Multi-trait Animal Model Research Articles

Multi-trait Bayesian analysis and genetic parameter estimates in production characters of Mecheri sheep of India

The present study was carried out to estimate the genetic parameters for direct and maternal influences on Mecheri sheep (Ovis aries) growth traits using Bayesian multi-trait animal model. The genetic parameters were calculated using data from 2825 Mecheri lambs born between 2010 and 2020 that were kept in semi-arid tropical climate. Mecheri sheep body weight (mean ± SE) at various stages, viz. BW, WW, and BW12, were 2.6 ± 0.01, 11.1 ± 0.05, and 20.7 ± 0.13kg, respectively. The Mecheri sheep gained 71.5 percent of their body weight at the age of 6months. With the exception of birth weight, the weights of the animals varied considerably (P < 0.01) by the year of birth. The fixed effect of sex significantly (P < 0.01) influenced all the growth traits examined. The direct estimates of heritability (± SD) for BW, WW, BW6, and BW12 was 0.21 ± 0.041, 0.21 ± 0.041, 0.12 ± 0.052, and 0.13 ± 0.053, respectively, and the maternal heritability for BW, WW, BW6, and BW12 was 0.18 ± 0.021, 0.08 ± 0.023, 0.11 ± 0.022, and 0.13 ± 0.033, respectively. Significant variance was indicated by moderately larger direct heritability estimates for BWT and WWT, indicating that there will be more opportunities for selection response during the genetic improvement programme. For the majority of the variables examined, direct heritability values were higher than maternal heritability values. The additive genetic correlation between WW and BW6, BW9, and BW12 was 0.70 ± 0.145, 0.57 ± 0.171, and 0.50 ± 0.194, respectively. The maternal genetic correlations ranged from 0.06 ± 0.152 (BW-BW12) to 0.86 ± 0.046 (BW6-BW9), and the residual correlation varied from 0.18 ± 0.034 (BW-WW) to 0.85 ± 0.013 (BW9-BW12). The BW had a stronger genetic relationship with WW and a weaker relationship with other growth traits. The WW and BW6 showed a favourable genetic relationship, even if the tendency was decreasing in the latter stages of maturation. The BW6 and BW9 demonstrated the strongest positive genetic relationship (0.90 ± 0.052) of all the variables analysed. After examining the heritabilities and genetic correlation between WW and BW6, it was established that selection based on at WW was beneficial, since it manifests early in life and would result in moderate genetic progress through selection.

Effects of Imported Semen Based on Different Selection Indices on Some Production and Reproduction Traits in Iranian Holstein Cattle.

The aim of the present study was to evaluate the effects of imported semen of Holstein bulls from different countries on the economic traits of their daughters using the Lifetime Net Income (LNI) index in various climates of Iran. The data included the first lactation records of 274,057 Holstein cows collected during 1993 to 2017 by the Animal Breeding Center of Iran from 10 large dairy farms located in various provinces of Iran. The investigated traits included milk, fat and protein yields, calving age and calving interval. Breeding values of progenies were predicted by the Best Linear Unbiased Prediction (BLUP) method under the multi-trait animal model using DMU software. The genetic-economic merit of the progenies was estimated by the LNI index. There were significant differences between the estimated breeding values (EBVs) of sire groups (based on bull semen origin) for milk, fat and protein yields, calving age and calving interval in each climate (p &lt; 0.01). The obtained results showed that the highest least-square means of LNI index in semi-cold, moderate and warm climates belonged to the daughters of French sires; however, daughters of German sires were estimated to have the highest least-square means in the cold climate.

Genetic parameter analysis of reproductive traits in Large White pigs.

The primary objective of this study was to determine the genetic parameters for reproductive traits among Large White pigs, including the following traits: total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), gestation length (GL), age at first service (AFS) and age at first farrowing (AFF). The dataset consisted of 19,036 reproductive records from 4,986 sows, and a multi-trait animal model was used to estimate genetic variance components of seven reproductive traits. The heritability estimates for these reproductive traits ranged from 0.09 to 0.26, with the highest heritability for GL and AFF, and the lowest heritability for NBA. The repeatabilities for TNB, NBA, LWB, ABW, and GL were ranged from 0.16 to 0.34. Genetic and phenotypic correlations ranged from -0.41 to 0.99, and -0.34 to 0.98, respectively. In particular, the correlations between TNB, NBA and LBW, between AFS and AFF, exhibited a strong positive correlation. Furthermore, for TNB, NBA, LBW, ABW, and GL, genetic correlations of the same trait between different parities were moderately to strongly correlated (0.32 to 0.97), and the correlations of adjacent parities were higher than those of nonadjacent parities. All the results in the present study can be used as a basis for the genetic assessment of the target population. In the formulation of dam line selection index, AFS or AFF can be considered to combine with TNB in a multiple trait swine breeding value estimation system. Moreover, breeders are encouraged to increase the proportion of sows at parity 3-5 and reinforce the management of sows at parity 1 and parity ≥8.

39 Genetic Associations between Feeding Behavior and Economic Interest Traits in Group-Housed Broilers

Abstract This study aimed to assess the genetic relationship among feeding behavior (FB), feed efficiency (FE), and production traits in broilers. A total of 44,234,350 feeders visit logs from 55,400 birds allocated into 88 trials were recorded by radio-frequency identification system. The daily data were averaged per animal for FB traits, considering the entire trial period. FB traits included the number of meals (NMEAL, count/day), time spent at feeders (TSF, hours/day), average portion size (PS, g), feeding rate (FR, g/hour), feeding activity range (FAR, hours/day), number of visited stations (NVS, count/day), and number of visited feeders (NVF, count/day). The total feed intake (FI), residual feed intake (RFI), and feed conversion rate (FCR) were also considered. Further, the body weight was measured at the start (SBW) and ending (FBW) of the trial. Multi-trait animal models were fitted for estimating the (co)variance components, considering the effects of sex and contemporary group as fixed. FB traits presented moderate to high heritability (h2) values, ranging from 0.26±0.02 (FAR) to 0.58±0.02 (TSF), whereas for FE and growth traits the h2 ranged from 0.16±0.01 (FBW) to 0.27±0.02 (RFI). The traits NMEAL, NVS, NVF, and FAR presented negative genetic correlations with FI, SBW, and FBW (-0.36 to -0.13) while PS and FR were positively correlated with all FE and production traits (0.12 to 0.43). RFI only presented non-null genetic correlations with PS (0.26±0.04), FR (0.23±0.03), and NVS (-0.11±0.04). Similarly, NMEAL and NVS also presented a weak negative correlation with FCR (-0.12±0.05 and -0.17±0.05, respectively). FB traits have a sizable genetic component and, therefore, may present a fast response to selection. The results also suggest that future selection for welfare-related feeding behavior (e.g., NMEAL, NVS, and NVF) would not affect negatively FE traits, although it might result in lighter animal lines in the long term.

Consequences of Selection for Post-weaning Growth Performance Traits on Fat Partition Traits in Rabbits

Genetic and phenotypic parameters, using multi-trait animal model, for weaning weight (WW), slaughter weight (SW) and daily gain (DG) from weaning to slaughter were applied on 218 New Zealand White rabbits to improve post-weaning growth traits using selection indices. The specific objective was to improve profitability of rabbit producers through increasing body weight at slaughter and daily gain from weaning to slaughter. Expected impacts of selection for post-weaning growth traits on fat partition traits were also investigated. The fat partition traits included weights of subcutaneous fat (SF) and intermuscular fat (IF) as carcass components and heart fat (HF), kidneys fat (KF), mesenteric fat (MF) and caul fat (CF) as offal components. Growth traits showed low to moderate heritability estimates (h2,0.03 to 0.37), while fat partition traits had higher h2 estimates (0.72 to 0.93), except for SF (h2=0.27). Application of the reduced index considering WW and DG was as efficient as full index in terms of accuracy of selection (rTI=0.66); it provided higher improvement in aggregate genotype represented in SW (+170.72gm) and DG (+2.41gm/day) for each round of selection. This improvement in post-weaning traits was accompanied by unfavorable expected impact in fat traits SF, HF, MF and CF (+45.32, +12.77, +56.50 and +12.25%, respectively) and favorable expected impact on IF and KF (-32.16 and -59.47gm, respectively). The single trait selection index (I6) with SW was found to be useful for rabbit meat consumers, as it decreased the undesirable fat gain in fat depots compared to the other indices, with some reduction in accuracy. In conclusion, it is possible to improve the post-weaning growth traits through selection programs, taking into consideration the undesirable changes in fat partition traits of New Zealand White rabbits.

Performance of the No-U-Turn sampler in multi-trait variance component estimation using genomic data

BackgroundMulti-trait genetic parameter estimation is an important topic for target traits with few records and with a low heritability and when the genetic correlation between target and secondary traits is strong. However, estimating correlations between multiple traits is difficult for both Bayesian and non-Bayesian inferences. We extended a Hamiltonian Monte Carlo approach using the No-U-Turn Sampler (NUTS) to a multi-trait animal model and investigated the performance of estimating (co)variance components and breeding values, compared to those for restricted maximum likelihood and Gibbs sampling with a population size of 2314 and 578 in a simulated and real pig dataset, respectively. For real data, we used publicly available data for three traits from the Pig Improvement Company (PIC). For simulation data, we generated two quantitative traits by using the genotypes of the PIC data. For NUTS, two prior distributions were adopted: Lewandowski-Kurowicka-Joe (LKJ) and inverse-Wishart distributions.ResultsFor the two simulated traits with heritabilities of 0.1 and 0.5, most estimates of the genetic and residual variances for NUTS with the LKJ prior were closer to the true values and had smaller root mean square errors and smaller mean absolute errors, compared to NUTS with inverse-Wishart priors, Gibbs sampling and restricted maximum likelihood. The accuracies of estimated breeding values for lowly heritable traits for NUTS with LKJ and inverse-Wishart priors were 14.8% and 11.1% higher than those for Gibbs sampling and restricted maximum likelihood, respectively, with a population size of 578. For the trivariate animal model with real pig data, the estimates of the genetic correlations for Gibbs sampling and restricted maximum likelihood were strongly affected by population size, compared to NUTS. For both the simulated and pig data, the genetic variances and heritabilities for NUTS with an inverse-Wishart prior were overestimated for low-heritability traits when the population size was 578.ConclusionsThe accuracies of variance components and breeding values estimates for a multi-trait animal model using NUTS with the LKJ prior were equal to or higher than those obtained with restricted maximum likelihood or Gibbs sampling. Therefore, when the population size is small, NUTS with an LKJ prior could be an alternative sampling method for multi-trait analysis in animal breeding.

Comparative Study of Phenotypes and Genetics Related to the Growth Performance of Crossbred Thai Indigenous (KKU1 vs. KKU2) Chickens under Hot and Humid Conditions.

To improve the body weight and growth performance traits of crossbred Thai indigenous chickens, phenotypic performance and genetic values were estimated. Crossbred Thai indigenous chickens, designated KKU1 and KKU2, were compared. The data included 1375 records of body weight (BW0, BW2, BW4, and BW16), breast circumference at 6 weeks of age (BrC6), and average daily gain (ADG0–2, ADG0–4, and ADG0–6). A multi-trait animal model with the average information-restricted maximum likelihood (AI-REML) was used to estimate the genetic parameters and breeding values. The results showed that the body weight and breast circumference traits (BW2, BW4, BW6, and BrC6) for the mixed sex KKU1 chickens were higher than for the KKU2 chickens (p < 0.05). For the growth performance traits, the KKU1 chickens had higher average daily gain and feed intake and a lower feed conversion ratio than the KKU2 chickens (p < 0.05). The survival rates were not different except at up to 6 weeks of age, when that of the KKU1 chickens was slightly lower. The specific combining ability, heritability, genetic and phenotypic correlations, and estimated breeding values showed that the KKU1 chickens had better genetics than the KKU2 chickens. In conclusion, KKU1 chickens are suitable for development as crossbred Thai indigenous chickens for enhanced growth performance and for commercial use.

Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (αS1-CN, αS2-CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the β-CN, κ-CN, and β-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the β-LG B variant was associated with a lower concentration of β-LG compared with the β-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the β-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health.

Genomic evaluation of binary traits in dairy cattle by considering genotype × environment interactions

Aim of study: To assess genotype by environment (G×E) interaction via single- and multi-trait animal models for binary traits in dairy cattle. Area of study: University of Tabriz, Tabriz, Iran. Material and methods: Phenotypic and genomic data were simulated considering a binary trait in four environments as different correlated traits. Heritabilities of 0.05, 0.10, 0.15, and 0.20 were considered to mimic the genetic variation of the binary trait in different environments. Eight scenarios resulted from combining the number of QTLs (60 or 300), LD level (high or low), and incidence of the binary trait (10% or 30%) were simulated to compare the accuracy of predictions. For all scenarios, 1667 markers per chromosome (depicting a 50K SNP chip) were randomly spaced over 30 chromosomes. Multi-trait animal models were applied to take account of G×E interaction and to predict the genomic breeding value in different environments. Prediction accuracies obtained from the single- and multi-trait animal models were compared. Main results: In the models with G×E interaction, the largest accuracy of 0.401 was obtained in high LD scenario with 60 QTLs, and incidence of 30% for the fourth environment. The lowest accuracy of 0.190 was achieved in low LD scenario with 300 QTLs and incidence of 10% for the first environment. Research highlights: Genomic selection with high prediction accuracy can be possible by considering the G×E interaction during the genetic improvement programs in dairy cattle.

Comparison of a single-step with a multistep single nucleotide polymorphism best linear unbiased predictor model for genomic evaluation of conformation traits in German Holsteins

Genomic evaluation based on a single-step model uses all available data of phenotype, genotype, and pedigree; therefore, it should provide unbiased genomic breeding values with a higher correlation of prediction than the current multistep genomic model. Since 2019, a mixed reference population of cows and bulls has been applied to the routine multistep genomic evaluation in German Holsteins. For a fair comparison between the single-step and multistep genomic models, the same phenotype, genotype, and pedigree data were used. Because of its simple structure of the standard multitrait animal model used for German Holstein conventional evaluation, conformation traits were chosen as the first trait group to test a single-step SNP BLUP model for the large, genotyped population of German Holsteins. Genotype, phenotype, and pedigree data were taken from the official August 2020 conventional and genomic evaluation. Because of the same trait definition in national and multiple across-country evaluation for the conformation traits, deregressed multiple across-country evaluation estimated breeding value (EBV) of foreign bulls were treated as a new source of data for the same trait in the genomic evaluations. Due to a short history of female genotyping in Germany, the last 3 yr of youngest cows and bulls were deleted, instead of 4 yr, to perform a genomic validation. In comparison to the multistep genomic model, the single-step SNP BLUP model resulted in a higher correlation and greater variance of genomic EBV according to 798 national validation bulls. The regression of genomic prediction of the current, full evaluation on the earlier, truncated evaluation was slightly closer to 1 than the multistep model. For the validation bulls or youngest genomic artificial insemination bulls, correlation of genomic EBV between the 2 models was, on average, 0.95 across all the conformation traits. We did not find overprediction of young animals by the single-step SNP BLUP model for the conformation traits in German Holsteins.

Genetic Evaluation of Body Weights and Egg Production Traits Using a Multi-Trait Animal Model and Selection Index in Thai Native Synthetic Chickens (Kaimook e-san2).

Simple SummaryNative chickens play a vital role in the rural economies of many countries and are considered a valuable genetic resource for use in the development of high-yielding breeds. Native breeds are used as foundation stocks for breeding through a system of crossbreeding with commercial breeds, taking advantage of heterosis. However, due to the constraints of their slow growth rate and low egg production, there is insufficient supply to meet consumer demand. Therefore, improving genetic knowledge is one way to solve these problems and achieve sustainable results. In this paper, a multi-trait animal model and a selection index are developed as a solution for this problem to improve growth and egg performance.To improve the genetics of both growth and egg production, which are limitations in purebred native chickens, new genetic lines can be developed using an appropriate genetic approach. The data used in this study included 2713 body weight (BW0, BW4, BW6, BW8, and BW10), breast circumference (BrC6), chicken age at first egg (AFE), and egg production (240EP, 270EP, 300EP, and 365EP) records covering the period 2015 to 2020. A multi-trait animal model with the average information-restricted maximum likelihood (AI-REML) and a selection index was used to estimate the variance components, genetic parameters, and breeding values. The results showed that males had significantly higher weights than females (p < 0.05) from 4 to 10 weeks of age and that this difference increased over the generations. The differences between BW0 and BrC6 by sex and generation were not significant (p > 0.05). The estimated heritability of body weight ranged from 0.642 (BW0) to 0.280 (BW10); meanwhile, the estimated heritability of BrC6 was moderate (0.284). For egg production traits, the estimated heritability of 240EP, 270EP, 300EP, and 365EP was 0.427, 0.403, 0.404, and 0.426, respectively, while the estimated heritability of AFE was 0.269. The genetic and phenotypic correlations among the growth traits (BW0 to BW10) were low to highly positive. The genetic and phenotypic correlations between growth (BW0 to BW10) and BrC6 traits were positive, and the genetic correlations between BW6 (0.80), BW8 (0.84), BW10 (0.93), and BrC6 were strongly positive. Genetic correlations among the egg production traits (240EP, 270EP, 300EP, and 365EP) were low to highly positive and ranged from 0.04 to 0.86. The genetic correlations between AFE and all egg production traits were low to moderately negative and ranged from −0.14 to −0.29. The positive genetic correlations between body weight (BW6, BW8, and BW10) and egg production traits were found only in 240EP. The average genetic progress of body weight traits ranged from −0.38 to 30.12 g per generation for BW0 to BW10 (p < 0.05); the genetic progress was 0.28 cm per generation for BrC6 (p > 0.05). The average genetic progress of cumulative egg production traits ranged from 4.25 to 12.42 eggs per generation for 240EP to 365EP (p < 0.05), while the average genetic progress of AFE was −7.12 days per generation (p < 0.05). In conclusion, our study suggests that the body weight at six weeks of age (BW6), breast circumference at six weeks of age (BrC6), cumulative egg production at 240 days of age (240EP), and age at first egg (AFE) are the traits that should be used as selection criteria, as they have a positive effect on the development of growth and egg production.

Genome-wide association study of beef bull semen attributes

BackgroundCattle production is dependent upon fertility because it results in producing offspring to offset production costs. A number of semen attributes are believed to affect fertility and are frequently measured as part of routine breeding soundness exams or semen collection procedures. The objective of this study was to perform a single-step genome-wide association study (ssGWAS) for beef bull semen attributes. Beef bull fertility phenotypes including volume (VOL), concentration (CONC), number of spermatozoa (NSP), initial motility (IMot), post-thaw motility (PTMot), three-hour post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SEC) were obtained from two artificial insemination (AI) centers. A total of 1819 Angus bulls with 50,624 collection records were used for ssGWAS. A five-generation pedigree was obtained from the American Angus Association and consisted of 6521 sires and 17,136 dams. Genotypes on 1163 bulls were also obtained from the American Angus Association and utilized in ssGWAS.ResultsA multi-trait animal model was used for the estimation of single nucleotide polymorphism (SNP) effects. Significant SNP were those with a -log10P-value threshold greater than 4.0. Volume, CONC, NSP, IMot, PTMot, 3HRPTMot, %NORM, PRIM, and SEC have five, three, six, seven, two, six, six, and two genome-wide significant SNP, respectively.ConclusionsSeveral significant SNP were determined to be near or within quantitative trait loci (QTL) associated with beef bull semen attributes. In addition, genes associated with fertility were found to contain or be near the significant SNP found in the study. The results indicate there are regions of the genome that impact fertility, proving inclusion of genomic information into genetic evaluation should be advantageous for genetic improvement of male fertility traits.

Confirmatory factor analysis and structural equation models to dissect the relationship between gait and morphology in Campolina horses

It is well known that locomotion and morphology are related. Several studies have been performed over the years showing the genetic correlation between them in horses. However, none could reveal the possible causative link between those traits in this species. The aim of this study was to apply statistical causal inference to dissect the possible causative links between morphology and locomotion traits in Campolina horses. It was used 49,994 horses with records for 28 traits. All phenotypic records were adjusted for fixed (contemporary group, stud, and age) and random (technician) effects except the animal genetic effect. All missing observations were removed, and the final dataset was left with 640 adjusted records for all traits. A confirmatory factor analysis (CFA) was conducted to build a measurement model for three latent traits: Morphology (M), Morphological quality (Q) and Locomotion traits (L). Here it was assumed that morphology and locomotion cannot be directly measured, but only morphological and locomotion aspects. A multitrait animal model was performed to obtain breeding values (EBV) for the three latent traits, and factor scores from CFA were adjusted for the EBV. The adjusted factor scores were used as input in structural equation models to investigate all possible causative links among latent traits. The measurement model showed acceptable fit to the data, and the differences between sample and model-implied (co)variance matrices were low (0.01). L had low heritability estimate (0.03) whereas M and Q had moderate (0.21 and 0.36, respectively). All genetic correlations were negative, but those between Q and the others were favorable for selection. There is a causative direct effect of Q over M (-0.30) and an indirect over L (-0.10). It was found a causative effect of M on L (0.35). Knowing causal paths among traits will help breeders to take selection decisions and may have important implications for strategies in horse improvement.

Genetic parameters for production traits in F1 reciprocal crossbred Chee Fah and Fah Luang chickens

Context Recently, Chee Fah and Fah Luang chickens have been registered as a black-bone native chicken in Thailand. Only a few studies revealed genetic information about them. No publication has reported any data related to their cross-mating, particularly, genetic parameters. Aims This study aimed to estimate genetic parameters for production traits of F1 generation of reciprocal crossbred Chee Fah and Fah Luang chickens. Methods A dataset of production traits of two crossbred groups was used in the present study. Effects of breed, month-day of incubation and sex were tested at P &lt; 0.05. Genetic parameters were estimated using the restricted maximum likelihood method with multi-trait animal model. Key results The crossbred Chee Fah × Fah Luang was significantly heavier and consumed more feed than Fah Luang × Chee Fah (P &lt; 0.05). Male chickens had significantly better 20-week-old bodyweight, feed conversion ratio and average daily gain compared with females for both crossbred groups (P &lt; 0.05). The effect of month-day of incubation had a significant influence on production traits (P &lt; 0.05), except for day-old bodyweight. Heritabilities for production traits of crossbred chickens were low to high. The highest estimate was observed for day-old bodyweight (0.97), followed by feed intake (0.40), 20-week-old bodyweight (0.06), average daily gain (0.05) and feed conversion ratio (0.03), respectively. Both positive and negative genetic correlations were found among their production traits. Favourable relationships were found between average daily gain versus bodyweight and versus feed conversion ratio (rgg = 0.99 and −0.90, respectively). Similarly, production traits showed phenotypic correlations in both directions, which ranged from −0.95 to 0.99. Conclusions Heritability estimations for production traits were found in low to high magnitude. The desirable genetic relationships were found between feed conversion ratio and day-old bodyweight, 20-week-old bodyweight and average daily gain, and between 20-week-old bodyweight and average daily gain. Implications These findings could be considered as a source of genetic data for enhancing production traits of crossbred black-bone native chickens.

Phenotypic and genetic parameters of productive traits in Rahmani and Romanov sheep and crossbreds.

Data of 651 lambs (68 Romanov, 49 Rahmani, 151 [♀1/2 Rahmani X ♂1/2 Romanov) and 383 (♀3/4 Rahmani and 1/4♂ Romanov]) were collected from Mehalet Mousa Farm, belonging to Animal Production Research Institute from the period of 2009 to 2016 to estimate phenotypic and genetic parameters. The traits studied were birth weight (BW), body weight at four week (BW4), body weight at eight weeks (BW8) and body weight at twelve weeks (BW12) or weaning weight. Least squares analysis of variance shows significance of the effects of breed groups, gender of lambs, birth type; month of birth and year of birth on all traits studied. Rahmani lambs had heavier BW, BW4, BW8 and BW12 while Romanov lambs had the lowest ones. The first generation (♀1/2 Rhamani X ♂1/2 Romanov) had heavier body weights than Romanov and the second generation (♀3/4 Rahmani X ♂1/4 Roamnov). Gender of lambs had highly significant effect on body weights. Males were significantly (p < 0.01) heavier than females for all traits studied. Least square means of BW, BW4, BW8 and BW12 for single lambs were 2.69, 10.43, 13.53 and 16.10 kg, respectively. Least square means of BW, BW4, BW8 and BW12 for twin lambs were 2.50, 9.37, 12.5 and 15.16 kg, respectively, while least square means of BW, BW4, BW8 and BW12 for triple lambs were 2.09, 7.86, 10.83 and 13.67 kg, respectively. Estimates of direct heritability measured by single trait animal model were 0.14, 0.23, 0.25 and 0.26 for BW, BW4, BW8 and BW12, respectively, and the corresponding measured by multi trait animal model were 0.17, 0.24, 0.32 and 0.36 for the same traits, respectively. All genetic and phenotypic correlations among different traits studied are positive and significant.

The impact of direct-maternal genetic correlations on international beef cattle evaluations for Limousin weaning weight.

In beef cattle maternally influenced traits, estimates of direct-maternal genetic correlations (rdm) are usually reported to be negative. In international evaluations, rdm can differ both within countries (rdm_WC) and between countries (rdm_BC). The rdm_BC are difficult to estimate and are assumed to be zero in the current model for international beef cattle evaluations (Interbeef). Our objective was to investigate re-ranking of international estimated breeding values (IEBVs) in international beef cattle evaluations between models that either used estimated values for rdm or assumed them to be 0. Age-adjusted weaning weights and pedigree data were available for Limousin beef cattle from ten European countries. International EBVs were obtained using a multi-trait animal model with countries modeled as different traits. We compared IEBVs from a model that uses estimated rdm_BC (ranging between −0.14 and +0.14) and rdm_WC (between −0.33 and +0.40) with IEBVs obtained either from the current model that assumes rdm_BC to be 0, or from an alternative model that assumes both rdm_BC and rdm_WC to be 0. Direct and maternal IEBVs were compared across those three scenarios for different groups of animals. The ratio of population accuracies from the linear regression method was used to further investigate the impact of rdm on international evaluations, for both the whole set of animals in the evaluation and the domestic ones. Ignoring rdm_BC, i.e., replacing estimated values with 0, resulted in no (rank correlations > 0.99) or limited (between 0.98 and 0.99) re-ranking for direct and maternal IEBVs, respectively. Both rdm_BC and rdm_WC had less impact on direct IEBVs than on maternal IEBVs. Re-ranking of maternal IEBVs decreased with increasing reliability. Ignoring rdm_BC resulted in no re-ranking for sires with IEBVs that might be exchanged across countries and limited re-ranking for the top 100 sires. Using estimated rdm_BC values instead of considering them to be 0 resulted in null to limited increases in population accuracy. Ignoring both rdm_BC and rdm_WC resulted in considerable re-ranking of animals’ IEBVs in all groups of animals evaluated. This study showed the limited impact of the current practice of ignoring rdm_BC in international evaluations for Limousin weaning weight, most likely because the estimated rdm_BC was close to 0. We expect that these conclusions can be extended to other traits that have reported rdm values in the range of rdm_WC values for weaning weight in Limousin.

Genetic parameters for body weight and morphometric traits in Santa Ines sheep using Bayesian inference

The objective of this study was to estimate genetic parameters between body weight and morphometric traits to choose selection criteria to improve genetically the body weight and body conformation using Bayesian inference. Body weight, body length, chest girth, croup height, croup width, withers height, and body condition score of 645 Santa Ines meat sheep were used to estimate the genetic parameters in a multi-trait animal model with Bayesian approach. The heritability estimates ranged from 0.19 ± 0.05 to 0.32 ± 0.07 (croup width and body condition score respectively), which allow them to respond to genetic selection. The genetic correlations between body weight and morphometric traits were positive and ranged from 0.18 ± 0.17 to 0.98 ± 0.02. The genetic correlation between body weight and chest girth (0.98 ± 0.02) indicates that selection based on one trait will result in genetic gain for the other. The body weight is the main selection criterion to increase the meat production via genetic selection of the animals due to its moderate heritability and moderate to high genetic correlation with the other traits studied. However, if calibrated electronic scales are not available for the sheep producers, they can use the morphometric measurements to carry out the selection of animals to result in indirect genetic gains for body weight. If the breeders want to choose only one morphometric trait to indirectly improve the genetic body weight, chest girth is recommended due to its extremely high genetic correlation with body weight. The next step would be to develop an index that takes into account all the morphometric measurements to use it as indirect indicator of body weight.

Restricted Selection Index to Relieve the Negative Repercussion in Reproductive Performance of Holstein Cows

The aim of this investigation was to develop restricted selection index aiming to improve 305-day yields of milk (MY), fat (FY) and protein (PY), while keeping the deterioration in days open (DO), calving interval (CI) and number of services per conception (NSPC) at minimum levels in Holstein cows. The data represent 3682 records of 1122 cows, daughters of 95 sires and 712 dams. The data were analyzed by multi-trait animal model with repeated measures. Eight selection indexes (five unrestricted and three restricted) were derived using MY, FY, PY, CI, DO and NSPC in various combinations as sources of information in the indexes. However, the true breeding value included MY, FY and PY. The highest accuracy of selection (0.60) resulted from selection based on the full index. Milk yield and SPC appeared to be the most valuable traits in the full index. Combining the two traits into one index (the best reduced index) gave 0.57 accuracy of selection. The index based on MY alone (the most accurate single trait index) gave 0.53 accuracy. It seems possible to reduce the expected genetic deterioration in the reproductive traits by restricting the full index to result in zero genetic change in NSPC (rTI=0.48). This restriction will allow the breeder to mitigate the deterioration in DO and CI by 12 and 16 days respectively, by sacrificing with part of the expected genetic improvement in productive traits (29, 40 and 48% in MY, FY and PY, respectively).

THE GENETIC AND NON-GENETIC AFFECT ON CARCASS CHARACTERISTICS OF NEW ZEALAND WHITE AND BALADI BLACK RABBITS

Data collected on 120random male at 12 weeks of age, from New Zealand white and Baladi Black rabbit. Data were analyzed applying multi-trait animal model ofcarcass characteristicsto obtain proportion of the phenotypic variance due to additive genetic effects (h2);common litter effects (c2); random error effects (e2); genetic (rG), common litter (rC), environmental (rE) and phenotypic (rP) correlations.Datawere carried out by weighed least-squares means method in the procedure GLM of statistical software SAS(2003) to obtain least squares of effects for breed, parity and season and significant. Heritability estimateswere mostly moderate 0.20, 0.12 and 0.37 for live weight at slaughter, weight after bleedingand weight with viscera and without head,were moderate and high and ranged from 0.29 to 54 for edible parts, were moderate and ranged from 0.30 to 0.35 for dressing yield, alsowere low and moderate and ranged from 0.03 to 0.33 for inedible parts.Common litter effects Estimates were 0.37, 0.55 and 0.55 for live weight at slaughter, weight after bleedingand weight with viscera and without head, were ranged from 0.28 to 0.60 for edible parts, were estimates ranged from 0.38 to 0.42for dressing yield and ranged from 0.42 to 0.55 inedible parts.All possible genetic correlations betweencarcasscharacteristics were moderate to highandpositive except among TEDand both of LU and FUR, also between DR1 and VIS were negative.Common litter correlationsamongrecords ofcarcasscharacteristicswere mostly moderate or high, positiveand negative.Correlations ofenvironmental betweencarcasscharacteristicswere mostly moderate or high, positive and negative. Estimates of (rP)between records of different carcasscharacteristicswere mainly positive and moderate or high magnitude. No significant differences of breed on carcass characteristics except for giblet. New Zealand White had highest of giblet and leaver. In mostly parity affected significantly on carcass characteristics, the highest value during first parity except for giblet and leaver were highest value during second parity. Contrary other traits were insignificantly.Most carcass characteristics were higher in winter months than other seasons.

Estimation of genetic parameters for production and reproductive traits in Indian Karan-Fries cattle using multi-trait Bayesian approach.

Estimates of variance components are needed for implementing genetic selection. This study was conducted to genetic parameters for production and reproductive traits on Indian Karan-Fries cattle using multi-trait repeatability animal model. Data collected from ICAR-National Dairy Research Institute, Karnal, India (from 1988 to 2019) were used. Single-trait and multi-trait repeatability animal models were used for parameter estimation. The posterior mean of Heritability estimates for 305-day milk yield (305-DMY), lactation milk yield (LMY), lactation length (LL) were 0.20 ± 0.03, 0.19 ± 0.03 and 0.06 ± 0.02, respectively. For age at first calving (AFC), calving interval (CI), and days open (DO), the posterior mean of heritability estimates were 0.24 ± 0.08, 0.06 ± 0.01, and 0.07 ± 0.02, respectively. The repeatability estimates for 305-DMY, LMY, LL, CI, and DO were 0.37 ± 0.02, 0.34 ± 0.02, 0.15 ± 0.02, 0.09 ± 0.02, and 0.12 ± 0.02, respectively. Genetic correlation between milk production traits (305-DMY, LMY, and LL) was positive and strong (> 0.80). However, the genetic correlation between milk production trait and AFC ranges from - 0.31 to 0.12. Unfavorable strong genetic correlations were observed between production and reproductive traits (CI and DO) with values ranged from 0.5 to 0.7. Phenotypic correlations among 305-DMY, LMY, and LL were generally positive and high. The moderate heritability estimates and potential genetic variation for 305-DMY, TMY, and AFC suggested that genetic gain can be obtained for these traits through genetic selection. Low heritability estimates found for LL, CI and DO, indicating that the possibility of changing these traits through genetic selection is small. High genetic correlation observed between productive and fertility traits were unfavorable. The existed strong genetic and phenotypic correlation estimates between CI and DO indicates that recording only one of them would be sufficient in the herd. As the multi-trait model showed slight improvements in the h2 as well as r estimates for both productive and reproductive traits over univariate analysis, future selection with a multi-trait animal model applying Bayesian approach would be recommended.