Genetic evaluations for growth heat tolerance in Angus cattle.

The objectives were to assess the impact of heat stress and to develop a model for genetic evaluation of growth heat tolerance in Angus cattle. The American Angus Association provided weaning weight (WW) and yearling weight (YW) data, and records from the Upper South region were used because of the hot climatic conditions. Heat stress was characterized by a weaning (yearling) heat load function defined as the mean temperature-humidity index (THI) units greater than 75 (70) for 30 (150) d prior to the weigh date. Therefore, a weaning (yearling) heat load of 5 units corresponded to 80 (75) for the corresponding period prior to the weigh date. For all analyses, 82,669 WW and 69,040 YW were used with 3 ancestral generations in the pedigree. Univariate models were a proxy for the Angus growth evaluation, and reaction norms using 2 B-splines for heat load were fit separately for weaning and yearling heat loads. For both models, random effects included direct genetic, maternal genetic, maternal permanent environment (WW only), and residual. Fixed effects included a linear age covariate, age-of-dam class (WW only), and contemporary group for both models and fixed regressions on the B-splines in the reaction norm. Direct genetic correlations for WW were strong for modest heat load differences but decreased to less than 0.50 for large differences. Reranking of proven sires occurred for only WW direct effects for the reaction norms with extreme heat load differences. Conversely, YW results indicated little effect of heat stress on genetic merit. Therefore, weaning heat tolerance was a better candidate for developing selection tools. Maternal heritabilities were consistent across heat loads, and maternal genetic correlations were greater than 0.90 for nearly all heat load combinations. No evidence existed for a genotype × environment interaction for the maternal component of growth. Overall, some evidence exists for phenotypic plasticity for the direct genetic effects of WW, but traditional national cattle evaluations are likely adequately ranking sires for nonextreme environmental conditions.

Regional and seasonal analyses of weights in growing Angus cattle.

This study evaluated the impact of region and season on growth in Angus seed stock. To assess geographic differences, the United States was partitioned into 9 regions based on similar climate and topography related to cow-calf production. Seasonal effects were associated with the month that animals were weighed. The American Angus Association provided growth data, and records were assigned to regions based on the owner's zip code. Most Angus cattle were in the Cornbelt, Lower Plains, Rocky Mountain, Upper Plains, and Upper South regions, with proportionally fewer Angus in Texas compared with the national cow herd. Most calves were born in the spring, especially February and March. Weaning weights (WW; = 49,886) and yearling weights (YW; = 45,168) were modeled with fixed effects of age-of-dam class (WW only), weigh month, region, month-region interaction, and linear covariate of age. Random effects included contemporary group nested within month-region combination and residual. The significant month-region interaction ( < 0.0001) was expected because of the diverse production environments across the country and cyclical fluctuations in forage availability. Additionally, significant seasonal contrasts existed for several regions. Fall-born calves were heavier ( < 0.01) than spring-born calves in the hot and humid Lower South region coinciding with fall being the primary calving season. The North and Upper Plains regions had heavier, spring-born calves ( < 0.01), more than 90% spring calving, and colder climates. Interestingly, no seasonal WW or YW differences existed between spring- and fall-born calves in the upper South region despite challenging environmental conditions. Angus seed stock producers have used calving seasons to adapt to the specific environmental conditions in their regions and to optimize growth in young animals.

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE-BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.

Correlations between purebred and crossbred body weight traits in Limousin and Limousin-Angus populations.

The purpose of this study was to estimate correlations between purebred and F1 crossbred performance to verify the appropriateness of current models used in multibreed selection. Records on birth weight (WB) and weaning weight (WW) from purebred Limousins (LIM) and Limousin × Angus progeny (F1) were used to estimate genetic parameters using a multiple-trait (purebred and F1 weights were different traits) approach. For WB, there were 148,647 records for LIM and 17,981 for F1, and for WW, there were 81,585 records for LIM and 21,778 for F1. The fixed effect in models for LIM and F1 animals was contemporary group. Random effects for LIM animals were direct genetic, maternal genetic, and maternal permanent environment effects. Random effects for F1 were sire and dam. The pedigree for Angus dams used for crossing was unavailable and therefore these dams were assumed unrelated. The direct h2 estimates (SE) for purebred animals were 0.41 (0.05) and 0.24 (0.02) for WB and WW, respectively. For F1, the same estimates were 0.22 (0.09) and 0.32 (0.05). Genetic correlations estimates between purebreds and crossbreds were 0.84 (0.07) and 0.64 (0.18) for WB and WW, respectively. The genetic correlation for WW estimated in this study suggests that F1 and purebred information for this trait should not be treated, genetically, as the same trait due to different genetic effects molding it. However, the genetic correlation for WB was much higher, indicating that this trait in purebreds and F1 is essentially the same trait.

Allometric comparison of Georgia dairy heifers on farms and at youth shows.

Studies were conducted to determine the relationship between allometric measures of growth of Holstein dairy heifers and placing in the show ring, and to compare differences in growth between Holstein heifers that are shown and not shown. In the first study, 494 Holstein show heifers were evaluated at the 2012 and 2013 Georgia Junior National Livestock Shows. Measurements were obtained for weight, head length, withers height, hip height, thurl width, and tail length. Heifer mass index (HMI), average daily gain (ADG), and age were calculated. In total, 72.5% of Holstein show heifers were underweight. Average ADG was 0.63 kg/d, which is below the industry recommendation of 0.7 to 0.8 kg/d. Variables were ranked and converted to percentages to account for differences in class size. Withers height, head length, and HMI were most indicative of show placing. In the second study, we compared differences between growth patterns of show heifers and non-show heifers. An additional 293 non-show Holstein heifers were evaluated on 3 Georgia dairy farms during the same period as the show. In total, 43.3% of non-show heifers were underweight. Average ADG for non-show heifers was 0.71 kg/d, which is within the industry recommendation of 0.7 to 0.8 kg/d. Show heifers weighed less for their age than non-show heifers and tended to be taller at the withers than non-show heifers. The HMI scores were similar for younger show and non-show heifers, but older show heifers had lower HMI scores than non-show heifers of the same age. Show heifers had HMI scores that were lower than values calculated from standard growth data. As show heifers matured, ADG decreased, whereas as non-show heifers matured, ADG increased. Youth, leaders, and parents need to be aware of the importance of growing replacement heifers correctly so that heifers calve at 22 to 24 mo of age at an acceptable size and scale and become profitable members of the milking herd.

Estimation of regional genetic parameters for mortality and 305-d milk yield of US Holsteins in the first 3 parities.

Several research reports have indicated increasing dairy cow mortality in recent years. The objectives of this research were to characterize the phenotypic differences in mortality in the first 3 parities across 3 regions of the United States to estimate the heritability of mortality of Holstein cows across regions and parities, and to estimate genetic and environmental correlations between milk yield and mortality across parities and regions. Dairy Herd Information (DHI) milk yield and mortality data were obtained from 3 different US regions: the Southeast (SE), Southwest (SW), and Northeast (NE). A total of 3,522,824 records for the first 3 parities were used: 732,009 (SE), 656,768 (SW), and 2,134,047 (NE) from 1999 to 2008. Cows that received a termination code of 6--"Cow died on the dairy; downer cows that were euthanized should be included here"--were given a mortality score of 2 (dead), whereas all other codes were assigned a mortality score of 1 (alive). Average annual mortalities in the first 3 parities across regions ranged from 2.2 to 7.2%, with mortality frequency increasing with increasing parity across all regions and with the SE having the highest mortality frequency. For genetic analysis, a 2-trait (305-d milk yield and mortality) linear-threshold animal model that fitted fixed effects of herd-year (for 305-d milk yield), cow age, days in milk (in month classes), month-of-termination, and random effects of herd-year (for mortality), animal, and residual was implemented. The model was used to estimate variance components separately for each region and parity. Heritability estimates for mortality were similar for all regions and parities, ranging from 0.04 to 0.07. Genetic correlations between mortality and 305-d milk yield across the first 3 parities were 0.14, 0.20, and 0.29 in SE; -0.01, 0.01, and 0.31 in SW; and 0.28, 0.33, and 0.19 in NE. We detected an adverse genetic relationship between milk production and mortality; however, the moderate magnitudes of the genetic correlations suggest that indices that include both milk yield and mortality could be effective in identifying sires that would provide opportunities for minimizing death loss even when selecting for increased milk yield.

Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models.

Different methods for genomic evaluation were compared for accuracy and feasibility of evaluation using phenotypic, pedigree, and genomic information for a trait influenced by a maternal effect. A simulated population was constructed that included 15,800 animals in 5 generations. Genotypes from 45,000 SNP were available for 1,500 animals in the last 3 generations. Genotyped animals in the last generation had no phenotypes. Weaning weight data were simulated using an animal model with direct and maternal effects. Additive direct and maternal effects were considered either noncorrelated (formula in text) or negatively correlated (formula in text). Methods of analysis were traditional BLUP, BayesC using phenotypes and ignoring maternal effects (BayesCPR), BayesC using deregressed EBV (BayesCDEBV), and single-step genomic BLUP (ssGBLUP). Whereas BayesCPR can be used when phenotypes of only genotyped animals are available, BayesCDEBV can be used when BLUP EBV of genotyped animals are available, and ssGBLUP is suitable when genotypes, phenotypes, and pedigrees are jointly available. For all genotyped and young genotyped animals, mean accuracies from BayesCPR and BayesCDEBV were lower than accuracies from BLUP for direct and maternal effects. The differences in mean accuracy were greater when genetic correlation was negative. Gains in accuracy were observed when ssGBLUP was compared with BLUP; for the direct (maternal) effect the average gain was 0.01 (0.02) for all genotyped animals and 0.03 (0.02) for young genotyped animals without phenotypes. Similar gains were observed for 0 and negative genetic correlation. Accuracy with BayesCPR was affected by ignoring phenotypes of nongenotyped animals and maternal effect and by not accounting for parent average. Accuracy with BayesCDEBV was affected by approximations needed for deregression, not accounting for parent average, and sequential rather than simultaneous fitting of genomic and nongenomic information. Whereas BayesCDEBV presented a considerable bias, especially for maternal effect, ssGBLUP was unbiased for both effects. The computing time was 1 s for BLUP, 44 s for ssGBLUP, and over 2,000 s for BayesC. Greatest computational efficiency and accuracy of genomic prediction for a maternally affected trait was obtained when information from all nongenotyped but related individuals was included and phenotypes, pedigree, and genotypes were available and considered jointly. Increasing the gain in accuracy of genomic predictions obtained by ssGBLUP over BLUP may require an increase in the number of genotyped animals.

Genotype by environment interaction for growth due to altitude in United States Angus cattle.

The objectives of this study were to determine if sires perform consistently across altitude and to quantify the genetic relationship between growth and survival at differing altitudes. Data from the American Angus Association included weaning weight (WW) adjusted to 205 (n = 77,771) and yearling weight adjusted to 365 (n = 39,450) d of age from 77,771 purebred Angus cattle born in Colorado between 1972 and 2007. Postweaning gain (PWG) was calculated by subtracting adjusted WW from adjusted yearling weight. Altitude was assigned to each record based upon the zip code of each herd in the database. Records for WW and PWG were each split into 2 traits measured at low and high altitude, with the records from medium altitude removed from the data due to inconsistencies between growth performance and apparent culling rate. A binary trait, survival (SV), was defined to account for censored records at yearling for each altitude. It was assumed that, at high altitude, individuals missing a yearling weight either died or required relocation to a lower altitude predominantly due to brisket disease, a condition common at high altitude. Model 1 considered each WW and PWG measured at 2 altitudes as separate traits. Model 2 treated PWG and SV measured as separate traits due to altitude. Models included the effects of weaning contemporary group, age of dam, animal additive genetic effects, and residual. Maternal genetic and maternal permanent environmental effects were included for WW. Heritability estimates for WW in Model 1 were 0.28 and 0.26 and for PWG were 0.26 and 0.19 with greater values in low altitude. Genetic correlations between growth traits measured at different altitude were moderate in magnitude: 0.74 for WW and 0.76 for PWG and indicate possibility of reranking of sires across altitude. Maternal genetic correlation between WW at varying altitude of 0.75 also indicates these may be different traits. In Model 2, heritabilities were 0.14 and 0.27 for PWG and 0.36 and 0.47 for SV. Genetic correlation between PWG measured at low and high altitude was 0.68. Favorable genetic correlations were estimated between SV and PWG within and between altitudes, suggesting that calves with genetics for increased growth from weaning to yearling also have increased genetic potential for SV. Genetic evaluations of PWG in different altitudes should consider preselection of the data, by using a censoring trait, like survivability to yearling.

Genotype by region and season interactions on weaning weight in United States Angus cattle.

The objective of this study was to determine if weaning weight performance is genetically consistent across different environments in the United States. The American Angus Association provided weight and pedigree data. Weaning weights observed in the Southeast (SoE) and Northwest (NW) were the focus of this study, as these regions are perceived as opposite extremes in climate. The 2 most represented calving seasons in each region were fall and winter in the SoE and winter and spring in the NW. The original data were edited to remove weaning weight records outside of 3 SD from the respective region-season mean, contemporary groups smaller than 20, and single-sire contemporary groups. The final dataset included 884,465 weaning weight records with 64,907 from fall-born calves in the SoE, 74,820 from winter-born calves in the SoE, 346,724 from winter-born calves in the NW and 398,014 from spring-born calves in the NW. Weaning weights of calves born in different region-season classes adjusted to 205 d of age were considered different but genetically correlated traits in a multivariate analysis. The sole fixed effect was weaning contemporary group and random effects included direct, maternal, maternal permanent environment, and a residual. Direct heritability estimates differed little across environments: 0.31 and 0.35 for weight in fall- and winter-born calves in the SoE, and 0.29 and 0.32 for winter- and spring-born calves in NW. Maternal heritability estimates ranged from 0.12 in the NW to 0.16 the SoE. Genetic correlations spanned from 0.69 to 0.93 among direct effects and from 0.65 to 0.95 among maternal effects. All heritability estimates had small (0.01 to 0.04) SE. The most distinct environments appeared to be winter in SoE and spring in NW (correlations of 0.69 and 0.65 for the direct and maternal effects). Different choices of sires for different environments might be justified to achieve the growth performance expected.

The relationship between weight, age, and average daily gain to show performance of Georgia 4-H and Future Farmers of America (FFA) commercial dairy heifers.

Three studies were conducted to determine the relationship between dairy heifer growth and placing in the show ring. In the first study, 1,744 commercial dairy heifers (all breeds and crossbred animals) were evaluated to determine effects of growth on placing within Georgia Commercial Dairy Heifer Shows from 2007 to 2010. Birth weights were determined using breed birth weight averages, with crossbreeds being the average of 2 parent breeds. Average daily gains (ADG) were calculated and heifers were given rankings based on placing in show and for age and weight. Data was analyzed using the Spearman correlation calculations in the SAS software (SAS Institute Inc., Cary, NC). Age and ADG were inversely correlated (r=-0.89). Mean ADG for all heifers was determined to be 0.65 kg, below National Research Council recommendations of 0.7 to 0.8 kg. No strong relationship (r=-0.07) was observed between ADG and placing. Heavier heifers within a class showed a small positive relationship (r=0.10) with placing. For study 2, 238 heifers shown at the 2010 Georgia Junior National Livestock Show (Perry, GA) were measured and evaluated for ADG, placing, body weight, age, withers height, hip height, hip width, and jaw width. Height at withers had a moderate relationship (r=0.42) with placing, followed by hip height (r=0.32). A positive relationship (r=0.65) was observed between withers height and hip height. The correlation between weight and placing was determined (r=0.11). Age and ADG had a strong inverse relationship (r=-0.87). Study 3 evaluated 1,489 Holstein heifers shown from 2007 to 2010. Data was analyzed using the Penn State Growth Monitor Spreadsheet Curves. In total, 63.75% did not meet Penn State recommendations for body weight gain. Performance and physical features associated with age indicates that commercial dairy heifers are underfed. The effects of heat stress and high feed costs also play a role. This has economic implications because these animals will likely require more time before they enter the milk herd. The Commercial Dairy Heifer Program is vital for youth development in Georgia. However, those involved need to be encouraged to improve nutritional management practices.

Estimation of genetic parameters for mature weight in Angus cattle.

The aim of this study was to estimate genetic parameters for BW of Angus cattle up to 5 yr of age and to discuss options for including mature weight (MW) in their genetic evaluation. Data were obtained from the American Angus Association. Only records from herds with at least 500 animals and with >10% of animals with BW at ≥ 2 yr of age were considered. Traits were weaning weight (WW, n = 81,525), yearling weight (YW, n = 62,721), and BW measured from 2 to 5 yr of age (MW2, n = 15,927; MW3, n = 12,404; MW4, n = 9,805; MW5, n = 7,546). Genetic parameters were estimated using an AIREML algorithm with a multiple-trait animal model. Fixed effects were contemporary group and departure of the actual age from standard age (205, 365, 730, 1,095, 1,460, and 1,825 d of age for WW, YW, MW2, MW3, MW4, and MW5, respectively). Random effects were animal direct additive genetic, maternal additive genetic, maternal permanent environment, and residual. Estimates of direct genetic variances (kg(2)) were 298 ± 71.8, 563 ± 15.1, 925 ± 52.1, 1,221 ± 65.8, 1,406 ± 80.4, and 1,402 ± 66.9; maternal genetic variances were 167 ± 4.8, 153 ± 6.1, 123 ± 9.1, 136 ± 12.25, 167 ± 18.0, and 110 ± 14.0; maternal permanent environment variances were 124 ± 2.9, 120 ± 4.3, 61 ± 7.5, 69 ± 11.9, 103 ± 15.9, and 134 ± 35.2; and residual variances were 258 ± 3.8, 608 ± 8.6, 829 ± 34.2, 1,016 ± 38.8, 1,017 ± 52.1, and 1,202 ± 63.22 for WW, YW, MW2, MW3, MW4, and MW5, respectively. The direct genetic correlation between WW and YW was 0.84 ± 0.14 and between WW and MW ranged from 0.66 ± 0.06 (WW and MW4) to 0.72 ± 0.11 (WW and MW2). Direct genetic correlations ranged from 0.77 ± 0.08 (YW and MW5) to 0.85 ± 0.07 (YW and MW2) between YW and MW, and they were ≥ 0.95 among MW2, MW3, MW4, and MW5. Maternal genetic correlations between WW and YW and MW ranged from 0.52 ± 0.05 (WW and MW4) to 0.95 ± 0.07 (WW and YW), and among MW they ranged from 0.54 ± 0.14 (MW4 and MW5) to 0.94 ± 0.07 (MW2 and MW3). Genetic correlations suggest that a genetic evaluation for MW may be MW2-based and that including BW from older ages could be accomplished by adjusting records to the scale of MW2.

Estimation of breed and heterosis effects for growth and carcass traits in cattle using published crossbreeding studies.

Current genetic evaluations are performed separately for each breed. Multiple breed genetic evaluations, however, assume a common base among breeds, enabling producers to compare cattle of different breed makeup. Breed and heterosis effects are needed in a multibreed evaluation because databases maintained by breed associations include few crossbred animals, which may not be enough to accurately estimate these effects. The objective of this study was to infer breed effects, maternal effects, direct heterosis effects, and maternal heterosis effects for growth and carcass traits using least squares means estimates from crossbreeding studies published in the literature from 1976 to 1996. The data set was formed by recording each least squares mean along with the breed composition, maternal breed composition, and direct and maternal heterozygosity. Each trait was analyzed using a single trait fixed effect model, which included study as a fixed effect and breed composition and heterozygosity as covariates. Breed solutions for each trait were expressed relative to the Angus breed. Direct breed effects for weaning weight ranged from -7.0 +/- 0.67 kg (British Dairy) to 29.3 +/- 0.74 kg (Simmental), and maternal effects ranged from -11.7 +/- 0.24 kg (Hereford) to 31.1 +/- 2.22 kg (Gelbvieh). Direct breed effects for birth weight ranged from -0.5 +/- 0.14 kg (British Dairy) to 10.1 +/- 0.46 kg (Continental Beef), and maternal effects ranged from -7.2 +/- 0.13 kg (Brahman) to 6.0 +/- 1.07 kg (Continental Beef). Direct breed effects ranged from -17.9 +/- 1.64 kg (Brahman) to 21.6 +/- 1.95 kg (Charolais), from -6.5 +/- 1.29 kg (Brahman) to 55.8 +/- 1.47 kg (Continental Beef), from -8.1 +/- 0.48 cm(2) (Shorthorn) to 21.0 +/- 0.48 cm(2) (Continental Beef), and from -1.1 +/- 0.02 cm (Continental Beef) to 0 +/- 0.00 cm (Angus) for postweaning BW gain, carcass weight, LM area, and fat thickness, respectively. The use of literature estimates to predict direct and maternal breed and heterosis effects may supplement their direct prediction in a multibreed evaluation.

Trends for conception rate of Holsteins over time in the southeastern United States.

The purpose of this study was to estimate trends in conception rate (CR) of Holsteins in the southeastern United States over time across month by milk production level and month by days in milk (DIM) subclasses. Data were obtained from Dairy Records Management Systems (Raleigh, NC) and included service records from 10 states (Virginia, Kentucky, North Carolina, South Carolina, Tennessee, Georgia, Florida, Alabama, Mississippi, and Louisiana). After eliminating records with lactation >1 and uncertain and extreme records (records without calving or birth date, with days to service after calving <21 or >250, or without next calving date), the final data set included 827,802 artificial insemination service records for 424,513 cows born from 1985 to 2000, and in 2,953 herds. Effects included in the model were year of birth (1985 to 1989, 1990 to 1994, 1995 to 2000), DIM class, milk production level (high, medium, low based on SD), service month, the covariate of cow age at calving, and 2- and 3-way interactions. Over time, an increase was observed for milk production and an overall decline in CR occurred. Examination of month by milk production subclass least squares means showed that in cool months (November to April) the deterioration of CR over time was small for low and medium milk production cows and virtually none for high-producing cows. However, in other months (May to June), there was a large decline over time in CR for cows in all milk production level subclasses. The trends in CR by DIM subclasses were examined for the months of February, May, June, and August. There was a general increase in CR with increasing DIM for all months within all birth-year groups. The months of February and August were somewhat similar for CR up to 175 DIM for the different birth-year groups. Much larger differences over time were observed for the months of May and June, and it appeared that for these 2 mo, cows in recent periods did not return to the same level of performance as cows in earlier periods. It may be that there has been a decline over time in the ability of cows to handle the onset of heat stress or the switch to pasture-based management systems.

Ant colony optimization as a method for strategic genotype sampling.

A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures.

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.

Evaluation of methods for computing approximate accuracies of predicted breeding values in maternal random regression models for growth traits in beef cattle.

The objective of this study was to determine the suitability of 2 methods for computing approximate accuracies of predicted breeding values, in which accuracy was defined as the squared correlation between the predicted and true breeding value, when modeling growth traits in beef cattle using random regression (RR) models. The first method (Strabel et al., S-M-B) was designed for use with multitrait models; thus, its use with RR models requires the clustering of measurements into different traits. The second method (Tier and Meyer, T-M) was more general, because it accounted for random coefficients other than zeros and ones and thus it could be used directly when fitting RR models. To investigate the performance of both methods, their results were compared with the true accuracies using a balanced simulated data set. The largest difference between approximate and true average accuracies for direct effects was observed at 205 d when S-M-B was used (4.6% males and 8.8% females). With regard to maternal effects, the largest differences in average accuracies were observed at 205 d in males when S-M-B was used (31.8%) and at the same age in females but when using T-M (33.3%). In general, bias increased for direct effect accuracies in males at the tails of the accuracy range, but for females and for maternal effect accuracies in both sexes, bias increased as accuracy increased. When a population was simulated to create large numbers of progeny for base females that did not have individual records, much greater errors were observed in the regression of approximate values on the true ones. When both approximate methods were compared using a real beef cattle data set, a good agreement was observed, particularly for direct effect accuracies in sires [i.e., at 205 d, the regressions were 0.98 (direct) and 0.95 (maternal) with r(2) over 0.99]. The largest discrepancies for sires between the methods were observed at 205 d for direct (2.7%) and maternal (16.3%) effect accuracies. For dams, the largest differences between methods were also observed at 205 d, 9.3% (direct), and 15.2% (maternal). The differences between methods for nonparent cattle were greater than for dams for maternal effect accuracies but intermediate between sires and dams for direct effect accuracies. In spite of the less biased results provided by T-M, its use could be problematic when employed in evaluations of large populations due to its greater memory and computation requirements (e.g., 170 and 478% more than S-M-B for a population of 11 million).

Environmental effects on conception rates of Holsteins in New York and Georgia.

The purpose of this study was to investigate the compounded impact on conception rates (CR) of the effects of milk production, service month, and days in milk (DIM) by using recent artificial insemination records of Holsteins in New York (NY) and Georgia (GA). Dairy Herd Improvement records were obtained from Dairy Records Management Systems in Raleigh, North Carolina. After removing records with lactations >1 and uncertain and extreme records (records without a calving or birth date, with days to service after calving of <21 or >250, and without the next calving date), the final data set comprised 298,015 service records for 160,879 cows and 23,366 service records for 12,184 cows in NY and GA, respectively, from 2000 to 2003. The analytical model included DIM class, milk-production level, service month, the covariate of cow's age at calving, and all 2-way interactions. The 2 states were analyzed separately. In general across the 2 states, CR declined as milk production increased, and CR declined during the hottest months. Conception rate was similar in NY and GA, at approximately 55% from December to April. In NY, CR declined by approximately 10% in May and June and mostly recovered by July. In GA, the CR started declining in May, bottomed at 31% in September, and did not recover until December. The difference in CR between high- and low-producing cows was 7% in NY and 6% in GA. That difference was the strongest from June to July in GA (15%) and was more uniform in NY. The increase in CR with increasing DIM varied across service season. The CR was nearly flat from 50 to 125 DIM in NY for all seasons, except for a large increasing trend in spring. In GA, there was also an increasing trend in fall. Conception rates were similar in NY and GA between December and May, and were strongly influenced by heat stress in GA from June to November. A decline in CR for reasons other than heat stress was present in both states in late spring. High production resulted in a faster decline of the CR in GA under heat stress. Models analyzing service records should include the DIM x season x region interaction.

Genetic evaluation of growth in a multibreed beef cattle population using random regression-linear spline models.

The objective of this study was to examine the feasibility of using random regression-spline (RR-spline) models for fitting growth traits in a multibreed beef cattle population. To meet the objective, the results from the RR-spline model were compared with the widely used multitrait (MT) model when both were fit to a data set (1.8 million records and 1.1 million animals) provided by the American Gelbvieh Association. The effect of prior information on the EBV of sires was also investigated. In both RR-spline and MT models, the following effects were considered: individual direct and maternal additive genetic effects, contemporary group, age of the animal at measurement, direct and maternal heterosis, and direct and maternal additive genetic mean effect of the breed. Additionally, the RR-spline model included an individual direct permanent environmental effect. When both MT and RR-spline models were applied to a data set containing records for weaning weight (WWT) and yearling weight (YWT) within specified age ranges, the rankings of bulls' direct EBV (as measured via Pearson correlations) provided by both models were comparable, with slightly greater differences in the reranking of bulls observed for YWT evaluations (>or=0.99 for BWT and WWT and >or=0.98 for YWT); also, some bulls dropped from the top 100 list when these lists were compared across methods. For maternal effects, the estimated correlations were slightly smaller, particularly for YWT; again, some drops from the top 100 animals were observed. As in regular MT multibreed genetic evaluations, the heterosis effects and the additive genetic effects of the breed could not be estimated from field data, because there were not enough contemporary groups with the proper composition of purebred and crossbred animals; thus, prior information based on literature values had to be included. The inclusion of prior information had a negligible effect in the overall ranking for bulls with greater than 20 birth weight progeny records; however, the effect of prior information for breeds or groups poorly represented in the data was important. The Pearson correlations for direct and maternal WWT and YWT ranged from 0.95 to 0.98 when comparing evaluations of data sets for which the out-of-range age records were removed or retained. Random regression allows for avoiding the discarding of records that are outside the usual age ranges of measurement; thus, greater accuracies are achieved, and greater genetic progress could be expected.

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.

Fertility traits in spring-calving Aberdeen Angus cattle. 2. Model comparison.

The aim of this study was to investigate the possible superiority of a threshold-linear (TL) approach for calving day (CD) and calving success (CS) analysis in beef cattle over 2 multiple-trait (MT), censored models, considering CD at the first 3 calving opportunities. The CD observations on animals that failed to calve in the latter models were defined as cows being assigned a penalty value of 21 d beyond the last observed CD record within contemporary group (PEN model) or censored CD values that were randomly obtained from a truncated normal distribution (CEN-model). In the TL model, CD records were treated as missing if a cow failed to calve, and parameters were estimated in a TL analysis including CS traits (TLMISS-model). The models included the effects of contemporary group (herd x year of calving x mating management), age at calving, physiological status at mating (lactating or nonlactating cow), animal additive genetic effects, and residual. Field data included 6,763 calving records obtained from first, second, and third parities of 3,442 spring-calving Uruguayan Aberdeen Angus cows. Models were contrasted using a data splitting technique, analyzing correlations between predicted breeding values (PBV) for each pair of subsamples, by rank correlations between PBV obtained with the different models, and by inspecting percentage of sires selected in common using the different approaches at 10 and 25% hypothetical percentages of animals selected. Breeding value correlations of CD between the subsamples for the TLMISS approach were greater (0.67 to 0.68) than correlations for the censored MT models (0.49 to 0.54). Average correlations between PBV of CD in 1 subsample obtained by CEN (PEN, TLMISS) and PBV of CS in the other subsample were -0.53 (-0.55, -0.60) in the first calving opportunity (CO), -0.54 (-0.58, -0.63) in the second CO, and -0.50 (-0.49, -0.58) in the third CO. Rank correlations between PBV for CD in PEN and CEN were high (0.93 to 0.97), but correlations of either method with PBV of CD in TLMISS ranged from 0.50 to 0.71. Common identification of bulls for the top 10% of sires (25% of sires), when selected with PEN/CEN models or the TLMISS model, varied between 50 (44%) and 60 (52%). The use of the TL animal model for genetic evaluation seems attractive for genetic evaluation of fertility traits in beef cattle.