Breeding Values Research Articles

Фенотипическая оценка клонов плюсовых деревьев сосны обыкновенной (Pinus sylvestris L.) в лесостепной зоне Башкирского Предуралья

An assessment of the main morphometric features of the ramets of clones of Scots pine plus trees in a comparative aspect has been carried out. The vegetative progeny of plus trees of the species are represented as part of a clonal plantation created in 2005 in the Dyurtyulinskiy forestry of the forest-steppe zone of the Republic of Bashkortostan in an area with the С2 type of forest growing conditions. Compliance with the breeding and genetic principle of phenotypic assessment of intraand intergroup differences in Scots pine clones and ramets, as well as methodical and methodological requirements for conducting the field stage of the study, has been ensured. Taxation indicators (the height and diameter of the stem, the diameter and length of the crown, the increment of the central shoot in height) have been taken into account for 100 clones at a continuous count. A varying nature of the distribution of the average values of the analyzed stem indicators in the vegetative progeny of plus trees has been revealed. The best growth in stem diameter (25.5±0.73 cm) and height (11.2±0.16 m), as well as central shoot increment (0.45±0.02 m), are typical for the progeny of clones of the plus tree 29/17. Clones 263/40, 264/41, 262/39 with taxation indicators above the average for the entire set of trees in the clone archive are of high breeding value, which indicates the specificity of the genotypes of these specimens. The levels of significance of the differences and the intensity of selection of the best clones of plus trees based on morphometric features have been determined, which indicates the different nature of the individual non-identity of each of the plus trees relative to the rest of the trees studied. The valuable genotypes of the progeny of Scots pine plus trees identified in the clone archive can be recommended for further use in forest seed breeding when creating highly productive and sustainable artificial plantations of this valuable tree species in the region.

Reviewing the definition of mortality in broiler chickens and its implications in genomic evaluations.

Mortality is an economically important trait usually handled as a discrete outcome from hatch time until selection in most broiler breeder programs. However, in other species, it has been shown that not only does the genetic component change over time, but also there are maternal genetic effects to be considered when mortality is recorded early in life. This study aimed to investigate alternative trait definitions of mortality with varying models and effects. Three years' worth of data were provided by Cobb-Vantress, Inc. and included 2 mortality traits. The first trait was binary, whether the bird died or not (OM), and the second trait was a categorical weekly mortality trait. After data cleaning, 6wk of data for the 2 given mortality traits were used to develop 5 additional trait definitions. The definitions were broiler mortality (BM), early and late mortality (EM & LM), and 2 traits with repeated records as cumulative or binary (CM and RM, respectively). Variance components were estimated using linear and threshold models to investigate whether either model had a benefit. Genomic breeding values were predicted using the BLUP90 software suite, and linear regression validation (LR) was used to compare trait definitions and models. Heritability estimates ranged from 0.01 (0.00) to 0.16 (0.01) under linear and 0.04 (0.01) to 0.21 (0.01) under threshold models, indicating genetic variability within the population across these trait definitions. The genetic correlation between EM and LM ranged from 0.48 to 0.81 across the different lines, indicating they have divergent genetic backgrounds and should be considered different traits. The LR accuracies showed that EM and LM used together in a 2-trait model have comparable accuracies to that of OM while giving a more precise picture of mortality. When including the maternal effect, the direct heritability considerably decreased for EM, indicating that the maternal effect plays an important role in early mortality. Therefore, a suitable approach would be a model with EM and LM while considering the maternal effect for EM. Single nucleotide polymorphism effects were estimated, and no individual SNP explained more than 1% of the additive genetic variance. Additionally, the SNP with the largest effect size and variance were inconsistent across trait definitions. Chicken mortality can be defined in different ways, and reviewing these definitions and models may benefit poultry breeding programs.

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

Genetic gain of Dekalb brand maize hybrids released during the first two decades of this century in the United States 95–115 relative maturity markets

AbstractEra studies are key to estimate the historic rate of genetic gain for commercial traits of a crop. We evaluated for the first time nearly all maize hybrids released (269) in five relative maturity markets (95, 100, 105, 110, and 115) from Bayer Crop Science Dekalb brand over the last 20 years. We focused on grain yield, plant height, moisture, and test weight. We then compared the predicted gains derived from the genome‐wide prediction models with the estimated ones. We found that genetic gain was positive and significant for grain yield across the five relative maturities, ranging from 0.077 to 0.147 Mg ha−1 year−1, while it was negative (desirable) and significant for plant height, with a decrease from −0.259 to −0.456 cm year−1. We did not find significant changes for grain harvest moisture and test weight. The genetic gain estimates were consistent with predictions using the company's genomic estimated breeding values. We concluded that breeding has delivered increased yield and decreased plant height over this period for Dekalb hybrids, and the company's genomewide prediction model can be used to predict the direction of future maize genetic gains. This era study includes a larger number of hybrids, broadens the range of relative maturities, and incorporates comparisons with genomewide predictions compared to previous era studies.

Adaptability of winter bread wheat varieties in the competitive variety testing in the Udmurt Republic

The purpose of the study was to evaluate the adaptive properties of winter wheat varieties in the competitive variety testing, to identify the best ones for the soil and climatic conditions of the Udmurt Republic based on a set of economically valuable traits. In 2020–2023 seven winter wheat varieties were tested in comparison with the standard ‘Volzhskaya K’ and the variety ‘Moskovskaya 39’. The adaptability of the varieties was determined according to the method of A.V. Kilchevsky and L.V. Khotyleva (1985), S.A. Eberhart, W.A. Russell (1966). Weather conditions during the years of study (drought in 2021 and 2023, excessive moisture in 2022) made it possible to evaluate varieties for drought resistance, determine their resistance to lodging, and the ability to use favorable conditions to form high yields. It was revealed that the most adapted varieties were ‘Cheberina’ and ‘26.12/6’, in 2021–2023 they formed the highest yield (50.0–50.1 hwt/ha) and provided a significant increase of 5.5–5.6 hwt/ha to the standard variety ‘Volzhskaya K’. These varieties significantly exceeded the standard variety in general adaptive capacity (GAC). There was found that the varieties ‘Lyubava’, ‘2.05/3’ and ‘Cheberina’ were the most valuable breeding varieties that combined high productivity with a stable yield. The varieties ‘Cheberina’, ‘26.12/6’, ‘Volzhskaya K’, ‘A-332/2’ were highly responsive to improved environmental conditions (bi > 1). Based on the study results, the adaptable, highly productive variety ‘Cheberina’ was sent to the state variety testing. There has been established that the variety forms a strong elastic stem of medium height, resistant to lodging, and increases ear weight and length, and its grain content in favorable conditions of the spring-summer period. An estimation of disease infection during the spring-summer growing season in 2022 showed that the variety possesses complex resistance to powdery mildew, leaf rust, and blotch of an ear and leaves. The variety formed grain of the third class with such indicators as 66 % of kernel hardness, 779 g/l of grain nature weight, 28 % of gluten of the first quality group, 12.8 % of protein in grain.

Results of evaluating the early maturity and yield potential of wild vetch varieties growing in Moscow Oblast

Wild-growing varieties are used in the breeding of Vicia sativa L. in order to expand genotypic diversity and improve the environmental stability of created varieties. In order to find wild-growing vetch possessing valuable breeding traits, 22 specimens from 15 countries were evaluated. Tests were conducted in a field experiment in the south of Moscow Oblast in 2018, 2020, and 2021. The ‘Vera’ variety served as the reference. On the basis of three-year data and in accordance with the International Classification of Vicia sativa L., the specimens were evaluated for early maturity, crop and seed production, seed size, leading shoot length, branchiness, number of pods per plant, and number of seeds in a pod. All the specimens were characterized by early maturity of fodder mass and seeds. The three-year average duration of the “sprouting- crop ripeness” period amounted to 34.7-46.3 days, while that of “sprouting-seed maturity” reached 59.0-67.5 days. In wild-growing specimens, the dry weight of the plant in the phase of crop ripeness ranged from 1.0 to 5.0 g (18-92 % of the reference). The fodder mass yield per plant was lower than that of the reference variety due to the poorer development of lateral shoots. Nineteen specimens were at the same level as or superior to the reference variety in terms of plant height. The three-year average seed yield of the specimens varied from 1.3 to 3.2 g (28-70 % of the reference). An analysis of seed yield showed that all specimens produced fewer pods than the reference. However, seven specimens were identified to be annually superior to the reference in terms of other traits: seed size (kk-36830, 36831, 36837, 36848, and 36850); the average number of seeds in the pod (kk-36871); the maximum number of pods in a node; the number of pods and the number of productive nodes on the leading shoot (kk-36879). These specimens are of interest for use in breeding vetch for seed yield.

He origin and breeding value of the breeding bulls from which cows with record productivity were obtained

He origin and breeding value of the breeding bulls from which cows with record productivity were obtained

Impact of truncating historical data on prediction ability of dairy sheep selection candidates

Along the last decades, the genetic evaluation methodology has evolved, improving breeding value estimates. Many breeding programmes have historical phenotypic records and large number of generations, but to make use of them could result in more inconveniences than benefits. In this study, the prediction ability of genotyped young animals was assessed by simultaneously evaluating the removal of historical data, two pedigree deepness and two methodologies (traditional BLUP and single−step genomic BLUP or ssGBLUP), using milk yield records of 40 years of three Latxa dairy sheep populations. The linear regression method was used to compare predictions of young rams before and after progeny testing, with six cut-off points, by intervals of 4 years (from 1992 to 2012), and statistics of ratio of accuracies, bias, and dispersion were calculated. The prediction accuracy of selection candidates, when genomic information was included, was the highest in all Latxa populations (between 0.54 and 0.69 with full data set). Nevertheless, the deletion of historical phenotypic data resulted on moderate accuracy gain in the bigger data size populations (mean gain 2.5%), and the smaller population took advantage of a moderate data deletion (2.7% gain by removing data until 2004), reducing accuracy when more records were removed. The bias of validation individuals was lower when the breeding value was predicted based on genomic information (between 2.1 and 13.9), being lower when the biggest amount of data was deleted in the bigger data size populations (5.2% reduction), and the smaller population was benefited from data deletion between 1996 and 2008 (3.8% bias reduction). Meanwhile, the slope of estimated genetic trend was lower when less data were included, and an overestimation of the unknown parent group estimates was observed. The results indicated that ssGBLUP evaluations were outstanding, compared with traditional BLUP evaluations, while the depth of pedigree had a very small influence, and deletion of historical phenotypic data was beneficial. Thus, Latxa routine genetic evaluations would benefit from truncating phenotypic records between 2000 and 2004, the use of two pedigree generations and the implementation of ssGBLUP methodology.

Combining Ability and Hybrid Breeding in Tunisian Melon (Cucumis melo L.) for Fruit Traits

A half-diallel cross study of seven melon inbred lines was carried out. The seven parents and their 21 F1 hybrids were evaluated for precocity of maturity, average weight per fruit, and fruit quality (fruit size, rind thickness, and soluble solids). The Diallel analysis was investigated for breeding values of these melon genotypes via general and specific combining ability, relationships between general and specific combining ability, and heterosis for the evaluated traits. The analysis of variance of the traits evaluated indicated highly significant differences among genotypes, suggesting the presence of adequate genetic variation for breeding. Additive genetic effects were most important with respect to fruit weight, while genetic dominance and epistasis effects mainly controlled fruit quality traits (fruit size, rind thickness, and TSS). Parent 1 (P1) and parent 3 (P3) had significant positive general combining ability effects for fruit weight. Also, P3 had positive general combining ability effects for fruit length and diameter, and cavity diameter. P3 was found to show maximum significant GCA in the desirable direction for all the traits except for TSS. Evaluation of heterosis (%) revealed that hybrid P1 × P3 can be considered as the best-performing hybrid for average fruit weight, TSS, and precocity, which also exhibited the highest positive and significant SCA effect for these traits. These results suggested that, among the melon genotypes studied, there is the potential to generate superior new varieties in hybrid production.

Comparison of genomic prediction accuracy using different models for egg production traits in Taiwan country chicken

In local chickens targeted for niche markets, genotyping costs are relatively high due to the small population size and diverse breeding goals. The single-step genomic best linear unbiased prediction (ssGBLUP) model, which combines pedigree and genomic information, has been introduced to increase the accuracy of genomic estimated breeding value (GEBV). Therefore, this model may be more beneficial than the genomic BLUP (GBLUP) model for genomic selection in local chickens. Additionally, the single-step genome-wide association study (ssGWAS) can be used to extend the ssGBLUP model results to animals with available phenotypic information but without genotypic data. In this study, we compared the accuracy of (G)EBVs using the pedigree-based BLUP (PBLUP), GBLUP, and ssGBLUP models. Moreover, we conducted single-SNP GWAS (SNP-GWAS), GBLUP-GWAS, and ssGWAS methods to identify genes associated with egg production traits in the NCHU-G101 chicken to understand the feasibility of using genomic selection in a small population. The average prediction accuracy of (G)EBV for egg production traits using the PBLUP, GBLUP, and ssGBLUP models is 0.536, 0.531, and 0.555, respectively. In total, 22 suggestive- and 5% Bonferroni genome-wide significant-level SNPs for total egg number (EN), average laying rate (LR), average clutch length, and total clutch number are detected using 3 GWAS methods. These SNPs are mapped onto Gallus gallus chromosomes (GGA) 4, 6, 10, 18, and 25 in NCHU-G101 chicken. Furthermore, through SNP-GWAS and ssGWAS methods, we identify 2 genes on GGA4 associated with EN and LR: ENSGALG00000023172 and PPARGC1A. In conclusion, the ssGBLUP model demonstrates superior prediction accuracy, performing on average 3.41% than the PBLUP model. The implications of our gene results may guide future selection strategies for Taiwan Country chickens. Our results highlight the applicability of the ssGBLUP model for egg production traits selection in a small population, specifically NCHU-G101 chicken in Taiwan.

Evaluation of Selective Culling as a Containment Strategy for African Swine Fever at a Vietnamese Sow Farm.

Selective culling, also known as the "tooth extraction approach", is a strategy for controlling African swine fever (ASF) by removing only sick and suspect animals instead of the entire herd in Vietnam. This method prioritizes preserving healthy animals, particularly valuable breeding pigs. Despite its implementation in various forms, no standardized protocol based on scientific principles has been established. Farms typically adapt this strategy based on their understanding, which can vary significantly. In implementing of selective culling that is not based on scientific principles, there is a significant risk of spreading the disease. The aim of this study is to evaluate the consequences of selective culling as currently implemented in Vietnam. Our analysis on a large sow farm revealed that current practices rely heavily on clinical observations without laboratory confirmations. This approach allows ASF-infected animals to remain on the farm longer, potentially exacerbating the spread of the virus. Thus, selective culling poses a substantial risk by potentially exacerbating the spread of disease. Our findings emphasize that early diagnosis of ASF and systematic removal of infected pigs are critical components for the effective implementation of selective culling strategies and that a high level of fragmentation to minimize contact between animals plays a key role. The optimal approach is to test conspicuous animals and separate them. Under no circumstances should suspect animals be left in the herd for several days before they become severely ill and succumb to the disease.

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of mid-infrared spectra traits for animal breeding

With the rapid development of animal phenomics and deep phenotyping, we can obtain thousands of traditional (but also molecular) phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the mega-scale linear mixed model (MegaLMM), a factor model-based approach that is able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding value (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of GEBV (U) prediction with TT models and MegaLMM. We used a total of 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits (average fat percentage [AFP], average methane production [ACH4], and average SCS [ASCS]) collected on 3,302 first-parity Holstein cows. The 3,421 milk MIR wavepoint traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNPs was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for AFP (0.51 vs. 0.93), ACH4 (0.30 vs. 0.86), and ASCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions for AFP (0.59 vs. 0.86), ACH4 (0.47 vs. 0.78), and ASCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., ACH4).

APIS: an updated parentage assignment software managing triploids induced from diploid parents.

In aquaculture, sterile triploids are commonly used for production as sterility gives them potential gains in growth, yields, and quality. However, they cannot be reproduced, and DNA parentage assignment to their diploid or tetraploid parents is required to estimate breeding values for triploid phenotypes. No publicly available software has the ability to assign triploids to their parents. Here, we updated the R package APIS to support triploids induced from diploid parents. First, we created new exclusion and likelihood tables that account for the double allelic contribution of the dam and the recombination that can occur during female meiosis. As the effective recombination rate of each marker with the centromere is usually unknown, we set it at 0.5 and found that this value maximizes the assignment rate even for markers with high or low recombination rates. The number of markers needed for a high true assignment rate did not strongly depend on the proportion of missing parental genotypes. The assignment power was however affected by the quality of the markers (minor allele frequency, call rate). Altogether, 96-192 SNPs were required to have a high parentage assignment rate in a real rainbow trout dataset of 1,232 triploid progenies from 288 parents. The likelihood approach was more efficient than exclusion when the power of the marker set was limiting. When more markers were used, exclusion was more advantageous, with sensitivity reaching unity, very low false discovery rate (<0.01), and excellent specificity (0.96-0.99). Thus, APIS provides an efficient solution to assign triploids to their diploid parents.

The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds.

Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain - K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K - 0.67, Ca - 0.61, Mg - 0.4, Mn - 0.5, Fe - 0.38, Zn - 0.46, Cu - 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.

Converting estimated breeding values from the observed to probability scale for health traits

Dairy cattle health traits are paramount from a welfare and economic viewpoint; therefore, modern breeding programs prioritize the genetic improvement of these traits. Estimated breeding values for health traits are published as the probability of animals staying healthy. They are obtained using threshold models, which assume that the observed binary phenotype (i.e., healthy or sick) is dictated by an underlying normally distributed liability exceeding or not a threshold. This methodology requires significant computing time and faces convergence challenges as it implies a nonlinear system of equations. Linear models have more straightforward computations and provide a robust approximation to threshold models; thus, they could be used to overcome the mentioned challenges. However, linear models yield estimated breeding values on the observed scale, requiring an approximation to the liability scale analogous to that from threshold models to later obtain the estimated breeding values on the probability scale. In addition, the robustness of the approximation of linear to threshold models depends on the amount of information and the incidence of the trait, with extreme incidence (i.e., ≤ 5%) deviating from optimal approximation. Our objective was to test a transformation from the observed to the liability and then to the probability scale in the genetic evaluation of health traits with moderate and very low (extreme) incidence. Data comprised displaced abomasum (5.1M), ketosis (3.6M), lameness (5M), and mastitis (6.3M) records from a Holstein population with a pedigree of 6M animals, of which 1.7M were genotyped. Univariate threshold and linear models were performed to predict breeding values. The agreement between estimated breeding values on the probability scale derived from threshold and linear models was assessed using Spearman rank correlations and comparison of estimated breeding values distributions. Correlations were at least 0.95, and estimated breeding value distributions almost entirely overlapped for all the traits but displaced abomasum, the trait with the lowest incidence (2%). Computing time was ∼3x longer for threshold than for linear models. In this Holstein population, the approximation was suboptimal for a trait with extreme incidence (2%). However, when the incidence was ≥6%, the approximation was robust, and its use is recommended along with linear models for analyzing categorical traits in large populations to ease the computational burden.

Estimated breeding values of dairy sires for cow colostrum and transfer of passive immunity traits

The objective of this study was to derive the estimated breeding values (EBVs) of Holstein sires for colostrum and passive transfer of immunity traits to a) estimate the genetic association between these new traits and established production, conformation, and functional, and b) explore whether sires can be classified in specific profiles regarding the new traits. For cow colostrum traits, the study included 699 daughters of 67 sires from 6 commercial dairy herds. The number of daughters per sire ranged from 5 to 49. Passive transfer of immunity was measured as the blood serum total protein content in 854 purebred Holstein calves of 61 sires from 8 commercial dairy herds. The number of calves per sire ranged from 5 to 44. Data were statistically analyzed using mixed models. Approximate genetic correlations of the derived sire EBVs for cow colostrum and calf serum total protein with EBVs for several other traits were estimated. Moreover, sires were classified into colostrum and calf serum total protein profile groups. Approximate genetic correlations of cow colostrum and calf serum traits with milk production, conformation, and functional traits were mostly unfavorable. Colostrum total solids and protein contents were negatively correlated with milk yield, fat yield, protein yield, productive life, and livability. The only favorable correlation found was between colostrum total solids/calf serum total protein and daughter pregnancy rate. However, several bulls had favorable profiles in both colostrum/passive transfer of immunity and production traits. Colostrum/passive immunity traits could be included in future genetic improvement programs after careful structure of relevant indexes.

Estimation of genetic correlations and genomic prediction accuracy for reproductive and carcass traits in Hanwoo cows.

This study estimated the heritabilities (h2) and genetic and phenotypic correlations between reproductive traits, including calving interval (CI), age at first calving (AFC), gestation length (GL), number of artificial inseminations per conception (NAIPC), and carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) in Korean Hanwoo cows. In addition, the accuracy of genomic predictions of breeding values was evaluated by applying the genomic best linear unbiased prediction (GBLUP) and the weighted GBLUP (WGBLUP) method. The phenotypic data for reproductive and carcass traits were collected from 1,544 Hanwoo cows, and all animals were genotyped using Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The genetic parameters were estimated using a multi-trait animal model using the MTG2 program. The estimated h2 for CI, AFC, GL, NAIPC, CWT, EMA, BF, and MS were 0.10, 0.13, 0.17, 0.11, 0.37, 0.35, 0.27, and 0.45, respectively, according to the GBLUP model. The GBLUP accuracy estimates ranged from 0.51 to 0.74, while the WGBLUP accuracy estimates for the traits under study ranged from 0.51 to 0.79. Strong and favorable genetic correlations were observed between GL and NAIPC (0.61), CWT and EMA (0.60), NAIPC and CWT (0.49), AFC and CWT (0.48), CI and GL (0.36), BF and MS (0.35), NAIPC and EMA (0.35), CI and BF (0.30), EMA and MS (0.28), CI and AFC (0.26), AFC and EMA (0.24), and AFC and BF (0.21). The present study identified low to moderate positive genetic correlations between reproductive and CWT traits, suggesting that a heavier body weight may lead to a longer CI, AFC, GL, and NAIPC. The moderately positive genetic correlation between CWT and AFC, and NAIPC, with a phenotypic correlation of nearly zero, suggesting that the genotype-environment interactions are more likely to be responsible for the phenotypic manifestation of these traits. As a result, the inclusion of these traits by breeders as selection criteria may present a good opportunity for developing a selection index to increase the response to the selection and identification of candidate animals, which can result in significantly increased profitability of production systems.

Non-genetic Factors Associated with 305 Days Milk Yield in Kankrej Cattle

The performance data of Kankrej cattle for the present study were collected from the National Dairy Development Board (NDDB), Anand, under the pedigree selection programme (PS) in Banaskantha district of Gujarat state. After the standardization of initial records, the 305DMY records of 6037 Kankrej cattle sired by 54 bulls calved during the period of 19 years from 2003 to 2022 were utilized in the study. The least squares analysis of variance to reduce the nonorthogonality after considering their villages, parity, Age at calving groups, year-month of birth groups and year-month of calving groups were used to analyze the data on 305DMY production trait. The average 305DMY for the Kankrej cow was estimated to be 2302 ± 9.01 kg. The fixed factors such as parity, villages and year month of the calving group considered in the study had significant (p&lt;0.05) effects on the 305DMY traits except for the Age in the calving group (months) and year-month of the birth group. The non-significant effect of the year and month of birth highlighted the adaptive traits of the Kankrej cattle against the extreme temperature in the arid or semi-arid region of North Gujarat. The maximum estimated corrected yield and breeding value were estimated to be 3420 kg and 855 kg, respectively. The increased production performances observed in the study may be the result of the successful implementation of pedigree selection under the field conditions.

Study of HSP70 Gene and Relationship to Milk Productivity and Fat Ratio Holstein Cows

The study was conducted on 75 Holstein cows imported from Germany to determine the chi-square value, the breeding value, the additive and dominance effect, and the effect of the allele of the HSP70 gene on the variation of total milk production and fat ratio. The HSP70 gene was divided into three bands, each with a length of 922, 935 and 932 bp. Molecular analysis of the gene shows the presence of 12-point mutations distributed in three primers (T825C, G856A, G937A, A1135G, G1185C, G1753A, C1867T, A2017G, C2548T, G2572T, G2688C and T2734C). Mutation T825C changed the amino acid Ata to Lie, and mutation G856A changed the amino acid Aca. to Thr. The result showed that the chi-square value was significant in the mutations (C1867T, A2017G and G2688C) reached (4.25, 3.90 and 4.16). The higher milk production in the CT genotype of the C1867T mutation was 5442.7 kg/season, the higher breeding value, dominance deviation, additive and dominance variance (652.3, 1460, 726856 and 235183) respectively for total milk production. The higher ratio of fat in the CT genotype of the C2548T mutation was (5.04%), and the higher breeding value, dominance deviation, additive and dominance variance (1.30, 1.70, 0.576 and 0.0793), respectively for the ratio of fat.

Multi-model approach for optimizing cold-wave resilient maize selection: unveiling genotype-by-environment interaction and predicting yield stability

Cold waves both significantly reduce yield & damage crops as well; unforeseeable nature of cold waves makes it challenging for farmers to manage risk. Thus, we aim to select maize hybrids that thrive under cold stress (both escaping early cold-waves and tolerating cold snaps); pinpoint stable, high-yielding hybrids ideal for regions prone to cold stress. In this investigation 2 years winter trial in Nepal was appraised on diverse maize hybrids for cold wave tolerant, stable across four stations with a Randomized Complete Block Design & 3 replications at each station. Likewise, this research employed 4 statistical methods both fixed effect and linear mixed model: genotype-environment interactions (AMMI), visually analyzes genotype performance and stability across environments (GGE) breeding values of genotypes for selection (BLUP), multiple traits for selection (MTSI). This scholarship revealed significant (P < 0.001) impacts of genotype, environment, and their interaction (GEI) on yield. This GEI, accounting for 100% of yield variance, was mainly captured by 3 principal components, with the first explaining 49%. Notably, mixed-effects models and biplots identified superior hybrids exhibiting both high average yields and consistent performance. GGE biplot analysis unveiled high-yielding and adaptable: GK3157, NK6607, RMH1899 Super, GK3254, RMH666, Shan 111, DKC9149, and Sweety-1. Further, BLUP and WAASBYY analyses delineated the superior performers and stabilized hybrids for yield, with DKC9141, Uttam 121, NK6607, MM2929, RMH-666, GK 3254, and GK3157, and RMH-1899 super candidates for both high yield and stability. In Nepalgunj, Delta 3333, MM2122, and Shaan 111 excelled in both yield and stability, while Rampur favored Rampur Hybrid 6 and MM2424 for stability. Parwanipur and Tarahara shared similar winners for stability and yield, including MM2122, Shaan 111, and Delta 3333 in Parwanipur, and NK7884, MM2424, and Delta 2222 in Tarahara. Based on Multi genotype ideotype distance (MGDI), 9 hybrids were selected for yield and stability, including MM 2033, NK 6607, Sweety 1 so on; exhibited escape to cold waves whilst GK3254, TMMH-846, and MM-9442, were chosen for cold waves adapted hybrids. Moreover, by identifying cold-tolerant maize hybrids, this study has potential to mitigate risks for farmers (economic burden, crop failure) and bolster food security.