The empirical bias of estimates by restricted maximum likelihood, Bayesian method, and method R under selection for additive, maternal, and dominance models.

Bayesian analysis via Gibbs sampling, restricted maximum likelihood (REML), and Method R were used to estimate variance components for several models of simulated data. Four simulated data sets that included direct genetic effects and different combinations of maternal, permanent environmental, and dominance effects were used. Parents were selected randomly, on phenotype across or within contemporary groups, or on BLUP of genetic value. Estimates by Bayesian analysis and REML were always empirically unbiased in large data sets. Estimates by Method R were biased only with phenotypic selection across contemporary groups; estimates of the additive variance were biased upward, and all the other estimates were biased downward. No empirical bias was observed for Method R under selection within contemporary groups or in data without contemporary group effects. The bias of Method R estimates in small data sets was evaluated using a simple direct additive model. Method R gave biased estimates in small data sets in all types of selection except BLUP. In populations where the selection is based on BLUP of genetic value or where phenotypic selection is practiced mostly within contemporary groups, estimates by Method R are likely to be unbiased. In this case, Method R is an alternative to single-trait REML and Bayesian analysis for analyses of large data sets when the other methods are too expensive to apply.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.     

Utility of on- and off-farm weather records for studies in genetics of heat tolerance

Utility of weather information from on-farm and weather stations was evaluated for the application in studies on the genetics of heat stress. Daily milk yield of 31 primiparous Holstein cows was collected at Tifton, GA, from April 28 to July 19, 1993. Weather information was recorded on-farm and was available from weather stations in Georgia. Analyses used daily average of temperature–humidity index (THI). Effects of threshold of heat stress and the rate of decline in milk after the threshold were estimated. With on-farm weather data, threshold was at THI = 22 and rate of decline was − 1.12 kg of milk per unit of THI measured 2 days before milking. At the Tifton weather station, 3 km away from the farm, the threshold was THI = 20 and the rate was the same. With data from Macon, Columbus, Atlanta, and Athens stations, the threshold was at 20, 21, 20, and 20, respectively, and the rate of decline with a 2 day lag was − 0.88, − 1.02, − 0.90, and − 0.97 kg of milk per unit of THI. Subsequent analysis included 2260 test day records from the same farm from 1993 to 2003 and weather data from Tifton station. The highest rate of decline on milk yield of − 0.22 kg per unit of THI occurred at the threshold of 20 and no lag. For data restricted to 1999–2003, the threshold increased to 22 and the rate to − 0.46 kg per THI unit. Public stations provide satisfactory information for national genetic evaluation for heat stress. Critical parts in such an evaluation are modeling of test days and accounting for changes among farms and weather stations over time.     

Use of genomic recursions and algorithm for proven and young animals for single‐step genomic BLUP analyses – a simulation study

The purpose of this study was to examine accuracy of genomic selection via single‐step genomic BLUP (ssGBLUP) when the direct inverse of the genomic relationship matrix ( G ) is replaced by an approximation of G ^?1 based on recursions for young genotyped animals conditioned on a subset of proven animals, termed algorithm for proven and young animals (APY). With the efficient implementation, this algorithm has a cubic cost with proven animals and linear with young animals. Ten duplicate data sets mimicking a dairy cattle population were simulated. In a first scenario, genomic information for 20k genotyped bulls, divided in 7k proven and 13k young bulls, was generated for each replicate. In a second scenario, 5k genotyped cows with phenotypes were included in the analysis as young animals. Accuracies (average for the 10 replicates) in regular EBV were 0.72 and 0.34 for proven and young animals, respectively. When genomic information was included, they increased to 0.75 and 0.50. No differences between genomic EBV (GEBV) obtained with the regular G ^?1 and the approximated G ^?1 via the recursive method were observed. In the second scenario, accuracies in GEBV (0.76, 0.51 and 0.59 for proven bulls, young males and young females, respectively) were also higher than those in EBV (0.72, 0.35 and 0.49). Again, no differences between GEBV with regular G ^?1 and with recursions were observed. With the recursive algorithm, the number of iterations to achieve convergence was reduced from 227 to 206 in the first scenario and from 232 to 209 in the second scenario. Cows can be treated as young animals in APY without reducing the accuracy. The proposed algorithm can be implemented to reduce computing costs and to overcome current limitations on the number of genotyped animals in the ssGBLUP method.     

Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows

Up to 109,447 records of 49,656 Large White sows were used to evaluate the genetic relationship between number of pigs born dead (BD) and number born alive (BA) in first and later parities. Performance data (n = 30,832) for ultrasound backfat (BF) at the end of the test and days to reach 113.5 kg (AD) were used to estimate their relationships with BD and BA at first parity in a four-trait threshold-linear analysis (TL). Effects were year-farm, contemporary group (CG: farm-farrowing year-farrowing month) and animal additive genetic. At first parity, estimates of heritability were 0.09, 0.09, 0.37, and 0.31 for BA, BD, AD, and BF, respectively. The estimate of genetic correlation between BD and litter size was -0.04 (BD-BA). Corresponding values with test traits were both -0.14 (BD-AD, BD-BF). Estimates of genetic correlation between BA and performance traits were 0.08 (BA-AD) and 0.05 (BA-BF). The two test traits were moderately negatively correlated (-0.22). For later parities, a six-trait (BD, BA in three parities) TL model was implemented. The estimates of additive genetic variances and heritability increased with parity for BD and BA. Estimates of heritabilities were: 0.09, 0.10, and 0.11 for BD, and 0.09, 0.12, and 0.12 for BA in parities one to three, respectively. Estimates of genetic correlations between different parities were high (0.91 to 0.96) for BD, and slightly lower (0.74 to 0.95) for BA. Genetic correlations between BD and BA were low and positive (0.02 to 0.17) for BA in Parities 1 and 2, but negative (-0.04 to -0.10) for BA in Parity 3. Selection for increased litter size should have little effect on farrowing piglet mortality. Intense selection for faster growth and increased leanness should increase farrowing piglet mortality of first-parity sows. A repeatability model with a simple correction for the heterogeneity of variances over parities could be implemented to select against farrowing mortality. The genetic components of perinatal piglet mortality are independent of the ones for litter size in the first parity, and they show an undesirable, but not strong, genetic association in second parity.     

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.     

Genomic-polygenic evaluation of Angus-Brahman multibreed cattle for feed efficiency and postweaning growth using the Illumina 3K chip

The objectives of this study were to determine the fraction of additive genetic variance explained by the SNP from the Illumina Bovine3K chip; to compare the ranking of animals evaluated with genomic-polygenic, genomic, and polygenic models; and to assess trends in predicted values from these 3 models for residual feed intake (RFI), daily feed intake (DFI), feed conversion ratio (FCR), and postweaning BW gain (PWG) in a multibreed Angus-Brahman cattle population under subtropical conditions. Data consisted of phenotypes and genotypes from 620 bulls, steers, and heifers ranging from 100% Angus to 100% Brahman. Phenotypes were collected in a GrowSafe automated feeding facility (GrowSafe Systems, Ltd., Airdrie, Alberta, Canada) from 2006 to 2010. Variance components were estimated using single-trait genomic-polygenic mixed models with option VCE (Markov chain Monte Carlo) of the program GS3. Fixed effects were contemporary group (year-pen), age of dam, sex of calf, age of calf, Brahman fraction of calf, and heterozygosity of calf. Random effects were additive SNP, animal polygenic, and residual effects. Genomic predictions were computed using a model without polygenic effects and polygenic predictions with a model that excluded additive SNP effects. Heritabilities were 0.20 for RFI, 0.31 for DFI, 0.21 for FCR, and 0.36 for PWG. The fraction of the additive genetic variance explained by SNP in the Illumina 3K chip was 15% for RFI, 11% for DFI, 25% for FCR, and 15% for PWG. These fractions will likely differ in other multibreed populations. Rank correlations between genomic-polygenic and polygenic predictions were high (0.95 to 0.99; P < 0.0001), whereas those between genomic-polygenic and genomic predictions were low (0.65 to 0.74; P < 0.0001). Genomic-polygenic, genomic, and polygenic predictions for all traits tended to decrease as Brahman fraction increased, indicating that calves with greater Brahman fraction were more efficient but grew more slowly than calves with greater Angus fraction. Predicted SNP values were small for all traits, and those above and below 0.2 SNP SD were in multiple chromosomes, supporting the contention that quantitative traits are determined by large numbers of alleles with small effects located throughout the genome.     

Seasonal changes in the interactions among leptin, ghrelin, and orexin in sheep

The adaptation of the physiology of an animal to changing conditions of light and food availability is evident at the behavioral and hormonal levels. Melatonin, leptin, ghrelin, and orexin, which exhibit rhythmic secretion profiles under ad libitum feeding conditions, are sensitive to changes in daylength, forming a tight web of interrelationships in the regulation of energy balance. The aim of this study was to determine the effects of central injections of leptin, ghrelin, and orexin on the reciprocal interactions among these hormones and the influence of photoperiod on these responses. Twenty-four ovariectomized and estradiol-implanted ewes were used in a replicated switchback design. The ewes were assigned randomly to 1 of 6 treatment groups, and the treatments were infused into their third ventricles 3 times at 0, 1, and 2 h, with 0 h being at dusk. The treatments were as follows: 1) control, Ringer-Locke buffer; 2) leptin, 0.5 μg/kg BW; 3) ghrelin, 2.5 μg/kg BW; 4) orexin B, 0.3 μg/kg BW; 5) leptin antagonist, 50 μg/kg BW, then ghrelin, 2.5 μg/kg BW; and 6) leptin antagonist, 50 μg/kg BW, then orexin B, 0.3 μg/kg BW. Blood samples (5 mL) were collected at 15-min intervals for 6 h. The administration of leptin increased (P < 0.05) plasma concentrations of melatonin during short-day (ShD) photoperiods and decreased (P < 0.05) them during long-day (LD) photoperiods, whereas ghrelin decreased (P < 0.05) melatonin concentrations during ShD photoperiod, and orexin had no effect (P > 0.1). Leptin attenuated (P < 0.05) ghrelin concentrations relative to the concentration in controls during ShD. The plasma concentrations of orexin were reduced (P < 0.05) after leptin infusions during LD and ShD photoperiods; however, ghrelin had the opposite effect (P < 0.05) on orexin concentration. Orexin increased (P < 0.05) ghrelin concentrations during LD. Ghrelin and orexin concentrations were increased (P < 0.05) after leptin antagonist infusions. Our data provide evidence that the secretion of leptin, ghrelin, and orexin are seasonally dependent, with relationships that are subject to photoperiodic regulation, and that leptin is an important factor that regulates ghrelin and orexin releases in sheep.     

Central injection of exogenous IL-1β in the control activities of hypothalamic-pituitary-gonadal axis in anestrous ewes

This study was performed to determine the effect of intracerebroventricular (icv) injection of interleukin (IL)-1β on the gene expression, translation and release of gonadotropin-releasing hormone (GnRH) and the GnRH receptor (GnRHR) gene expression in the hypothalamus of anestrous ewes. In the anterior pituitary gland (AP), the expression of genes encoding: GnRHR, β subunits of luteinizing hormone (LH) and folliculotropic hormone (FSH) was determined as well as the effect of IL-1β on pituitary gonadotropins release. The relative mRNA level was determined by real-time PCR, GnRH concentration in the cerebrospinal fluid (CSF) was assayed by ELISA and the plasma concentration of LH and FSH were determined by radioimmunoassay. Our results showed that icv injection of IL-1β (10 or 50 μg/animal) decreased the GnRH mRNA level in the pre-optic area (POA) (35% and 40% respectively; p ≤ 0.01) and median eminence (ME) (75% and 70% respectively; p ≤ 0.01) and GnRHR gene expression in ME (55% and 50% respectively; p ≤ 0.01). A significant decrease in GnRHR mRNA level in the AP in the group treated with the 50 μg (60%; p ≤ 0.01) but not with the 10 μg dose was observed. The centrally administrated IL-1β lowered also GnRH concentration in the CSF (60%; p ≤ 0.01) and reduced the intensity of GnRH translation in the POA (p ≤ 0.01). It was not found any effect of icv IL-1β injection upon the release of LH and FSH. However, the central injection of IL-1β strongly decreased the LHβ mRNA level (41% and 50%; p ≤ 0.01; respectively) and FSHβ mRNA in the case of the 50 μg dose (49%; p ≤ 0.01) in the pituitary of anestrous ewes. These results demonstrate that the central IL-1β is an important modulator of the GnRH biosynthesis and release during immune/inflammatory challenge.