S. Tsuruta

Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score1

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes.

Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens

Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based.

Genetic components of heat stress for dairy cattle with multiple lactations

Data included 585,119 test-day records for milk, fat, and protein yields from the first, second, and third parities of 38,608 Holsteins in Georgia. Daily temperature-humidity indexes (THI) were available from public weather stations. Models included a repeatability test-day model with a random regression on a function of THI and a test-day random regression model using linear splines with knots at 5, 50, 200, and 305 d in milk and a function of THI. Random effects were additive genetic and permanent environmental in the repeatability model and additive genetic, permanent environmental, and herd year in the random regression model. Additionally, models included fixed effects for herd test day, calving age, milking frequency, and lactation stage. Phenotypic variance increased by 50 to 60% from the first to second parity for all yield traits with the repeatability model and by 12 to 15% from the second to third parity. General additive genetic variance increased by 25 to 35% from the first to second parity for all yield traits but decreased slightly from the second to third parity for milk and protein yields. Genetic variance for heat tolerance doubled from the first to second parity and increased by 20 to 100% from the second to third parity. Genetic correlations among general additive effects were lowest between the first and second parities (0.84 to 0.88) and were highest between the second and third parities (0.96 to 0.98). Genetic correlations among parities for the effect of heat tolerance ranged from 0.56 to 0.79. Genetic correlations between general and heat-tolerance effects across parities and yield traits ranged from -0.30 to -0.50. With the random regression model, genetic variance for heat tolerance for milk yield was approximately one-half that of the repeatability model. For milk yield, the most negative genetic correlation (approximately -0.45) between general and heat-tolerance effects was between 50 and 200 d in milk for the first parity and between 200 and 305 d in milk for the second and third parities. The genetic variance of heat tolerance increased substantially from the first to third parity. Genetic estimates of heat tolerance may be inflated with the repeatability model because of timing of lactations to avoid peak yield during hot seasons.

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.

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.

Short Communication: Genotype by Environment Interaction Due to Heat Stress

Heat stress was evaluated as a factor in differences between regional evaluations for milk yield in the United States. The national data set (NA) consisted of 56 million first-parity, test-day milk yields on 6 million Holsteins. The Northeastern subset (NE) included 12.5 million records on 1.3 million first-calved heifers from 8 states, and the Southeastern subset (SE) included 3.5 million records on 0.4 million heifers from 11 states. Climatic data were available from 202 public weather stations. Each herd was assigned to the nearest weather station. Average daily temperature-humidity index (mean THI) 3 d before test date was used as an indicator of heat stress. Two test-day repeatability models were implemented. Effects included in both models were herd-test date, age at calving class, frequency of milking, days in milk x season class, additive genetic (regular breeding value) and permanent environmental effects. Additionally, the second model included random regressions on degrees of heat stress (t = max[0, mean THI - 72]) for additive genetic (breeding value for heat tolerance) and permanent environmental effects. Both models were fitted with the national and regional data sets. Correlations involved estimated breeding values (EBV) from SE and NE for sires with >or=100 and >or=300 daughters in each region. When heat stress was ignored (first model) the correlations of regular EBV between SE and NE for sires with >or=100 (>or=300) daughters were 0.85 (0.87). When heat stress was considered (second model), the correlation increased by up to 0.01. The correlations of heat stress EBV between NE and SE for sires with >or=100 (>or=300, >or=700) daughters were 0.58 (0.72, 0.81). Evaluations for heat tolerance were similar in cooler and hotter regions for high-reliability sires. Heat stress as modeled explains only a small amount of regional differences, partly because test-day records depict only snapshots of heat stress.

Methods to approximate reliabilities in single-step genomic evaluation.

Reliability of predictions from single-step genomic BLUP (ssGBLUP) can be calculated by matrix inversion, but that is not feasible for large data sets. Two methods of approximating reliability were developed based on the decomposition of a function of reliability into contributions from records, pedigrees, and genotypes. Those contributions can be expressed in record or daughter equivalents. The first approximation method involved inversion of a matrix that contains inverses of the genomic relationship matrix and the pedigree relationship matrix for genotyped animals. The second approximation method involved only the diagonal elements of those inverses. The 2 approximation methods were tested with a simulated data set. The correlations between ssGBLUP and approximated contributions from genomic information were 0.92 for the first approximation method and 0.56 for the second approximation method; contributions were inflated by 62 and 258%, respectively. The respective correlations for reliabilities were 0.98 and 0.72. After empirical correction for inflation, those correlations increased to 0.99 and 0.89. Approximations of reliabilities of predictions by ssGBLUP are accurate and computationally feasible for populations with up to 100,000 genotyped animals. A critical part of the approximations is quality control of information from single nucleotide polymorphisms and proper scaling of the genomic relationship matrix.

Multiple trait genomic evaluation of conception rate in Holsteins.

A national data set of artificial inseminations in US Holsteins was used to obtain genetic evaluations for conception rate (CR). The objective of this study was to investigate the feasibility and resulting accuracy from using all available phenotypic, pedigree, and genomic information. Evaluations were performed by regular BLUP or by BLUP with the traditional pedigree and genomic relationships combined in a unified single-step procedure (SSP). Genetic parameters of CR in the first 3 parities were estimated with data from New York State only. Heritability estimates were around 2% and genetic correlations between CR in different parities were >0.73. The R(2) obtained with the SSP were almost twice as large as those achieved with regular BLUP. Computing the SSP took 2h, and it was 33% slower than a regular BLUP. A multiple-trait evaluation of CR using the SSP is both possible and advantageous.

Genetic components of heat stress in finishing pigs: Development of a heat load function

The objective of this study was to quantify the effect of heat stress during the life of a pig on its final weight, as a first step toward a genetic evaluation for heat tolerance. Data included carcass weights of 23,556 crossbred pigs [Duroc x (Landrace x Large White)] raised on 2 farms in North Carolina and slaughtered from May 2005 through December 2006. Weather data were available from a nearby weather station. Lifetime of a pig was assumed to be partitioned into 2 periods. During an initial period, the effect of heat stress was assumed to be negligible or compensated for later. During the second period ending in slaughtering, the ADG was assumed to be affected linearly by heat load. Weekly heat load was calculated as degrees of average temperature-humidity index in excess of a threshold (18 degrees C). The total heat load (H) was the sum of weekly heat loads during the second period. During the months of January to May H was 0; H reached a peak in September. The final BW during the peak of heat stress decreased about 6 kg compared with BW during months of non-heat stress. Weekly and monthly averages of carcass weight generally moved similarly to H. However, there were large fluctuations unrelated to H; the fluctuations were different on the 2 farms. The model included the effects of farm-year of slaughter, sex, age at slaughter, and H, where age at slaughter and H were linear regressions. In analyses, the threshold was varied from 16 to 20 degrees C, and the second period was varied from 8 to 16 wk. The greatest R(2) (10.4%) was at the threshold of temperature-humidity index = 18 degrees C for a period of 10 wk. Varying the threshold and the length of time reduced R(2) less than 1%. Least squares means of year-month and year-week of carcass weight were calculated using a model with the fixed effects farm-year-month or farm-year-week of slaughter, sex, and age at slaughter (linear covariate), and the random effect of birth litter. Changes in BW of finisher pigs due to heat stress can be quantified by H during the last 10 wk of the life of the pig.

A Bayesian threshold-linear model evaluation of perinatal mortality, dystocia, birth weight, and gestation length in a Holstein herd1

The objective of this research was to estimate genetic parameters for a multiple-trait evaluation of dystocia (DYS), perinatal mortality (PM), birth weight (BWT), and gestation length (GL) in Holsteins. The data included 5,712 calving records collected between 1968 and 2005 from the Iowa State University dairy breeding herd in Ankeny. The incidence of PM was 8.8% and that of DYS 28.8%; mean BWT was 40.5 kg, and GL was 279 d. A threshold-linear animal model included the effects of year, season, sex of calf, parity, sire group, direct genetic, maternal genetic, and maternal permanent environment. Direct heritabilities for DYS, PM, BWT, and GL were 0.11 (0.04), 0.13 (0.05), 0.26 (0.04), and 0.51 (0.05), respectively. Maternal heritabilities were 0.14 (0.04), 0.15 (0.03), 0.08 (0.01), and 0.08 (0.02), for DYS, PM, BWT, and GL, respectively. The heritabilities are the posterior means of the Gibbs samples with their standard deviations in parentheses. The direct genetic correlation between PM and DYS was estimated at 0.67 (0.19), whereas the maternal genetic correlation was 0.45 (0.16). Direct and maternal PM and DYS are partially controlled by the same genes. Selection on only calving ease is not sufficient to control PM. With moderate genetic correlations between all 4 traits, BWT and GL should be included with DYS and PM in an evaluation of calving performance.