A new method to estimate residual feed intake in dairy cattle using time series data.

In dairy, the usual way to measure feed efficiency is through the residual feed intake (RFI) method. However, this method is, in its classical form, a linear regression, which, by construction, does not take into account the evolution of the RFI components across time, inducing approximations in the results. We present here a new approach that incorporates the dynamic dimension of the data. Using a multitrait random regression model, the correlations between milk, live weight, DM intake (DMI) and body condition score (BCS) were investigated across the lactation. In addition, at each time point, by a matrix regression on the variance-covariance matrix and on the animal effects from the three predictor traits, a predicted animal effect for intake was estimated, which, by difference with the actual animal effect for intake, gave a RFI estimation. This model was tested on historical data from the Aarhus University experimental farm (1 469 lactations out of 740 cows). Correlations between animal effects were positive and high for milk and DMI and for weight and DMI, with a maximum mid-lactation, stable across time at around 0.4 for weight and BCS, and slowly decreasing along the lactation for milk and weight, DMI and BCS, and milk and BCS. At the Legendre polynomial coefficient scale, the correlations were estimated with a high accuracy (averaged SE of 0.04, min = 0.02, max = 0.05). The predicted animal effect for intake was always extremely highly correlated with the milk production and highly correlated with BW for the most part of the lactation, but only slightly correlated with BCS, with the correlation becoming negative in the second half of the lactation. The estimated RFI possessed all the characteristics of a classical RFI, with a mean at zero at each time point and a phenotypic independence from its predictors. The correlation between the averaged RFI over the lactation and RFI at each time point was always positive and above 0.5, and maximum mid-lactation (>0.9). The model performed reasonably well in the presence of missing data. This approach allows a dynamic estimation of the traits, free from all time-related issues inherent to the traditional RFI methodology, and can easily be adapted and used in a genetic or genomic selection context.

Genome scan for postmortem carcass traits in Nellore cattle.

Carcass traits measured after slaughter are economically relevant traits in beef cattle. In general, the slaughter house payment system is based on HCW. Ribeye area (REA) is associated with the amount of the meat in the carcass, and a minimum of backfat thickness (BFT) is necessary to protect the carcass during cooling. The aim of this study was to identify potential genomic regions harboring candidate genes affecting those traits in Nellore cattle. The data set used in the present study consisted of 1,756 Nellore males with phenotype records. A subset of 1,604 animals had both genotypic and phenotypic information. Genotypes were generated based on a panel with 777,962 SNPs from the Illumina Bovine HD chip. The SNP effects were calculated based on the genomic breeding values obtained by using the single-step GBLUP approach and a genomic matrix re-weighting procedure. The proportion of the variance explained by moving windows of 100 consecutive SNPs was used to assess potential genomic regions harboring genes with major effects on each trait. The top 10 non-overlapping SNP-windows explained 8.72%, 11.38%, and 9.31% of the genetic variance for REA, BFT, and HCW, respectively. These windows are located on chromosomes 5, 7, 8, 10, 12, 20, and 29 for REA; chromosomes 6, 8, 10, 13, 16, 17, 18, and 24 for BFT; and chromosomes 4, 6, 7, 8, 14, 16, 17, and 21 for HCW. For REA, there were identified genes ( and ) involved in the cell cycle biological process which affects many aspects of animal growth and development. The and genes, both from AA transporter family, was also associated with REA. The AA transporters are essential for cell growth and proliferation, acting as carriers of tissue nutrient supplies. Various genes identified for BFT (, , , , , and ) have been associated with lipid metabolism in different mammal species. One of the most promising genes identified for HCW was the . There is evidence, in the literature, that this gene is located in putative QTL affecting carcass weight in beef cattle. Our results showed several genomic regions containing plausible candidate genes that may be associated with carcass traits in Nellore cattle. Besides contributing to a better understanding of the genetic control of carcass traits, the identified genes can also be helpful for further functional genomic studies.

Genetic parameter estimates for carcass traits and visual scores including or not genomic information.

The objective of this study was to determine whether visual scores used as selection criteria in Nellore breeding programs are effective indicators of carcass traits measured after slaughter. Additionally, this study evaluated the effect of different structures of the relationship matrix ( and ) on the estimation of genetic parameters and on the prediction accuracy of breeding values. There were 13,524 animals for visual scores of conformation (CS), finishing precocity (FP), and muscling (MS) and 1,753, 1,747, and 1,564 for LM area (LMA), backfat thickness (BF), and HCW, respectively. Of these, 1,566 animals were genotyped using a high-density panel containing 777,962 SNP. Six analyses were performed using multitrait animal models, each including the 3 visual scores and 1 carcass trait. For the visual scores, the model included direct additive genetic and residual random effects and the fixed effects of contemporary group (defined by year of birth, management group at yearling, and farm) and the linear effect of age of animal at yearling. The same model was used for the carcass traits, replacing the effect of age of animal at yearling with the linear effect of age of animal at slaughter. The variance and covariance components were estimated by the REML method in analyses using the numerator relationship matrix () or combining the genomic and the numerator relationship matrices (). The heritability estimates for the visual scores obtained with the 2 methods were similar and of moderate magnitude (0.23-0.34), indicating that these traits should response to direct selection. The heritabilities for LMA, BF, and HCW were 0.13, 0.07, and 0.17, respectively, using matrix and 0.29, 0.16, and 0.23, respectively, using matrix . The genetic correlations between the visual scores and carcass traits were positive, and higher correlations were generally obtained when matrix was used. Considering the difficulties and cost of measuring carcass traits postmortem, visual scores of CS, FP, and MS could be used as selection criteria to improve HCW, BF, and LMA. The use of genomic information permitted the detection of greater additive genetic variability for LMA and BF. For HCW, the high magnitude of the genetic correlations with visual scores was probably sufficient to recover genetic variability. The methods provided similar breeding value accuracies, especially for the visual scores.

Associations between single nucleotide polymorphisms and carcass traits in Nellore cattle using high-density panels.

The objective of this study was to evaluate associations between single nucleotide polymorphism (SNP) markers and carcass traits measured postmortem in Nellore cattle. Records of loin eye area (LEA) and backfat thickness (BF) from 740 males and records of hot carcass weight (HCW) from 726 males were analyzed. All of the animals were genotyped using the BovineHD BeadChip. Association analyses were performed by the restricted maximum likelihood method that considered one SNP at a time. Significant SNPs were identified on chromosomes 2 and 6 for LEA and on chromosomes 7, 1, and 2 for BF. For HCW, associations with SNPs were found on chromosomes 13, 14, and 28, in addition to genome regions that were directly related to this trait, such as the EFCAB8 and VSTM2L genes, and to bone development (RHOU). Some SNPs were located in very close proximity to genes involved in basal metabolism (BLCAP, NNAT, CTNNBL1, TGM2, and LOC100296770) and the immune system (BPI).

Genetic association of growth traits with carcass and meat traits in Nellore cattle.

The aim of this study was to estimate genetic and phenotypic associations of growth traits with carcass and meat traits in Nellore cattle. Data from male and female animals were used for weaning weight (WW; N = 241,416), yearling weight (YW, N = 126,596), weight gain from weaning to yearling (GWY, N = 78,687), and yearling hip height (YHH, N = 90,720), respectively; 877 male animals were used for hot carcass weight (HCW) and 884 for longissimus muscle area (LMA), backfat thickness (BT), marbling score (MS), and shear force (SF). The variance components were estimated by the restricted maximum likelihood method using three-trait animal models that included WW. The model for WW included direct and maternal additive genetic, maternal permanent environmental, and residual effects as random effects; contemporary group as fixed effects; and age of dam at calving and age of animal as covariates (linear and quadratic effects). For the other traits, maternal effects and the effect of age of dam at calving were excluded from the model. Heritability ranged from 0.10 ± 0.12 (LMA) to 0.44 ± 0.007 (YW). Genetic correlations ranged from -0.40 ± 0.38 (WW x LMA) to 0.55 ± 0.10 (HCW x YW). Growth, carcass, and meat traits have sufficient genetic variability to be included as selection criteria in animal breeding programs.

Genetic association between body composition measured by ultrasound and visual scores in Brazilian Nelore cattle.

The objective of this study was to evaluate the genetic variability of body composition traits measured by ultrasound, growth traits, and visual scores as well as their genetic associations in Nelore cattle. A total of 9,765, 13,285, 13,061, 12,811, 3,484, 3,484, 3,483, and 3,303 records of weight at time of ultrasound measure (W550), 12th-13th rib LM area (LMA), backfat thickness (BF), rump fat thickness (RF), visual scores for body structure (BS), finishing precocity (FP), muscling (MS), and sheath and navel characteristics (SN), respectively, were used. The model included contemporary group (defined as year and season of birth, sex, and management group) as a fixed effect and age of dam at calving and age of the animal (linear and quadratic effects) as covariates. The direct additive genetic effect was included as a random effect. The analyses also included 46,157 observations of BW adjusted to 120 d. The (co)variance components were estimated by the restricted maximum likelihood method using a multitrait animal model. Heritability estimates for W550, LMA, BF, RF, BS, FP, MS, and SN were 0.37 ± 0.030, 0.33 ± 0.03, 0.24 ± 0.02, 0.28 ± 0.03, 0.24 ± 0.04, 0.38 ± 0.05, 0.29 ± 0.05, and 0.38 ± 0.06, respectively. The estimated genetic correlations between visual scores and LMA were moderate and positive, ranging from 0.37 to 0.44. Similar results were obtained for the estimated genetic correlations between FP and MS with fat thickness measures (BF and RF). Low genetic correlations were estimated between SN and BS and between SN and the body composition traits, indicating that selection for body composition traits and BS will not affect sheath and navel size. The estimated genetic correlations between weight adjusted to 120 d of age (W120) and W550 and BS were high (0.87 and 0.91) and moderate with LMA (0.49 and 0.55), FP (0.37 and 0.41), and MS (0.47 and 0.55). The visual scores and ultrasound-measured body composition traits have enough genetic variation for selection purposes in Nelore cattle. Selection based on visual scores for body structure, finishing precocity and muscling should lead to desired changes in body composition albeit much more slowly than direct selection on those traits measured by ultrasound. Selection for heavier BW at early ages should lead to favorable changes in yearling LM area and visual scores.

Parâmetros genéticos de escore visual do umbigo em bovinos da raça Nelore / Genetics parameters for navel visual score in Nellore cattle

Foram utilizados 128.700, 44.227, 90.383, 47.506, 42.619, 45.057, 17.666 e 27.181 dados, respectivamente, de peso à desmama (PD), peso ao sobreano (PS), escore de umbigo à desmama (UD), escore de umbigo à desmama de macho (UDM), escore de umbigo à desmama de fêmea (UDF), escore de umbigo ao sobreano (US), escore de umbigo ao sobreano de macho (USM) e escore de umbigo ao sobreano de fêmea (USF) com o objetivo de estimar parâmetros genéticos de escore visual do umbigo e as respectivas correlações genéticas com as características de crescimento - peso à desmama e peso ao sobreano -, em bovinos da raça Nelore, aplicando-se um modelo animal em análises uni e bicaracterísticas. As estimativas de herdabilidade (h²) para as características UD, UDM, UDF, US, USM, USF, PD e PS foram de 0,14±0,01; 0,18±0,02; 0,15±0,01; 0,26±0,01; 0,32±0,03; 0,27±0,02, 0,29±0,01 e 0,27±0,02, respectivamente, em análises unicaracterísticas. Em análises bicaracterísticas, as estimativas de h² para UD, US, PD e PS foram de 0,15, 0,27, 0,29 e 0,45, respectivamente. As correlações genéticas estimadas entre UDM e UDF, entre USM e USF e entre UD e US foram positivas e altas, as correlações genéticas entre escore do umbigo e características de crescimento foram todas positivas e de magnitudes de baixa a moderada.

Records of 128,700, 44,227, 90,383; 47,506; 42,619; 45,057; 17,666 and 27,181 animals were analyzed, for weight at weaning (WW), yearling weight (YW), navel score at weaning (NW), male navel score at weaning (MNW), female navel score at weaning (FNW), navel score at yearling (NY), male navel score at yearling (MNY) and female navel score at yearling (FNY), respectively, to estimate genetic parameters of navel visual scores and growth traits in Nelore cattle, using uni and bi-traits analysis. Heritability estimates obtained by uni-traits analysis for NW, MNW, FNW, NY, MNY, FNY, WW and YW traits were 0.14±0.01; 0.18±0.02; 0.15±0,01; 0.26±0,01; 0.32±0.03; 0.27±0.02; 0.29±0.01 and 0.27±0.02, respectively. Heritability estimates obtained by bi-traits analysis of NW, NY, WW and YW were 0.15; 0.27; 0.29 and 0.45. Genetic correlations between MNW and FNW, between MNY and FNY and between NW and NY, were positive and high, suggesting that these traits were determined by the same genes. Genetic correlation between navel score and growth traits were all positive and of low to moderate magnitude.