Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach.

Four performance-related traits [growth trait (GROW), feed efficiency trait 1 (FE1) and trait 2 (FE2), and dissection trait (DT)] and 4 categorical traits [mortality (MORT) and 3 disorder traits (DIS1, DIS2, and DIS3)] were analyzed using linear and threshold single- and multi-trait models. Field data included 186,596 records of commercial broilers from Cobb-Vantress, Inc. Average-information restricted maximum likelihood and Gibbs sampling-based methods were used to obtain estimates of the (co)variance components, heritabilities, and genetic correlations in a traditional approach using best linear unbiased prediction (BLUP). The ability to predict future breeding values (measured as realized accuracy) was checked in the last generation when traditional BLUP and single-step genomic BLUP were used. Heritability estimates for GROW, FE1, and FE2 in single- and multi-trait models were similar and moderate (0.22 to 0.26) but high for DT (0.48 to 0.50). For MORT, DIS1, and DIS2, heritabilities were 0.13, 0.24, and 0.34, respectively. Estimates from single- and multi-trait models were also very similar. However, heritability for DIS3 was higher from the single-trait threshold model than for the multi-trait linear-threshold model (0.29 vs. 0.19). Genetic correlations between growth traits and MORT were weak, except for maternal GROW, which had a moderate negative correlation (-0.50) with MORT. The genetic correlation between MORT and DIS1 was strong and positive (0.77). Feed efficiency 1, which was moderately heritable (0.25) and is highly selected for, was not genetically related to MORT of broilers and other disorders. Broiler MORT also had moderate heritability (0.13), which suggests that MORT and FE1 can be improved through selection without negatively impacting other important traits. Selection of heavier maternal GROW also may decrease offspring MORT.

Data compression can discriminate broilers by selection line, detect haplotypes, and estimate genetic potential for complex phenotypes.

Accurately establishing the relationships among individuals lays the foundation for genetic analyses such as genome-wide association studies and identification of selection signatures. Of particular interest to the poultry industry are estimates of genetic merit based on molecular data. These estimates can be commercially exploited in marker-assisted breeding programs to accelerate genetic improvement. Here, we test the utility of a new method we have recently developed to estimate animal relatedness and applied it to genetic parameter estimation in commercial broilers. Our approach is based on the concept of data compression from information theory. Using the real-world compressor gzip to estimate normalized compression distance (NCD) we have built compression-based relationship matrices (CRM) for 988 chickens from 4 commercial broiler lines-2 male and 2 female lines. For all pairs of individuals, we found a strong negative relationship between the commonly used genomic relationship matrix (GRM) and NCD. This reflects the fact that "similarity" is the inverse of "distance." The CRM explained more genetic variation than the corresponding GRM in 2 of 3 phenotypes, with corresponding improvements in accuracy of genomic-enabled predictions of breeding value. A sliding-window version of the analysis highlighted haplotype regions of the genome apparently under selection in a line-specific manner. In the male lines, we retrieved high population-specific scores for IGF-1 and a cognate receptor, INSR. For the female lines, we detected an extreme score for a region containing a reproductive hormone receptor (GNRHR). We conclude that our compression-based method is a valid approach to established relationships and identify regions under selective pressure in commercial lines of broiler chickens.

Sexual dimorphism in livestock species selected for economically important traits.

Most breeding companies evaluate economically important traits in males and females as a single trait, assuming genetic correlation of 1 between phenotypes measured in both sexes. This assumption may not be true because genes may be differently expressed in males and females. We estimated genetic correlations between males and females for growth and efficiency traits in broiler chickens, growth traits in American Angus beef cattle, and birth weight and preweaning mortality in purebred pigs; therefore, each trait was treated differently in males and females. Variance components were estimated in single- and multiple-trait models, jointly or separated into both sexes. Furthermore, we calculated traditional and genomic evaluations, and we correlated EBV or genomic EBV (GEBV) from joint and separate evaluations for males and females. For broiler chickens, genetic correlations ranged from 0.86 to 0.94. For Angus cattle, genetic correlations ranged from 0.86 to 0.98 for early growth traits and were less, ranging from 0.68 to 0.84, for postweaning gain. In pigs, genetic correlations ranged from 0.98 to 0.99 for birth weight and from 0.71 to 0.73 for preweaning mortality. For some models in all 3 animal species, the joint and separate analyses had different heritabilities. Despite differences in heritability, the correlations within the sex-specific trait EBV and between the sex-specific and the joint trait EBV were very strong, regardless of the model or inclusion of genomic information. Males and females differed for traits measured late in the animal's life; however, strong traditional EBV correlations and also GEBV correlations indicate that considering the traits equal in males and females may have no negative impact on selection.

Genetic evaluation for growth, body composition, feed efficiency, and leg soundness.

A multiple trait linear-threshold model was used to analyze data for BW, residual feed intake, breast meat yield (BMY), conformation score (CS), area (AR), tibial dyschondroplasia, valgus, varus, and rotated tibia. Leg soundness traits were considered as binary responses. At the liability scale, the model included the fixed effects of flock-week of hatch, and sex of the bird and the genetic additive effect, and the error terms as random. The random maternal effect was included in the model only for BW. A full Bayesian implementation of the model was straightforward even though large number of traits and missing records were present. As expected, binary traits have the lowest heritability. Heritability ranged from 0.12 for tibial dyschondroplasia to 0.44 for BMY. Genetic correlations between BW and conformation traits were moderate to high. Residual feed intake was negatively correlated with BW (-0.15), AR (-0.13), BMY (-0.04), and CS (-0.12). Genetic correlation between leg soundness traits were generally low and negative with the exception of the correlation between valgus and varus (-0.70) and between varus and rotated tibia (-0.39). Genetic correlations between BW, BMY, CS, and AR with leg soundness traits were in general negative and low in magnitude. Thus, selecting for improved leg soundness will have minimal effect on BW and carcass traits. Furthermore, genetic improvement in residual feed intake will result in improvements in carcass traits. Simultaneous genetic improvement in leg soundness and innovative husbandry practices should improve broiler welfare without significant adverse effects on production efficiency.

Different methods of selecting animals for genotyping to maximize the amount of genetic information known in the population.

It is possible to predict genotypes of some individuals based on genotypes of relatives. Different methods of sampling individuals to be genotyped from populations were evaluated using simulation. Simulated pedigrees included 5,000 animals and were assigned genotypes based on assumed allelic frequencies for a SNP (favorable/unfavorable) of 0.3/0.7, 0.5/0.5, and 0.8/0.2. A field data pedigree (29,101 animals) and a research pedigree (8,688 animals) were used to test selected methods using simulated genotypes with allelic frequencies of 0.3/0.7 and 0.5/0.5. For the simulated pedigrees, known and unknown allelic frequencies were assumed. The methods used included random sampling, selection of males, and selection of both sexes based on the diagonal element of the inverse of the relationship matrix (A(-1)) and absorption of either the A or A(-1) matrix. For random sampling, scenarios included selection of 5 and 15% of the animals, and all other methods presented concentrated on the selection of 5% of the animals for genotyping. The methods were evaluated based on the percentage of alleles correctly assigned after peeling (AK(P)), the probability of assigning true alleles (AK(G)), and the average probability of correctly assigning the true genotype. As expected, random sampling was the least desirable method. The most desirable method in the simulated pedigrees was selecting both males and females based on their diagonal element of A(-1). Increases in AK(P) and AK(G) ranged from 26.58 to 29.11% and 2.76 to 6.08%, respectively, when males and females (equal to 5% of all animals) were selected based on their diagonal element of A(-1) compared with selecting 15% of the animals at random. In the case of a real beef cattle pedigree, selection of males only or males and females yielded similar results and both selection methods were superior to random selection.

Multi-breed genetic evaluation in a Gelbvieh population.

A multi-breed model was presented for the genetic evaluation of growth traits in beef cattle. In addition to the fixed effects, random direct and maternal genetic effects, and random maternal permanent environmental effects are considered; the model also fits direct and maternal heterosis and direct and maternal breed-of-founder (BOF) x generation group effects using a Bayesian approach that weights prior literature estimates relative to information supplied by the dataset to which the model will be applied. The multi-breed evaluation procedures also allow the inclusion of external evaluations for animals of other breeds. The multi-breed model was applied to a dataset provided by the American Gelbvieh Association. Different analyses were conducted by varying the weights given to the prior literature relative to the information provided by the dataset. Large differences were observed for the heterosis estimates, the BOF x generation group effect estimates, and the predicted breeding values across breeds due to the weights posed on prior literature estimates versus estimates derived directly from data. However, the rankings within breed were observed to be relatively robust to the different weights on prior information.

Genetic evaluation in the presence of uncertain additive relationships. I. Use of phenotypic information to ascertain paternity.

A simulation was carried out to investigate the implementation of a genetic evaluation when the additive relationship matrix is not completely known due to the presence of uncertain paternity in the pedigree. Data were simulated and analyzed using a linear mixed model that included a fixed contemporary group effect plus random additive and residual effects. For the univariate scenario, either 1 or 2 records of a single trait with heritabilities of 33, 50, and 67% were used to compute the probability of being the true sire (PTS) of each candidate sire for a given offspring. One record of 3 correlated traits was used to compute PTS in a 3-trait scenario. A Bayesian procedure via Markov Chain Monte Carlo was used to carry out the implementation, in which the PTS was computed without the need to invert the relationship matrix. The average probability of the true sire being identified as such (PSA), as well as the percentage difference (PD) between PSA and an equal prior probability assigned to each candidate sire, were computed for the single and 3-trait scenarios. Using 1 trait, PSA increased with an increase in heritability. When repeated records were considered, the PD was increased by 50 to 386% compared with using just 1 record per animal for the varying heritabilities and number of candidate sires, suggesting that phenotypic information was better able to discriminate among candidate sires when more than 1 record was used to determine PSA. Using 3 correlated traits increased PD by 77 to 98% when compared with using 1 record of a trait with 67% heritability. Similarly, the PD was increased by 105 to 1,021%, when compared with using 1 record of a trait with 33% heritability. These results indicate that the probability of identifying the true sire increased when 3 correlated traits were used to compute PSA. The correlations between true and predicted breeding values of 3 traits were increased by 6 to 7% for all animals and 64 to 89% for animals with unknown paternity in the pedigree when estimated probability of paternity was used as compared with equal prior probability assigned to each candidate sire. For traits such as birth weight and weaning weight, in which only 1 measurement is taken, the 3-trait scenario could result in more animals being assigned the true sire than if birth or weaning weight was used separately. Further research is needed to determine the performance of this methodology in field data as well as the potential implementation of this methodology in conjunction with molecular information.

Success at first insemination in Australian Angus cattle: analysis of uncertain binary responses.

Field data from Australian Angus herds were used to investigate 2 methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included herd, year, and month of mating as fixed effects; unrelated service sire, additive animal, and residual as random effects; and linear and quadratic effects of age at mating as covariates was used to analyze binary data. An average gestation length (GL) derived from artificial insemination data was used to assign an insemination date to females mated to natural service sires. Females that deviated from this average GL led to uncertain binary responses. Two analyses were carried out: 1) a threshold model fitted to uncertain binary data, ignoring uncertainty (M1); and 2) a threshold model fitted to uncertain binary data, accounting for uncertainty via fuzzy logic classification (M2). There was practically no difference between point estimates obtained from M1 and M2 for service sire and herd variance; however, when uncertain binary data were analyzed ignoring uncertainty (M1), additive variance and heritability estimates were greater than with M2. Pearson correlations indicated that no major reranking would be expected for service sire effects and animal breeding values using M1 and M2. Given the results of the current study, a threshold model contemplating uncertainty is suggested for noisy binary data to avoid bias when estimating genetic parameters.

Longitudinal multiple-trait versus cumulative single-trait analysis of male and female fertility and hatchability in chickens.

The objective of the current study was to compare the performances of a longitudinal multipletrait (MT) and cumulative single-trait (ST) best linear unbiased prediction (BLUP) analyses for eggs set (E), percentage fertility (F), and percentage hatched of fertile eggs (H) based on rank correlations of hens and roosters (ROO). Longitudinal data consisted of weekly records, and the ST analysis used cumulative records of the 3 traits. Comparisons were carried out using simulated data, in which true values assigned during the simulation were compared with estimates obtained from MT and ST analyses. Two methods were used to calculate breeding values and ROO effects. Weekly E records equal to zero were assumed as valid records, and missing weekly F and H records were predicted in MT analysis. However, missing F and H were not included in the cumulative record. Weekly E, F, and H records were converted to a cumulative record following a procedure used by a commercial broiler company. Further, ST models were developed to evaluate hens and ROO separately as currently practiced in the industry. There was virtually no difference in hen rank correlations between true and predicted breeding values obtained from MT (0.64) and ST (0.62) analyses for E, thus suggesting that hens would rank similarly when weekly or cumulative data were used for genetic evaluation of E. With ST analysis, rank correlations between true and estimated hen breeding values decreased, compared with MT analysis, by 74 and 75% for F and H, respectively. For F (H), the rank correlation between true and estimated ROO effects were 0.64 (0.67) and 0.17 (0.12) with MT and ST analyses, respectively. The results suggested that longitudinal MT BLUP, which handles missing records, was better at estimating true hen and ROO effects than ST BLUP. Therefore, it was recommended that the MT BLUP method be used for genetic evaluation of hens and ROO for E, F, and H.

Male and female fertility and hatchability in chickens: a longitudinal mixed model approach.

The objective of the current study was to investigate different approaches for handling missing records and to develop and implement a multivariate longitudinal mixed model for the genetic evaluation of male and female fertility and hatchability in chickens. The traits recorded on a weekly basis were eggs set (E), percentage fertility (F), and percentage hatch of fertile eggs (H). Three approaches for handling missing records were investigated: 1) all records with zero weekly laid eggs were removed and remaining records with missing F and H were predicted (M1); 2) missing records, including zero weekly laid eggs, were assumed known and equal to zero (M2); and 3) zero weekly laid eggs were assumed as a valid record and missing F and H were predicted (M3). A longitudinal mixed model was used for the multiple trait analysis of E, F, and H. Fixed effects included week-flock, age of service sire (rooster in the pen at data collection), and age of hen. Unrelated service sire, additive hen, permanent environmental, and the residual term were included as random effects. Heritability estimates ranged from 0.06 to 0.13 for E, F, and H. Heritability estimates of F and H were overestimated using M2. Correlations among the 3 traits were highest using M2 and lowest using M1. Pearson correlations indicated that reranking of birds and service sires could occur when using M1. Therefore, M1 should not be used for the analysis of longitudinal data of fertility and hatchability in chickens. Furthermore, M3 seems to be the most ideal method for handling missing records.

Teat scores in first-parity Gelbvieh cows: relationship with suspensory score and calf growth traits.

Teat and udder suspensory scores from 9,418 first-parity Gelbvieh cows and growth records from 19,119 calves were used to estimate genetic and environmental parameters for teat and suspensory score and to investigate the relationship of teat and suspensory score with calf growth traits and maternal genetic growth effects. First-parity cows did not have multiple records within 280 d, gave birth to one calf, were 4 yr of age or younger at first-calving, and were at least 50% Gelbvieh. Producers scored cows within 24 h of parturition. Teat score (T), a subjective measure of teat size, ranged from 0 (very large) to 50 (very small), and suspensory score (S), a subjective score of udder support, ranged from 0 (very pendulous) to 50 (very tight). Unadjusted birth weight (BW), weaning weight, and yearling weight of the calves, born in the first three parities to cows with first-parity T and S records, were used to calculate pre- and postweaning ADG (WG and YG, respectively). A mixed model was used for the multiple trait analysis of T, S, BW, WG, and YG, which included herd-year, month of calving, age of cow at calving, and sex of calf (included only for BW, WG, and YG) as systematic effects; regression on the percentage of Gelbvieh; and additive animal and maternal genetic of dam (included only for BW and WG), maternal permanent environment (included only for BW and WG), and residual as random effects. The genetic correlation between T and S was 0.95, suggesting that T and S are basically the same trait in this dataset. The genetic correlations between T (S) with direct BW, WG, and YG and with maternal BW and WG were -0.18 (-0.06), 0.38 (0.31), 0.09 (-0.01), -0.16 (-0.16), and -0.47 (-0.55), respectively, suggesting that cows with smaller teats and tighter udders produced less milk and raised calves that had higher genetic growth potential for WG. Further, the Pearson correlations between predicted breeding values of T and S with maternal WG indicated that animals with extremely large teats or pendulous udders may produce more milk, but that the calf may have trouble accessing it. Conversely, with extremely small teats or tight udders, smaller amounts of milk would be produced and there may be a problem producing enough milk to maintain the growing calfs maintenance requirements. Therefore, it may be more beneficial for producers to select animals that have intermediate breeding values for T and S.

Simulation study of teat score in first-parity Gelbvieh cows: parameter estimation.

Teat scores from 9,598 first-parity Gelbvieh cows were used to investigate the adequacy of grouping approaches to decrease score misclassifications or inconsistencies as well as to simplify the data collection process. The procedure was tested using simulated data and then validated using teat score records of Gelbvieh cattle. First-parity cows were considered to be 4 yr of age or younger at first calving, did not have multiple records within 280 d, and were at least 50% Gelbvieh. Producers scored cows within 24 h of parturition. Teat score, a subjective measure of teat size, ranged from 0 (very large) to 50 (very small). A linear mixed model that included herd-year, month of calving, and age at calving as systematic effects; regression on the percentage of Gelbvieh; and additive breeding values (BV) and residual as random effects was used to generate the data. Simulated data were analyzed using one of three scoring methods: all values (S50), 10 classes (S10), and five classes (S5). The 10 classes were formed by subdividing every five scores into a single class starting at score zero. Similarly, the five classes were formed by combining every 10 scores into one class. The average Pearson correlations, based on five replicates, between the true and estimated BV (systematic effects) were 0.36 (0.85), 0.35 (0.89), and 0.32 (0.87) using S50, S10, and S5, respectively. Average correlations between estimated BV (systematic effects) were 0.97 (0.95), 0.89 (0.92), and 0.92 (0.97) based on S50 and S10, S50 and S5, and S10 and S5, respectively. Field data were used to validate the simulation procedure. The field data were categorized into 10 classes (F10) and five classes (F5) as described for the simulated data. Pearson correlations between estimated BV (systematic effects) were 0.99 (0.93), 0.93 (0.88), and 0.93 (0.96), based on F50 with F10, F50 with F5, and F10 with F5, respectively. The extremely high correlations between predicted BV based on S50, S10, S5, F50, F10, with F5 suggest that a simplified score classification method could be adopted without compromising the expected genetic progress for the trait under consideration. Furthermore, the difference in corresponding Pearson correlations across the field and simulated data might suggest the presence of some inconsistencies or misclassifications of the actual scoring system.

Effects of selection for ultrasound intramuscular fat percentage in Angus bulls on carcass traits of progeny.

Angus bulls (n = 20) from three pure-bred herds in Georgia were acquired to determine the impact of selecting sires based on phenotypic yearling ultrasound intramuscular fat percentage (UIMF) or UIMF EPD on marbling score of steer progeny. Each year in each herd, pairs of bulls were selected to create large differences based on their age adjusted phenotypic yearling UIMF measurements. The average UIMF, weighted by number of progeny per sire, was 3.75% (SD = 1.10%) and 1.70% (SD = 0.53%) for high UIMF (HU) and low UIMF (LU) bulls, respectively. All available ultrasound measurements collected in the purebred co-operator herds were combined with other ultrasound records collected by the American Angus Association for the computation of genetic values for ultrasound fat thickness, ribeye area, and intramuscular fat percentage. Each year bulls were randomly mated to 14 to 30 commercial Angus females. Carcass weight, fat thickness at the 12th rib, ribeye area at the 12th rib, marbling score, yield grade, and quality-grade measurements were collected on 188 steer progeny. Carcass data were linearly adjusted to 480 d of age at slaughter. Steer progeny sired by HU bulls had higher age-adjusted marbling score and quality grade (P < 0.05), and smaller age-adjusted ribeye area (P < 0.05) than steer progeny sired by LU bulls. No significant differences between phenotypic UIMF lines were found for age-adjusted fat thickness (P = 0.84) and yield grade (P = 0.33) in the steer progeny. The regression of age-adjusted carcass marbling score and quality grade of the steer progeny on ultrasound intramuscular fat percentage EPD of the sires produced highly significant regression coefficients of 90.50 and 49.20, respectively. Thus, yearling Angus bulls selected for high-phenotypic UIMF and UIMF EPD can be expected to produce steer progeny with significantly higher amounts of marbling and quality grade. It also appears that marbling can be increased without corresponding increases in external fat thickness and yield grade.