single-trait cross-validation procedure. However, it can be shown that a univariate single-step model applying a scalar λ is simply a condensed form of an extended model containing two genetic factors, factor H~N(0, H) and factor A~N(0, A), where the partitioning of the total genetic variance reflects λ. For multivariate single-step genetic evaluation, this model condensation implies that all involved genetic variances may yield the same λ, which is highly unlikely. Hence, it is required to estimate λ by accounting for its heterogeneity using the extended model for variance component estimation. This study used an extended single-step model to estimate variances and λs for calving difficulty (CD), gestation length (GL), and birth weight (BW) using Australian Angus data. A total of 129,851 animals with 45,575 genotypes were analysed. Initial variances obtained from a pedigree-only model were then used as starting values for the extended single-step model assigning 90% of the genetic variance to factor A and 10% to factor H. Since CD is a categorical trait with three categories, a threshold model-Gibbs sampling method was used to estimate variances. Heritability estimates for the extended single-step model were very similar to those from the pedigree only model implying that the single-step model was not explaining more variation in the data than the pedigree only model. For CD, GL, and BW, the total heritability estimates were 0.39 ± 0.04, 0.68 ± 0.02, and 0.44 ± 0.01, respectively. For the same traits, the total maternal heritability estimates were 0.17 ± 0.02, 0.11 ± 0.01, and 0.09 ± 0.01, respectively. In contrast, to the Gibbs sampling starting values, the genetic variance was partitioned between A and H such that direct genetic λ estimates for CD, GL, and BW were 0.36 ± 0.05, 0.62 ± 0.03, 0.75 ± 0.03, respectively. Maternal genetic λ estimates ranged from 0.01 ± 0.01 (for BW) to 0.05 ± 0.01 (for CD). The results imply that λ values are heterogeneous in multivariate single-step genomic evaluation. Further studies are needed to investigate the consequences of using heterogenous λ values for direct genetic and maternal genetic components in multivariate single-step evaluation in terms of model dimensions, solver convergence rate, and model forward predictive ability.
Remodelling the genetic evaluation of NFI in beef cattle - Part 1: Developing an equivalent genetic model
Net feed intake (NFI) is the residual portion of daily feed intake (DFI) not explained by growth or maintenance requirements. The NFI phenotype (NFIp) is based on a 70-day test period where DFI and fortnightly weights (to calculate average daily gain (ADG) and maintenance as metabolic mid-weight (MMWT)) are measured. Recording NFIp is costly, and shortening the test length would be advantageous. However, research has shown that ADG cannot be accurately measured from a shortened test. Genetic NFI EBVs (NFIg) were calculated using DFI EBV adjusted for ADG and MMWT EBV and were shown to have a Pearson correlation of 0.99 with the NFIp EBV from 3,088 Angus steers. The regression slope between NFIg and NFIp EBVs was 1.14. Alternative NFIg models where growth and maintenance requirements were obtained from BREEDPLAN live weight traits instead of live weights recorded in the test period, demonstrated high Pearson correlations (r=0.87 to 0.93) and regression slopes between 0.63 and 0.97 with NFIp EBVs. Results suggest that genetic NFI EBVs can be obtained, with growth and maintenance requirements being determined from BREEDPLAN live weight traits. This provides the opportunity to determine if the length of the test to measure DFI can be shortened, reducing the cost of recording NFI per animal.
Estimation of optimum polygenic and genomic weights in single-step genetic evaluation of carcass traits in Australian Angus beef cattle
Optimum polygenic and genomic weights enhance the accuracy of breeding value estimates in single-step genomic evaluations. This study estimated the contribution from marker information to total additive genetic variation referred as λ using an extended single-step model in a multi-trait variance component estimation based procedure using data for six Australian Angus carcase traits. The λ for these traits ranged from 0.54 (for carcass intramuscular fat) to 0.79 (for carcass eye muscle area). Heritabilities were similar between the pedigree only and the extended single-step multi-trait model when using the total genetic variance, and ranged from 0.37 (for carcass rib fat) to 0.53 (for carcass weight), suggesting that the single-step model did not explain more genetic variance than pedigree based models. Results suggest that the scalar λ in the current single-step routine evaluation could be replaced by an extended single-step model allowing for different proportions of the additive genetic co-variance explained by markers for all elements of the genetic co-variance matrix.
Genome-Wide Association Studies for Body Weight and Average Daily Feed Intake During the Feedlot Test Period
Feed efficiency component traits such as body weight (BW) and daily feed intake are economically relevant traits in beef cattle breeding programs. The objectives of this work were to identify genomic regions associated with BW and average daily feed intake (ADFI) during the feedlot period, and to evaluate whether these genetic variants for each trait were consistent over the 70-day test period. Data on 2070 Angus steers were used to estimate (co)variance components using the genomic relationship matrix (gREML) fitted in ASReml. For the studied traits, a two-trait repeatability (TT-REPM) and a two-trait random regression (TT-RRM) models were performed. SNP-effects for the TT-REPM and TT-RRM were estimated using a post analysis back-solving approach using the genomic estimated breeding values from each model respectively. For each trait, results were validated with single-trait animal models (ST-ANIMs) at the beginning and at the end of the test period using single-SNP regression in the GCTA software. Results from the genome-wide association studies (GWAS) using TT-REPM and TT-RRM were similar to the conventional approach using the ST-ANIMs. For all models, the variants rs43350564 and rs109326204 presented the strongest association with BW and ADFI, respectively. The identified SNP effects remained constant throughout the feedlot test period and could be useful for understanding the biology of feed efficiency. Further studies with more data and possibly with longer feed lot test periods are needed to investigate the effect of genomic regions for feed efficiency traits over the feedlot trajectory.
Genetic and Phenotypic Parameters for Feed Efficiency Traits in Australian Angus Beef Cattle
Feed intake represents a major cost to all animal production systems. Increasing the efficiency in which animals turn this feed into product can be a major goal in many animal breeding programs. Consequently, Australian beef producers have been measuring many traits associated with feed efficiency in an attempt to increase the accuracy of selection, precision of genetic parameters estimates and ultimately increase the amount of genetic gain achieved. The objective of this study was to estimate genetic and phenotypic parameters for traits associated with feed efficiency from records on 1614 Angus Steers from the Australian Beef Information Nucleus (BIN). Traits analysed included Average Daily Weight Gain (ADG), Metabolic Mid-Weight (MMWT), Daily Feed Intake (FI), Feed Conversion Ratio (FCR) and Residual (or Net) Feed Intake (RFI). Parameters were estimated using bivariate animal models in ASReml. Heritability estimates ranged from 0.12 ±0.06 for FCR to 0.49 ±0.09 for FI. High genetic correlations were estimated between FI and RFI (0.83±0.05) and FI and ADG (0.81±0.08). Significant genetic correlation also existed between ADG and MMWT (0.65±0.12) and between MMWT and FI (0.68±0.08). Heritability estimates show that there would be a favourable response to selection for the efficiency traits in this population. The positive and unfavourable genetic correlation between ADG and RFI, suggest that improving RFI would result in lower ADG. Given this, further studies are required to investigate genetic associations between efficiency traits and other economically important traits, in addition to examine new ways of utilizing feed efficiency information in breeding programs.
Feed Efficiency in Beef Cattle Breeding Programs
The conversion of feed into usable products, also known as feed efficiency (FE), is important given the necessity to increase quality food production. This concept is also important given the environmental sustainability and profitability of the beef production systems. The present thesis analysed different aspects involving FE and their inclusion in beef cattle breeding programs. Thus, this work aimed to increase the understanding of genetic variability of the FE traits and the inclusion of residual feed intake (RFI) into genomic assisted beef cattle breeding programs.
The first research chapter was oriented to better understand the genetic variability of the main FE traits and their association with growth and meat quality traits in an Angus population. The analysed FE component traits were daily weight gain, metabolic midweight, average of daily, feed intake, feed conversion ratio, RFI, and residual gain (RG), evaluated during a 70-day feedlot test period. In this chapter, it was concluded that selection on RFI would have a negative impact in growth and carcass traits. Therefore, it was suggested that selection only for RFI would have negative impacts on growth and meat quality in the studied population.
The second research chapter compared repeatability (REPM) and random regression models (RRM) on feed efficiency component traits during the feedlot test period. Unlike the REPM, the RRM can accommodate changes in parameters of those traits over duration of the test period. First-order RRM applied to body weight (BW) and average daily feed intake (ADFI), shown that genetic parameters tend to change during the feedlot period. By comparing these models, it was concluded that ignoring the change in parameters, regardless of feed costs, resulted in a loss of selection response of approximately 3%
The third research chapter analysed REPM and RRM when using genomic information. Genomic variants associated with BW and ADFI variation were identified and a change of their effect during the feedlot test period was evaluated. For both traits, RRM presented the best fit and only one genomic region was detected with a constant effect throughout the 70-d feedlot test period. For BW and ADFI, the strongest associated variants were rs43350564 and rs109326204, located on chromosomes 20 and 5, respectively. These identified SNP may help to unravel the biology of FE and can be used for more accurate genomic prediction of breeding values.
The final research chapter evaluated various realistic multi-trait selection strategies incorporating RFI, as well as including the use of genomic selection (GS). Here it was concluded that selecting on RFI via a genomic test yielded the largest increase in selection accuracy and overall responses of 0.48 and 64.9%, respectively. Finally, it was concluded that including feed efficiency increased the $ net return without compromising meat quality and cow condition score in the beef cattle breeding programs.
Modelling live weight and feed intake during a feedlot test period in Australian Angus Cattle
Feed efficiency related traits modelled as repeated measurements may allow an increased precision of genetic parameters estimates, accuracy of selection and a subsequent increase in genetic gain. The objective of the present work was to compare different models for live weight (LW) and daily feed intake (DFI) during a 78-day feedlot test period using 1763 Angus Steers from the Australian Beef Information Nucleus (BIN). Genetic parameters were estimated using repeatability and random regression models for single and multiple traits. Using the bivariate random regression model, the heritabilities for LW and DFI were in the range of 0.46 to 0.56, and from 0.43 to 0.48, respectively; with repeatabilities ranging from 0.92 to 0.95, and from 0.69 to 0.72, respectively. The corresponding genetic correlation increased from 0.50 at day 1 up to 0.85 at day 78. Based on the Akaike’s information criterion and Bayesian information criterion values, random regression models presented smaller values indicating the best fit in comparison to the repeatability models. In practice, random regression analysis should be adopted for predicting more precisely the genetic merit in feed efficiency related traits. A positive and unfavourable genetic correlation was estimated for the studied traits, suggesting that it would be necessary to build a selection index to optimally select for both traits.