J. K. Bertrand

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 cows 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.

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.

Biceps femoris and rump fat as additional ultrasound measurements for predicting retail product and trimmable fat in beef carcasses.

One hundred ninety-eight steers of Angus and Hereford breeding were evaluated ultrasonically for fat thickness over the 12-13th rib (UFAT), fat thickness over the rump (URUMP), 12-13th longissimus muscle area (UREA), and depth of the biceps femoris (UROUND) before slaughter. Carcass measurements associated with the USDA yield grade were also obtained. Carcasses were fabricated into closely trimmed (.32 cm fat), boneless subprimals. Regression procedures were used to predict weight and the percentages of retail product and trimmable fat. Final weight (FINALWT) accounted for most of the variation when predicting kilograms of retail product and trimmable fat, with R2 values of .836 and .435, respectively. As single predictors URUMP and UFAT accounted for most of the variation when predicting the percentages of retail product and trimmable fat with R2 values of .244 and .220, respectively. Adding URUMP to equations that included FINALWT, UREA, and UFAT increased R2 values for percentage of retail product from .175 to .318 and for weight of retail product from .847 to .865, whereas the addition of UROUND did not appreciably increase R2 values for the same models. Adding URUMP and UROUND to the model of FINALWT, UREA, and UFAT to predict kilograms and the percentage of trimmable fat increased R2 values from .530 to .610 and from .254 to .360, respectively. Models using live-animal measurements to predict weight and the percentage of retail product gave R2 values equal to models using the actual measurements found in the USDA Yield Grade equation.

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus1

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.

The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification.

The use of gene expression data to diagnose complex diseases represents an exciting area of medicine; however, such data sets are often noisy, requiring the selection of feature subsets to obtain maximum classification accuracy. Due to the high dimensions of many expression data sets, filter-based methods are commonly used, but often yield inconsistent results. Optimization algorithms can outperform filter methods, but often require preselection of features to achieve good results. To address the problems of many commonly used feature selection methods, the ant colony algorithm (ACA) is proposed for use on data sets with large numbers of features. The ACA is an optimization algorithm capable of incorporating prior information, allowing it to search the sample space more efficiently than other optimization methods. When applied to several high-dimensional data sets, the ACA was able to identify small subsets of highly predictive and biologically relevant genes without the need for extensive preselection of features. Using the selected genes to train a latent variable model yielded substantial increases in prediction accuracy when compared to several rank-based methods and results obtained in previous studies. The superiority of the ACA algorithm was validated through simulation.

Applications of an Animal Model in the United States Beef Cattle Industry

Abstract The theory of mixed linear models is finding widespread application in the United States beef cattle industry. At least 15 beef breeds have developed or are in the process of developing national genetic improvement programs based on best linear unbiased prediction procedures and the animal model (or reduced animal model). These 15 breeds represent over 600,000 new registrations each year. The commercial industry is moving rapidly toward acceptance of genetic values on yearling bulls from these programs. Both single trait and multiple trait analyses are conducted depending on breed and traits analyzed. All breeds have developed models for maternally influenced traits. At present, primary emphasis is on growth; however, some breeds have included such traits as calving ease and hip height. Interest is developing among breeders for genetic evaluations of carcass traits. Procedures have been developed for generating genetic values on a daily basis for young animals that are not included in the major analys is due to the time of year their records were obtained. These interim genetic values provide information between major analyses.

A practical longitudinal model for evaluating growth in Gelbvieh cattle

Genetic evaluation of growth in Gelbvieh beef cattle was examined by multiple-trait (MTM) and random regression (RRM) analysis. The data set comprised 541,108 animals with 1,120,086 records. Approximately 15% of the animals in the data set had at least one record measured outside of the accepted MTM age ranges for weaning weight (Wwt) and yearling weight (Ywt). Fourteen percent of Wwt records and 19% of Ywt records were measured outside the accepted ranges for MTM analysis, and thus were excluded from MTM evaluations. Two RRM evaluations were performed using cubic Legendre polynomials (RRML) and linear splines (RRMS) with three knots at 1, 205, and 365 d of age. Data Set 1 (d1) utilized all available records, whereas Data Set 2 (d2) included only records measured within MTM ranges (1 d, 160 to 250 d, and 320 to 410 d). The RRML models did not reach convergence until diagonalization was imposed. After diagonalization, it was found that all longitudinal models required fewer iterations to converge than the MTM. Correlations between the MTM, RRML-d2, and RRMS-d2 evaluations were >or=0.99 for all three traits, indicating that these models were equivalent when predicting breeding values from data within the MTM age ranges. Correlations between MTM, RRML-d1, and RRMS-d1 were >0.99 for Bwt and >0.95 for Wwt and Ywt. The lower correlations for Wwt and Ywt indicate that the added information does affect breeding value prediction. The RRM has the capability to incorporate records measured at all ages into genetic evaluations at a computing cost similar to the MTM.

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.

Selection for low birth weight and high yearling weight in angus beef cattle

Abstract Angus bulls were chosen for either high birth weight (BW), high yearling weight (YW), or low BW, high YW using Expected Progeny Difference (EPD) estimates for proven sires. The high group had birth weight EPD ⩾ 3.0 kg and yearling weight EPD ⩾ 20 kg while the low group had birth weight EPD ⩽ 1.5 kg and yearling weight EPD ⩾ 20 kg. The selected sires were randomly mated to registered Angus cows over 4 years and resulting progeny data were analyzed for BW, weaning weight, post-weaning gain, YW, gestation length and yearling pelvic measurements. High group least squares means (LSM) for BW exceeded the low group by 3.8 kg ( P P > 0.10) were seen for weaning weights, post-weaning gain or yearling weights although actual LSM were in close agreement with sire EPD differences between groups. Yearling weight LSM differed by 6.6 kg compared with a 6.3 kg difference between high and low groups for sire yearling weight EPD means. Pelvic measurements were comparable between groups ( P > 0.10). Results of this study indicate that antagonistic selection for low BW and high growth rate is feasible using EPD. Therefore, the potential exists for increasing the genetic merit for growth with minimal birth problems associated with birth weight.

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.