Average Information Restricted Maximum Likelihood Research Articles

Genetic Profiles of Purine, Uric Acid, Superoxide Dismutase, and Growth in Thai Slow-Growing Chickens.

The objective of this study was to estimate genetic parameters and genetic correlations between growth characteristics and purine and uric acid in the breast and liver and superoxide dismutase (SOD) in the blood. The growth characteristics included body weight (BW) at hatching (BW0), BW at 2, 4, 6, 8, and 10 weeks of age, average daily gain (ADG) at 0-2, 2-4, 4-6, 6-8, and 8-10 weeks of age, and breast circumference at 6, 8, and 10 weeks of age (BrC6, BrC8, and BrC10) were recorded from 300 Thai native chickens (Shee breed). In total, 30 chickens (15 males and 15 females) were randomly euthanized to collect breast meat, liver, and blood samples to determine the purine content. A multiple-trait animal model and an average information-restricted maximum likelihood (AI-REML) were used to estimate the variance components and genetic parameters. The estimated heritability values for all growth traits were moderate and ranged from 0.304 to 0.485, 0.270 to 0.335, and 0.286 to 0.314 for BW, ADG, and BrC, respectively. The estimated heritability values for various biochemical traits, including purine content, uric acid, and SOD levels, were low to moderate and ranged from 0.035 to 0.143, and 0.050 to 0.213 in breast meat and liver, respectively. In genetic correlations, total purine content showed a strong negative correlation with growth traits, whereas uric acid and SOD levels exhibited varying degrees of correlation with BW and ADG. These results highlight the importance of genetic parameters between growth and biochemical traits in Thai native chickens and provide valuable insights for breeding programs aimed at improving growth performance and meat quality. This study indicated the potential use of heritability values and genetic correlations to enhance selective breeding strategies using the multiple-trait genetic evaluation method for optimal trait combinations in poultry.

Genetic parameter estimation of preweaning growth traits in Akkaraman sheep.

This study aimed to evaluate (co)variance components and heritability of pre-weaning body weight traits and the Kleiber ratio in Akkaraman sheep. Data collected between 2018 and 2023 from Çankırı province, Türkiye, formed the basis of this research. The considered traits were birth weight (BW), weaning weight (WW), average pre-weaning daily weight gain (ADWG), and Kleiber ratio (KR). Utilizing animal mixed models and the average information restricted maximum likelihood (AI-REML) procedure, genetic parameters were estimated. Direct heritability estimates were 0.30 ± 0.02, 0.23 ± 0.02, 0.23 ± 0.02, and 0.28 ± 0.03 for BW, WW, ADWG, and KR, respectively. Maternal genetic heritabilities were 0.03 for WW and 0.02 for WW, ADWG, and KR. Direct genetic correlations among pre-weaning growth traits were negative for BW-WW and BW-ADWG and positive for WW-ADWG at -0.05, -0.23, and 0.98, respectively. The moderate direct heritability estimate suggests the presence of genetic variability in the Akkaraman sheep breed, indicating potential genetic progress through selective breeding. Despite the low maternal permanent environmental effects, the significant impact of maternal care on lamb growth in this breed underscores the importance of considering both direct and maternal effects in the genetic evaluation of early growth traits. The observed negative genetic correlations among birth weight, weaning weight, and daily live weight gain until weaning emphasize the need for careful consideration in selection programs for the Akkaraman breed.

A computationally efficient algorithm to leverage average information REML for (co)variance component estimation in the genomic era

BackgroundMethods for estimating variance components (VC) using restricted maximum likelihood (REML) typically require elements from the inverse of the coefficient matrix of the mixed model equations (MME). As genomic information becomes more prevalent, the coefficient matrix of the MME becomes denser, presenting a challenge for analyzing large datasets. Thus, computational algorithms based on iterative solving and Monte Carlo approximation of the inverse of the coefficient matrix become appealing. While the standard average information REML (AI-REML) is known for its rapid convergence, its computational intensity imposes limitations. In particular, the standard AI-REML requires solving the MME for each VC, which can be computationally demanding, especially when dealing with complex models with many VC. To bridge this gap, here we (1) present a computationally efficient and tractable algorithm, named the augmented AI-REML, which facilitates the AI-REML by solving an augmented MME only once within each REML iteration; and (2) implement this approach for VC estimation in a general framework of a multi-trait GBLUP model. VC estimation was investigated based on the number of VC in the model, including a two-trait, three-trait, four-trait, and five-trait GBLUP model. We compared the augmented AI-REML with the standard AI-REML in terms of computing time per REML iteration. Direct and iterative solving methods were used to assess the advances of the augmented AI-REML.ResultsWhen using the direct solving method, the augmented AI-REML and the standard AI-REML required similar computing times for models with a small number of VC (the two- and three-trait GBLUP model), while the augmented AI-REML demonstrated more notable reductions in computing time as the number of VC in the model increased. When using the iterative solving method, the augmented AI-REML demonstrated substantial improvements in computational efficiency compared to the standard AI-REML. The elapsed time of each REML iteration was reduced by 75%, 84%, and 86% for the two-, three-, and four-trait GBLUP models, respectively.ConclusionsThe augmented AI-REML can considerably reduce the computing time within each REML iteration, particularly when using an iterative solver. Our results demonstrate the potential of the augmented AI-REML as an appealing approach for large-scale VC estimation in the genomic era.

Estimates of genetic parameters and genetic trends for growth traits in Menz sheep under community-based breeding programs

The main objectives of this study were to estimate genetic and phenotypic parameters for growth traits in Menz sheep under community-based breeding programs (CBBPs). Data used in the study were collected from five Menz CBBPs villages during 15-year period (2009–2023). Studied growth traits were birth weight (BW), weaning weight (WW), six-month weight (6 MW), yearling weight (YW), Average daily gain from birth to weaning (ADG1), average daily gain from weaning to six months (ADG2) and average daily gain from six months to yearling (ADG3). Fixed effects were estimated using the GLM procedure of SAS 9.4. Genetic parameters were estimated fitting different univariate animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure with WOMBAT software. The most appropriate model for each trait was determined applying likelihood ratio tests. Model including only direct additive genetic effect was the most appropriate model for BW, YW, ADG2 and ADG3, whereas model including direct additive and maternal additive genetic effect without taking into account covariance between them was the most appropriate for WW, 6 MW and ADG1. Based on the most appropriate models, direct heritability of BW, WW, 6 MW, YW, ADG1, ADG2 and ADG3 were 0.29 ± 0.01, 0.45 ± 0.01, 0.46 ± 0.02, 0.42 ± 0.02, 0.44 ± 0.01, 0.75 ± 0.02 and 0.21 ± 0.02, respectively. The estimated maternal heritability were 0.04 ± 0.01, 0.02 ± 0.01 and 0.02 ± 0.01 for WW, 6 MW and ADG1, respectively. Additive coefficient of variance were ranged from 9.53 % for BW to 33.02 % for ADG2. Moderate to higher estimates of direct heritability and additive coefficient of variation (CVA) in the current study indicates higher potential for genetic improvement of studied traits through direct selection. The genetic trend for the Menz sheep's six-month weight in Molale CBBPs village reveals a promising annual growth rate of 0.20 kg. Optimizing the ongoing breeding program into specializing-sire producer groups and applying intensive selection on the sub-nucleus flock could improve the efficiency of the breeding program. Genetic correlations among the studied traits were ranged from −0.50 for ADG-ADG2 to 0.98 for WW-ADG1. Genetic correlation between 6 MW and other studied traits was positive and moderate in magnitude. To achieve optimum genetic gain, the implemented selection criteria for Menz sheep (6 MW) should be continued.

Estimates of genetic parameters for growth traits in dorper crossbred sheep population.

The study was carried out to estimate genetic and phenotypic parameters for growth traits in Dorper crossbred sheep. The data set consisted of 5717 growth records from 1347 individuals of Dorper 50% crossbred sheep descended from 43 sires and 344 dams born between the years 2012 and 2022 at Debre Birhan Agricultural Research Center sheep research station, Ethiopia. Studied traits were birth weight (WT0), weaning weight (WT3), six months weight (WT6), yearling weight (WT12), average daily gain from birth to weaning (ADG1), average daily gain from weaning to six months (ADG2), average daily gain from six months to yearling (ADG3). The (co)variance components were estimated by fitting six different univariate animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure. Contrary to the genetic trend, phenotypic performance for all studied traits showed a declining trend over the years. Direct heritability estimates of 0.10 ± 0.06, 0.29 ± 0.09, 0.37 ± 0.10, 0.10 ± 0.09, 0.43 ± 0.15, 0.04 ± 0.05, and 0.14 ± 0.09 were obtained for WT0, WT3, WT6, WT12, ADG1, ADG2 and ADG3, respectively. Genetic correlations among the studied traits ranged from -0.43 (between ADG2 and ADG3) to 0.99 (between WT3 and ADG1). Selection for weaning, six months and pre-weaning average daily gain would be expected to yield good response as these traits were found moderately heritable. Strong to moderate genetic correlation of WT3 with WT6, WT12, and ADG1 suggested that selection based on WT3 would result in improvement of other growth traits due to correlated response.

Prediction of body condition score throughout lactation by random regression test-day models.

Regular monitoring of body condition score (BCS) changes during lactation is a crucial management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The aim of this study was to investigate the ability of random regression test-day models (RR-TDM) to predict BCS for the entire lactation in dairy cows even if the actual scoring is limited to one BCS record. The data consisted of test-day records of milk yield (MY), fat percentage (FP), protein percentage (PP) and BCS (based on a 9-point scale with unit increments; 1-9) collected from 2014 to 2022 in 128 herds in the Walloon Region of Belgium. In total, 20,698 test-day records on 2166 first-parity Holstein cows (2-12 with an average of 9.42 test-day records per cow) were available for MY, FP and PP; and 7985 records on the same animals (2-12 with an average of 3.68 records per cow) were available for BCS. To estimate the solutions, only one randomly selected BCS record per animal along with all her MY, FP and PP records were used, which were then used to predict BCS data (calibration set). The remaining BCS (1-11 with an average 2.86 BCS records per animal) were used to evaluate the goodness of the predictions (validation set). Multiple-trait RR-TDM was used to estimate (co)variance components through the average information restricted maximum likelihood (AI-REML) algorithm. Predicted BCS were grouped into nine classes as the original observed BCS used for comparison. Pearson correlation between the predicted and observed BCS, prediction error (PE), absolute prediction error (APE) and root mean squared prediction error (RMSE) were calculated. Mean (standard deviation; SD) BCS was 4.97 (1.01), 4.95 (1.07) and 4.98 (1.00) BCS units in the full, calibration and validation datasets, respectively. Pearson correlation between the observed and predicted BCS was 0.71, mean (SD) PE was 0.04 (0.52) BCS units, mean (SD) APE was 0.48 (0.53) BCS units and RMSE was 0.72 BCS units. These findings demonstrate the ability of RR-TDM to predict BCS for the entire lactation using a single BCS record along with available test-day records of milk yield and composition in Holstein dairy cows.

Estimation of Genetic Parameters of Early Growth Traits in Dumeng Sheep.

This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits.

Estimation of Additive and Dominance Genetic Variances for Growth and Multiple Stress at Different Ages in Pacific White Shrimp Litopenaeus vannamei

AbstractThis study aimed to estimate the additive and dominance genetic variance in growth and multiple‐stress tolerance traits in 7‐week‐old and 15‐week‐old Pacific white shrimp Litopenaeus vannamei. Four different animal models were studied by including all or different subsets of the following effects: additive genetic effects (A), additive genetic and common environmental effects (A + C), additive genetic and dominance effects (A + D), and additive, common environmental, and dominance effects (A + D + C). Variance components were estimated using the average information restricted maximum likelihood method. In general, estimates of additive genetic variance were inflated under the simple model (A) and decreased remarkably under the more complex models (A + C, A + D, and A + D + C). The genetic parameters of two‐stage (7‐week and 15‐week shrimp) growth and multiple‐stress tolerance traits were more suitable for estimation using the A + C model. The additive effects (0.311–0.754) of seven traits in 7‐week shrimp were greater than the dominance effects (4.950 × 10−7 to 0.201), whereas the additive effects of the same seven traits in adult shrimp ranged from 7.712 × 10−8 to 0.468 and the dominance effects ranged from 0.074 to 0.577. Except for survival time, carapace length, and carapace width, the additive effects of the other traits were smaller than the corresponding dominance effects. Thus, better genetic improvement of 7‐week and 15‐week shrimp growth and multiple‐stress tolerance can be obtained with selective breeding than with crossbreeding and better genetic improvement of 7‐week shrimp growth traits can be obtained with crossbreeding than with selective breeding. Our study further enriches the database for enhancing growth and stress resistance in Pacific white shrimp and provides reference information for growth improvement and multiple‐stress resistance breeding.

Genetic analysis of efficiency-related traits in Boer x Central Highland goats.

This study aimed to identify important non-genetic factors and estimate genetic parameters for efficiency-related traits in Boer x Central Highland goats. The genetic parameters were estimated using the Average Information Restricted Maximum Likelihood algorithm using the WOMBAT program fitting animal model. The least-squares means for growth efficiency from birth to 3 months (GE1), 3-6 months (GE2), 6-12 months (GE3), relative growth rate from birth to 3 months (RGR1), 3-6 months (RGR2) and 6-12 month (RGR3) were 294.0 ± 5.06, 36.6 ± 1.20, 44.9 ± 1.81, 1.46 ± 0.01, 0.32 ± 0.01 and 0.19 ± 0.01, respectively. Birth type, blood level, sex of the kid, and year of kidding had a sizable effect on efficiency-related traits. About 18, 3.0, 23, 20, and 12% of the phenotypic variation in GE2, GE3, RGR1, RGR2, and RGR3 was explained by the direct additive genetic effect. Except for RGR3, all investigated traits were under the influence of maternal genetic effect, and maternal heritability ranged from 0.09 to 0.17. The total heritability estimate depicts that slow genetic progress would be expected from selection. Nevertheless, even with this level of heritability, selection for efficiency-related traits would improve the efficiency of chevon production as these traits are economically important traits. Nearly six-months of age was when farmers sold Boer crossbred goats. Therefore, improving the growth efficiency till the marketing age (GE2) in such a scenario could increase the production efficiency.

Estimation of heritability with genomic information by method R.

Estimating heritabilities with large genomic models by established methods such as restricted maximum likelihood (REML) or Bayesian via Gibbs sampling is computationally expensive. Alternatively, heritability can be estimated indirectly by method R and by maximum predictivity, referred to as MaxPred here, at a much lower computing cost. By method R, the heritability used for predictions with whole and partial data is considered the best estimate when the predictions based on partial data are unbiased relative to those with the complete data. By MaxPred, the heritability estimate is the one that maximizes predictivity. This study compared heritability estimation with genomic information using average information REML (AI-REML), method R and MaxPred. A simulated population was generated with ten generations of 5000 animals each and an effective population size of 80. Each animal had one record for a trait with a heritability of 0.3, a phenotypic variance of 10.0 and was genotyped at 50 k SNP. In method R, the heritability estimate is found when the expectation of a regression coefficient is equal to one. The regression is the EBV of selection candidates calculated with the whole dataset regressed on the EBV of candidates calculated from a partial dataset. In this study, we used the GBLUP framework and therefore, GEBV was calculated. The partial dataset was created by removing the last generation of phenotypes. Predictivity was defined as the correlation between the adjusted phenotypes of the selection candidates and their GEBV calculated from the partial data. We estimated the heritability for populations that included between three and 10 generations. In every scenario, predictivity increased as more data was used and was the highest at the simulated heritability. However, the predictivity for all data subsets and all heritabilities compared did not differ more than 0.01, suggesting MaxPred is not the best indication for heritability estimation. For the whole dataset, the heritability was estimated as 0.30 ± 0.01, 0.26 ± 0.01 and 0.30 ± 0.04 for AI-REML without genomics, AI-REML with genomics and method R with genomics, respectively. Heritability estimation with genomics by method R reduced timing by 83%, implying a reduction in computing time from 9.5 to 1.6 h, on average, compared to AI-REML with genomics. Method R has the potential to estimate heritabilities with large genomic information at a low cost when many generations of animals are present; however, the standard error can be high when only a few iterations are used.

Genetic parameters for ewe lifetime productivity traits in Central Anatolian Merino sheep

Rapidly increasing population size, recent breaks in production and supply chains, due to pandemics, and those further expected because of climate change emphasize the compelling importance of animal productivity. Traits associated with the productivity of ewes have a direct impact on the overall productivity, profitability and efficiency of sheep farms. Therefore, the objective of the current study was to estimate the variance components, heritabilities as well as genetic and phenotypic correlations, for traits associated with the lifetime productivity of Central Anatolian Merino ewes. Birth and weaning records of lambs for each ewe as well as environmental factors such as sex and birth type were utilized for deriving ewe lifetime productivity traits. Genetic parameter estimations involved observations from 3101 Central Anatolian Merino ewes, encompassing mean and total lamb birth weight per ewe (MBW and TBW), mean and total lamb weaning weight per ewe (MWW and TWW), and the mean and total number of lambs weaned (MNLW and TNLW). This analysis utilized a pedigree comprising 13229 animals. Average Information Restricted Maximum Likelihood (AIREML) based Animal Linear Mixed Model estimations for the decomposition of (co)variance components were implemented based on pedigree-derived numerator relationship matrix by also accounting for the number of parities as a fixed factor. Low to moderate heritability estimates, ranging between 0.29 and 0.42, were observed for the lifetime productivity traits of the breed. A wide range of genetic and phenotypic correlations (between −0.56 and 0.99) was observed among the traits of interest. The outcomes of the study are anticipated to significantly enhance the global sheep production systems by uncovering the essential parameters for incorporating ewe’s lifetime reproductive performance and productivity into the selection indices. This will also benefit Central Anatolian Merino farmers, who form a large proportion of sheep producers in Turkey.

Genetic background of hematological parameters in Holstein cattle based on genome-wide association and RNA sequencing analyses

Hematological parameters refer to the assessment of changes in the number and distribution of blood cells, including leukocytes (LES), erythrocytes (ERS), and platelets (PLS), which are essential for the early diagnosis of hematological system disorders and other systemic diseases in livestock. In this context, the primary objectives of this study were to investigate the genomic background of 19 hematological parameters in Holstein cattle, focusing on LES, ERS, and PLS blood components. Genetic and phenotypic (co)variances of hematological parameters were calculated based on the Average Information Restricted Maximum Likelihood (AIREML) method and 1,610 genotyped individuals and 5,499 hematological parameter records from 4,543 cows. Furthermore, we assessed the genetic relationship between these hematological parameters and other economically important traits in dairy cattle breeding programs. We also carried out genome-wide association studies and candidate gene analyses. Blood samples from 21 primiparous cows were used to identify candidate genes further through RNA sequencing (RNA-seq) analyses. Hematological parameters generally exhibited low-to-moderate heritabilities ranging from 0.01 to 0.29, with genetic correlations between them ranging from -0.88 ± 0.09 (between mononuclear cell ratio and lymphocyte cell ratio) to 0.99 ± 0.01 (between white blood cell count and granulocyte cell count). Furthermore, low to moderate approximate genetic correlations between hematological parameters with one longevity, 4 fertility, and 5 health traits were observed. One-hundred-and-99 significant single nucleotide polymorphisms (SNP) located primarily on the Bos taurus autosomes (BTA) BTA4, BTA6, and BTA8 were associated with 16 hematological parameters. Based on the RNA-seq analyses, 6,687 genes were significantly downregulated and 4,119 genes were upregulated when comparing 2 groups of cows with high and low phenotypic values. By integrating genome-wide association studies (GWAS), RNA-seq, and previously published results, the main candidate genes associated with hematological parameters in Holstein cattle were ACRBP, ADAMTS3, CANT1, CCM2L, CNN3, CPLANE1, GPAT3, GRIP2, PLAGL2, RTL6, SOX4, WDFY3, and ZNF614. Hematological parameters are heritable and moderately to highly genetically correlated among themselves. The large number of candidate genes identified based on GWAS and RNA-seq indicate the polygenic nature and complex genetic determinism of hematological parameters in Holstein cattle.

Heritability estimates and genome-wide association study of methane emission traits in Nellore cattle.

The objectives of the present study were to estimate the heritability for daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to perform genome-wide association studies (GWAS) to identify genomic regions and candidate genes influencing the genetic variation of CH4 and CH4res. Methane emission phenotypes of 743 Nellore animals belonging to 3 breeding programs were evaluated. CH4 was measured using the sulfur hexafluoride (SF6) tracer technique (which involves an SF6 permeation tube introduced into the rumen, and an appropriate apparatus on each animal), and CH4res was obtained as the difference between observed CH4 and CH4 adjusted for dry matter intake. A total of 6,252 genotyped individuals were used for genomic analyses. Data were analyzed with a univariate animal model by the single-step GBLUP method using the average information restricted maximum likelihood (AIREML) algorithm. The effects of single nucleotide polymorphisms (SNPs) were obtained using a single-step GWAS approach. Candidate genes were identified based on genomic windows associated with quantitative trait loci (QTLs) related to the 2 traits. Annotation of QTLs and identification of candidate genes were based on the initial and final coordinates of each genomic window considering the bovine genome ARS-UCD1.2 assembly. Heritability estimates were of moderate to high magnitude, being 0.42 ± 0.09 for CH4 and 0.21 ± 0.09 for CH4res, indicating that these traits will respond rapidly to genetic selection. GWAS revealed 11 and 15 SNPs that were significantly associated (P < 10-6) with genetic variation of CH4 and CH4res, respectively. QTLs associated with feed efficiency, residual feed intake, body weight, and height overlapped with significant markers for the traits evaluated. Ten candidate genes were present in the regions of significant SNPs; 3 were associated with CH4 and 7 with CH4res. The identified genes are related to different functions such as modulation of the rumen microbiota, fatty acid production, and lipid metabolism. CH4 and CH4res presented sufficient genetic variation and may respond rapidly to selection. Therefore, these traits can be included in animal breeding programs aimed at reducing enteric methane emissions across generations.

Genetic determinism of sensitivity to environmental challenges using daily feed intake records in three lines of pigs.

In pig breeding, environmental challenges can affect the welfare and productivity of animals. Resilient animals have the capacity to be minimally affected by these environmental challenges. Understanding the genetic basis of sensitivity to these environmental challenges is crucial for selecting more resilient animals, thereby enhancing welfare and productivity. The aims of this study were to 1) estimate the probability of the occurrence of an unrecorded environmental challenge on a given day using daily feed intake (DFI) data and 2) evaluate the genetic determinism of environmental sensitivity in three pig lines bred in real selection conditions. Data comprised of 100,799, 186,247, and 304,826 DFI records from 1,618, 2,517, and 3,788 Landrace (LA), Large White (LW), and Piétrain (PI) male pigs, respectively. The pedigree included 3,730, 5,649, and 9,293 animals for LA, LW, and PI, respectively. The probabilities of the occurrence of an unrecorded environmental challenge on a given day were estimated via a mixture model. The probabilities (p) of being "high coefficient of variation days" were then taken as reference and used in genetic analysis as an environmental descriptor to describe the environment. DFI records were analyzed using two linear models: a linear reaction norm animal model (RNAM) and the animal model. (Co)variance components were estimated using average-information restricted maximum likelihood (AI-REML). The means of the probabilities of the occurrence of an environmental challenge for LA, LW, and PI were 0.24, 0.10, and 0.22, respectively, indicating that the probability of an environmental challenge was low for most of the days. The genetic correlations between the intercept and the slope obtained from the RNAM for LA, LW, and PI were -0.52, 0.06, and -0.36, respectively. These findings suggest that selecting hypothetically for decreased DFI in nonstressful conditions would result in pigs with increased DFI in stressful conditions in the LA and PI lines, whereas it would have a minor impact on the environmental sensitivity of LW. The proportion of resilient animals for LA, LW, and PI was 75.0, 74.2, and 72.2%, respectively, implying that most of the animals were resilient. The study demonstrated that the slope of DFI is heritable and can effectively be used as an indicator of sensitivity to environmental challenges. These results are valuable in improving the resilience of livestock species to environmental challenges through genetic selection.

Estimation of genetic parameters for growth traits and kleiber ratio in dorper sheep breed

The study aimed to estimate genetic and phenotypic parameters for growth traits and Kleiber ratio in Dorper sheep breed. Data used in this study were collected over 12 years (2012–2023) at Debre Birhan Agricultural Research Center sheep research station in Ethiopia. Studied traits were body weights at birth (WT0), weaning (WT3), six month (WT6), and yearling (WT12) age; average daily gains from birth to weaning (ADG0-3), from weaning to six months (ADG3-6), from six months to yearling (ADG6-12); and Kleiber ratios from birth to weaning (KR1) and from weaning to six months (KR2). The (co)variance components were estimated with different animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure. The best-fitted model for each trait was determined using likelihood ratio tests. Phenotypic performance for WT3, WT6, WT12, ADG0-3 and ADG3-6 showed a decline trend at a rate of 0.216 kg, 0.794 kg, 0.671 kg, 2.601 g and 4.865 g over years respectively. However, WT3, WT6, WT12, ADG0-3 showed a positive genetic improvement trend at a rate of 0.029 kg, 0.043 kg, 0.049 kg and 0.257 g over years respectively. Year of birth had a significant effect (P < 0.001) on all studied traits. Model including direct genetic as well as permanent environmental effect (Model 2) was chosen as the most appropriate model for WT0. Model which included only direct genetic effect (Model 1) was the best-fit model for all other studied traits. Direct heritability estimates based on suitable models were 0.07 ± 0.06, 0.11 ± 0.06, 0.09 ± 0.07, 0.11 ± 0.09 0.11 ± 0.06, 0.00 ± 0.04, 0.15 ± 0.07 and 0.00 ± 0.04 for WT0, WT3, WT6, WT12, ADG0-3, ADG3-6, KR1 and KR2 respectively. The variance ratio for the permanent environmental effect was 0.13 ± 0.04 for WT0. Genetic correlations among the traits ranged from negative (−0.39) for WT0-KR1 to high (0.99) for WT3-ADG0-3 and phenotypic correlations ranged from negative (−0.31) for WT0-KR1 to high (0.98) for WT3-ADG0-3. The low direct heritability estimates for the studied traits indicated that genetic improvement by direct selection might be difficult. Further investigation for the unexpected declined trend of phenotypic performance over years need to be required.

Estimation of (Co) Variance Components and Genetic Parameters for Pre- and Post-Weaning Growth Traits in Dağlıç Sheep.

The goal of this study was to estimate (co) variance components and genetic parameters for pre- and post-weaning growth traits in Dağlıç sheep, considering the direct additive genetic, maternal genetic, and maternal permanent environmental effects, with different statistical models. The information of 21,735 native Dağlıç lambs born between 2011 and 2021 was used to estimate (co) variance components by the Average Information-Restricted Maximum Likelihood algorithm. The results showed that the most suitable model was Model 3 for birth weight (BW), average daily gain (ADG), and weaning weight (WW). Model 4 was the most appropriate for weight at three (W3), weight at six (W6), and weight at twelve months of age (W12). The direct heritabilities for BW, W3, ADG, WW, W6, and W12 were 0.35 ± 0.02, 0.36 ± 0.03, 0.27 ± 0.02, 0.22 ± 0.02, 0.47 ± 0.05, and 0.47 ± 0.05, respectively. Genetic and phenotypic correlations amongst the traits were in the range of 0.103 ± 0.008 to 0.995 ± 0.002. These results can be used for the improvement of growth traits in the Dağlıç breed of sheep through selection.

Genetic and Phenotypic Trend and Inbreeding Effects for Sexual and Egg Production Traits of Iraqi Local Chickens

A study was conducted to estimate genetic and phenotypic trend and inbreeding effect on a sexual and egg production trait in Iraqi brown local chickens. Data on 2234 hens raised in poultry research stations from successive 6 generations of selection for high egg production during the first 24 weeks of production were used. Data of age at sexual maturity (ASM), body weight at sexual maturity (BWSM), first egg weight (FEWT), egg weight (EW), egg number (EN), and egg mass (EM) were recorded individually. The univariate animal models under Average Information Restricted Maximum Likelihood (AI-REML) of Wombat package were applied to estimate the heritability, BLUP-breeding value and inbreeding rate. The heritability estimates based on individual animal model was 0.39±0.05, 0.42±0.05, 0.17±0.04, 0.68±0.04, 0.40±0.05 and 0.40±0.05 for the traits mentioned above respectively. Average breeding values across generation tend to be positive in productive traits but it was negative in ASM, BWSM and FEW. The overall predicted genetic gains, after 6 generations of selection, estimated by the regression coefficients of the breeding value on generation number were equal to -0.010, 0.029, 0.033, -0.397, -1.514, and -41.541, for ASM, BWSM, FEWT, EW, EN and EM, respectively. In conclusion, the moderate to high heritability estimated for egg production traits suggested that these traits could be improved through selection. The effect of the inbreeding rate negatively on some traits and positively on other traits suggested that both traits could not be improved simultaneously using additive selection. The inconsistency between phenotypic and genetic trends suggests that genetic progress was not achieved in a higher magnitude.

Optimizing selection strategy for enhancing reproduction efficiency in Indian crossbred goats using milk productivity as a selection criterion.

The present study was aimed at optimizing the selection strategy for enhancing reproductive efficiency and milk productivity of Alpine × Beetal crossbred goats. The data set included 2949 milk trait records across parities and 1389 milk records from first parity and corresponding reproductive traits. The traits included for analysis were 150-day milk yield (150DMY), days in milk (DIM), peak yield (PY) and total milk yield (TMY). The litter size (LS) and litter weight (LW) were used for specifically formulating selection plan using indirect selection. The least squares mean for lactation traits during the first parity were 150DMY: 195.32 ± 2.09 kg, DIM: 236.42 ± 3.04 days, PY: 1.82 ± 0.02 kg, TMY: 269.62 ± 4.52 kg. Notably, Alpine × Beetal goats demonstrated genetic superiority pan India for milk productivity as compared to other native goat breeds. The least squares mean for 150DMY across all parities was 236 ± 3.13 kg. An animal model employing average information restricted maximum likelihood was used for (co)variance component estimation to get the genetic parameters. The analysis revealed total heritability estimates for 150DMY, DIM, PY and TMY as 0.18 ± 0.06, 0.04 ± 0.04, 0.12 ± 0.06 and 0.08 ± 0.05, respectively. Repeatability estimates for 150DMY, DIM, and TMY were 0.28 ± 0.04, 0.21 ± 0.03 and 0.37 ± 0.03, respectively. Bivariate analysis of 150DMY with reproductive traits revealed heritability for LS and LW as 0.05 ± 0.01 and 0.10 ± 0.01, respectively using Gibbs sampling. Strong and positive genetic correlations of 150DMY with other production and reproduction traits was observed, such as DIM (0.72), PY (0.98), TMY (0.88), LS (0.57) and LW (0.33). Moderate heritability and repeatability estimate of 150DMY, along with its positive correlation with production and reproductive traits suggested it as a suitable selection criterion for early selection and overall genetic progress of lactation traits. The genetic trend analysis showed an overall improvement in all these traits, with observed gain of 98.4 g per year for 150DMY, 0.04 days per year for DIM, 0.5 g per year for PY and 220.5 g per year for TMY. We observed that selecting based on 150DMY would lead to a favourable indirect improvement for LW as 79 g and LS 0.04 units per generation. We, therefore, recommend employing 150DMY as the single trait selection criteria to enhance both milk productivity and reproductive potential of Alpine × Beetal goats.

GENETIC PARAMETERS AND CORRELATIONS OF EARLY WEIGHT TRAITS IN A NIGERIAN INDIGENOUS HEAVY X LIGHT ECOTYPES POPULATION OF CHICKENS

&lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt;: Improvement of the Nigerian indigenous chickens is desirable because of its socioeconomic important. &lt;strong&gt;Objective&lt;/strong&gt;: To estimate the heritability, genetic and phenotypic correlations among body weight, body length, wing length and shank length of the mating of heavy ecotype cocks with light ecotype hens of Nigerian indigenous chickens. &lt;strong&gt;Methodology&lt;/strong&gt;: The mating design was such that one heavy ecotype cock mated with 10 light ecotype hens in twenty replicate pens. The mating produced 752 chicks from ten batches of hatch. The estimates of variance components were obtained from paternal half sib analysis using the average information restricted maximum likelihood of BLUPF90 family of programs. &lt;strong&gt;Results&lt;/strong&gt;: The heritability of body weight ranged from 0.20 to 0.44 from 6 to 12 weeks of age. Similarly, the heritability of body length, wing length and shank length ranged from 0.39 to 0.63, 0.51 to 0.71 and 0.31 to 0.54 respectively from 6 to 12 weeks of age. The genetic correlations among the early growth traits measured were negative to positive in magnitude and ranged from -0.99 to 0.66 while the phenotypic correlations ranged from -0.15 to 0.91. &lt;strong&gt;Implication&lt;/strong&gt;: The results imply that early growth traits of light and heavy ecotype Nigerian indigenous chicken could be improved through mass selection. &lt;strong&gt;Conclusion&lt;/strong&gt;: It is concluded from this study that the heritability of early growth traits were moderate to high in magnitude and the genetic and phenotypic correlations mostly positive.&lt;/p&gt;

Growth rate and efficiency-related traits in Kermani lambs: estimations of (co)variance components and genetic parameters.

Using Kermani sheep, the current study estimated (co)variance components and genetic parameters for average daily gain, Kleiber's ratio, growth efficiency, and relative growth rate. Data were analyzed by the average information restricted maximum likelihood (AI-REML) method using six animal models with different combinations of direct and maternal effects. The best-fitting model was determined after testing for improvement in log-likelihood values. The estimates of h2 for average daily gain (ADG), Klieber's ratio (KR), growth efficiency (GE), and relative growth rate (RGR) in pre- and post-weaning phases were 0.13 ± 0.6 and 0.17 ± 0.02, 0.12 ± 0.04, and 0.16 ± 0.03; 0.05 ± 0.05 and 0.07 ± 0.03 and 0.06 ± 0.02 and 0.07 ± 0.01, respectively. Maternal heritabilities (m2) ranged from 0.03 ± 0.01 for relative growth rate in pre-weaning phase to 0.11 ± 0.04 for average daily gain in post-weaning period. The maternal permanent environmental component (Pe2) accounted for 3 to 13% to the phenotypic variance for all the studied traits. Estimated values of additive coefficient of variations (CVA) ranged from 2.79% for relative growth rate at 6 months of age to 23.74% for growth efficiency at yearling age. Genetic and phenotypic correlations among traits were ranged from -0.687 to 0.946 and -0.648 to 0.918, respectively. The result indicated that selection for growth rate and efficiency-related traits would also be less effective in achieving genetic change, because there was little additive genetic variation among Kermani lambs.