Journal of Animal Breeding and Genetics最新文献

Body weight is an economically important trait in poultry that shows sexual dimorphism (SD). In the present study, variation in SD in Mazandaran native chickens was investigated in terms of the (Co) variance components and genetic parameters of body weight between males and females. Studied traits were body weights at hatch (BW1), 8 weeks (BW8) and 12 weeks of age (BW12). Also, for weight at sexual maturity (WSM) covariance components were only estimated in females. Cross-sex direct and maternal correlations were also estimated for studied traits except for WSM. For this purpose, a deep 21-generation pedigree and body weight data (57,576 BW1, 72,925 BW8, 62,727 BW12 and, 42,496 WSM) were used. Evaluation of SD of body weight was performed using six bivariate animal models with and without considering the genetic and permanent maternal environmental effects under the restricted maximum likelihood method in WOMBAT software. Model with direct additive genetic effects and maternal genetic effects without covariance between them was identified as the best model for BW1 and BW8. The Model including direct additive genetic effects and permanent maternal environmental effects was the best model for BW12 and WSM. Direct heritability (h²) estimates for BW1, BW8 and, BW12 were, respectively, 0.05 ± 0.013, 0.17 ± 0.02 and, 0.25 ± 0.03 in males and, 0.05 ± 0.012, 0.15 ± 0.01 and 0.21 ± 0.01 in females. Also, the direct heritability of WSM based on univariate analysis in females was estimated to be 0.40 ± 0.01. Maternal heritability ($��h_m^2�$) varied from 0.39 ± 0.01 (BW1) to 0.04 ± 0.009 (BW8) in males, and 0.36 ± 0.10 (BW1) to 0.04 ± 0.006 (BW8) in females. The correlation between direct genetic effects between males and females for BW1 was not significantly different from one. The direct genetic correlation between the two sexes for BW8 and BW12 was significantly different from 1 concluding that these traits are dimorphic in terms of direct genetic effects and therefore independent selection in both sexes is possible.

Inbreeding depression results in a decrease in the average phenotypic values of affected traits. It has been traditionally estimated from pedigree-based inbreeding coefficients. However, with the development of single-nucleotide polymorphism arrays, novel methods were developed for calculating the inbreeding coefficient, and consequently, inbreeding depression. The aim of the study was to analyse inbreeding depression in 6 growth and 2 reproductive traits in the Asturiana de los Valles cattle breed using both genealogical and molecular information. The pedigree group comprised 225,848 records and an average equivalent number of complete generations of 2.3. The molecular data comprised genotypes of 2693 animals using the Affymetrix medium-density chip. Using the pedigree information, three different inbreeding coefficients were estimated for the genotyped animals: the full pedigree coefficient (F_PED), and the recent and ancient inbreeding coefficients based on the information of the last three generations (F_PED<3G) and until the last three generations (F_PED>3G), respectively. Using the molecular data, seven inbreeding coefficients were calculated. Four of them were estimated based on runs of homozygosity (ROH), considering (1) the total length (F_ROH), (2) segments shorter than 4 megabases (F_ROH<4), (3) between 4 and 17 megabases (F_ROH4-17), and (4) longer than 17 Mb (F_ROH>17). Additionally, the three inbreeding coefficients implemented in the Plink software (F_HAT1-3) were estimated. Inbreeding depression was estimated using linear mixed-effects model with inbreeding coefficients used as covariates. All analysed traits (birth weight, preweaning average daily gain, weaning weight adjusted at 180 days, carcass weight, calving ease, age at first calving, calving interval) showed a statistically significant non-zero effect of inbreeding depression estimated from the pedigree group, except for the Postweaning Average Daily Gain trait. When inbreeding coefficients were based on the genomic group, statistically significant inbreeding depression was observed for two traits, Preweaning Average Daily Gain and Weaning Weight based on F_ROH, F_ROH>17, and F_HAT3 inbreeding coefficients. Nevertheless, similar to inbreeding depression estimated based on pedigree information, estimates of inbreeding depression based on genomic information had no relevant economic impact. Despite this, from a long-term perspective, genotyped data could be included to maximize genetic progress in genetic programs following an optimal genetic contribution strategy and to consider individual inbreeding load instead global inbreeding. ROH islands were identified on chromosomes 2, 3, 8, 10, and 16. Such regions contain several candidate genes for growth development, intramuscular fat, body weight and lipid metabolism that are related to production trai

Inbreeding depression is a genetic phenomenon associated with the loss of fitness and mean phenotypic performance due to mating between relatives. Historically, inbreeding coefficients have been estimated from pedigree information. However, the onset of genomic selection programs provides large datasets of individuals genotyped using SNP arrays, enabling more precise assessment of an individual's genomic-level inbreeding using genomic data. One of the traits most sensitive to issues stemming from increased inbreeding is reproduction. This is particularly important in equine, in which fertility is only moderate compared to other livestock species. To explore this further, we evaluated the effect of inbreeding on five reproductive traits (age at first foaling (AFF), average interval between foalings (AIF), total number of foalings (NF), productive life (PL) and reproductive efficiency (RE)) in Pura Raza Español mares using genomic data. Residual predicted phenotypes were obtained by purging these traits through the REML (wg_Residual) and ssGREML (g_Residual) approaches in reproductive data of 29,847 PRE mares using the BLUPF90+ program. Next, we used pedigree-based (F_ped) and ROH-based genomic (F_ROH) inbreeding coefficients derived from 1018 animals genotyped with 61,271 SNPs to estimate the inbreeding depression (linear regression). Our results indicated significant levels of inbreeding depression for all reproductive traits, with the exception of the AIF trait when F_ped was used. However, all traits were negatively affected by the increase in genomic inbreeding, and F_ROH was found to capture more inbreeding depression than F_ped. Likewise, REML models (ssGREML) using genomic data for estimated predicted residual phenotypes resulted in higher variance explained by the model compared with the models not using genomics (REML). Finally, a segmented regression analysis was conducted to evaluate the effect of inbreeding depression, revealing that the levels of genealogical and genomic homozygosity do not manifest uniformly in reproductive traits. In contrast, the levels of inbreeding depression ranged from low to high as homozygosity increased. This analysis also showed that reproductive traits are very sensitive to inbreeding depression, even with relatively low levels of homozygosity.

The objective of this study was to estimate genetic effects on parameters of the Brody and Richards growth curves using body weight records from birth to 12 months of age on 2287 Muzaffarnagari lamb for a period of 29 years (1976–2004). Estimated growth curve parameters were analysed using six univariate animal models, and genetic correlations among and between the parameters of each function and between parameters of the functions and observed birth and yearling weights were estimated using bivariate analyses. Significant environmental factors including birth year, sex, season, birth status and dam parity were included as fixed effects in all models. Likelihood ratio tests indicated that maternal genetic effects were significant only for birth weight (BW) and degree of maturity at birth (u₀) for the Brody and Richards functions. For these traits, direct heritabilities were similar (0.21, 0.19 and 0.17, respectively), but the estimated maternal heritability for BW (0.18) was twice that of u₀ for both functions. Heritabilites for yearling weight and asymptotic final body weights for the Brody and Richards functions were 0.28, 0.17 and 0.21, respectively. The remaining growth curve parameters were lowly heritable, ranging from zero for the predicted degree of maturity at the age of maximum growth rate for the Richards function to 0.08 for the maturing rate parameter of the Brody function. Genetic correlations between corresponding parameters for different growth functions exceeded 0.88. Our results showed that the Brody and Richards functions had similar genetic architecture, but the Richards function had no apparent advantages over the more easily interpreted Brody function. Failure to identify maternal genetic effects on maturing rate parameters suggested that both functions failed to identify potentially important maternal genetic effects. Therefore, there is no usefulness of estimated growth curve parameters in selection compared to the simple multi-trait genetic evaluations of individual body weights.

The study was done to determine additive, maternal and common permanent environmental effects and best-suited model for some production traits using six univariate animal models that differed in the (co)variance components fitted to assess the importance of maternal effect using likelihood ratio test in Murrah buffaloes. Data from 614 Murrah buffaloes related to production traits were collected from history pedigree sheets maintained at the buffalo farm, Department of Livestock Production and Management (LPM), LUVAS, Hisar. The production traits under this study were 305 days milk yield (305DMY), peak yield (PY), lactation length (LL), dry period (DP), lactation milk yield (LMY) and wet average (WA). The heritability estimates were in the range of 0.33–0.44 for 305DMY, 0.25–0.51 for PY, 0.05–0.13 for LL, 0.03–0.23 for DP, 0.17–0.40 for LMY and 0.37–0.66 for WA. Model 1 was considered best for most of the traits, viz., 305DMY, PY, LL, LMY and WA followed by model 2 for DP. Covariance and correlated values within the traits caused inflation of heritability in model 3 and model 6. The maximum covariance between the additive and maternal effect was found in trait LMY, which was 14,183.90 in model 3 and the minimum value was also reported in the same trait for model 6, valued at −3522.37. Multivariate analysis showed that all production traits were moderate to high and positively correlated with each other except for DP, which was low and negative genetic and phenotypic correlated. Spearman's rank correlation coefficients of breeding value among all six models were high and significant, ranged from 0.78 to 1.00 for all the traits except DP, therefore any of the models could be taken into account depending upon the availability of data.

Genomic structural variants (SVs) constitute a significant proportion of genetic variation in the genome. The rapid development of long-reads sequencing has facilitated the detection of long-fragment SVs. There is no published study to detect SVs using long-read data from sheep. We applied a long-read mapping approach to detect SVs and characterized a total of 30,771 insertions, deletions, inversions and translocations. We identified 716, 916, 842 and 303 specific SVs in Southdown sheep, Alpine merino sheep, Qilian White Tibetan sheep and Oula sheep, respectively. We annotated these SVs and found that these SV-related genes were primarily enriched in the well-established pathways involved in the regulation of the immune system, growth and development and environmental adaptability. We detected and annotated SVs based on NGS resequencing data to validate the accuracy based on third-generation detection. Moreover, five candidate SVs were verified using the PCR method in 50 sheep. Our study is the first to use a long-reads sequencing approach to construct a novel structural variation map in sheep. We have completed a preliminary exploration of the potential effects of SVs on sheep.

The objective of this study was to define desired genetic gains from economically important traits of Pacific white shrimp (Litopenaeus vannamei) using participatory approaches. Two questionnaires were sent out to 100 Pacific white shrimp farmers in all five Iranian shrimp farming provinces. Questionnaire A (Q-A) includes management factors and farming environments. Moreover, in this questionnaire, farmers were asked to rank the fourth most important traits in shrimp among 10 economic traits in the list for genetic improvement. In questionnaire B (Q-B), priorities of the four traits with the highest value were obtained using pairwise comparison. The results showed that the four most important traits were white spot syndrome virus resistance (WSSV), growth rate before 4 months (GR), acute hepatopancreatic necrosis disease resistance (AHPND), and female total weight at ablation (FTW). Medians of the best individual preference values were WSSV (0.222), GR (0.173), AHPND (0.157), and FTW (0.053). Most disagreements were found between the social group preference values in the commercial products and water salinity categories. Desired genetic gains were 1.71%, 1.57%, 0.53% and 0.31% for GR, AHPND, WSSV and FTW, respectively. This study highlighted that despite environmental and management differences, participatory approaches can achieve desired genetic results for Pacific white shrimp breeding programme.

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, β-lactoglobulin, sum of whey proteins, casein, α_s1-casein, α_s2-casein, β-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were −0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between β-lactoglobulin and κ-casein) to 0.892 (between α_s1-casein and α_s2-casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c²) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h²) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

Restricted selection is used to control genetic changes in one or more characters. Three main selection indices are adopted for this purpose. First, Kempthorne's index is used to maximize aggregate breeding value (BV) with changes in some traits restricted to zero; second, Harville's index is used to maximize aggregate BV with proportional changes for some traits; and third, Yamada's index is mathematically used to achieve the relative desired changes for all traits. Kempthorne's index is equivalent to Harville's index. However, the relationship between Kempthorne's and Yamada's indices has not been clarified. In addition, the characteristics of restricted selection indices and the relationship between BV and restricted BV (RBV) are also unknown. The aim of this study was to clarify the characteristics of restricted selection indices and describe the relationship between BV and RBV by using linear algebra and geometric techniques, respectively. First, I proved that Yamada's index is part of Kempthorne's index. Second, I investigated the relationship between BVs that were estimated using an ordinary selection index (EBVs) and RBVs estimated using a restricted selection index (ERBVs) and proved that the ERBVs of the restricted traits are proportional to the relative desired changes. Third, I proved that RBV is represented by a linear function of BV and geometrically represented the relationship between BV and RBV. In this study, new findings on restricted selection indices and RBV were obtained. This useful clarification of the relationship between BV and RBV will make it possible to evaluate the response to selection using not only a restricted selection index, but also a restricted BLUP in computer simulation studies.