Genome-wide association study for stayability at different calvings in Nellore beef cattle.

BACKGROUNDING: Stayability, which may be defined as the probability of a cow remaining in the herd until a reference age or at a specific number of calvings, is usually measured late in the animal's life. Thus, if used as selection criteria, it will increase the generation interval and consequently might decrease the annual genetic gain. Measuring stayability at an earlier age could be a reasonable strategy to avoid this problem. In this sense, a better understanding of the genetic architecture of this trait at different ages and/or at different calvings is important. This study was conducted to identify possible regions with major effects on stayability measured considering different numbers of calvings in Nellore cattle as well as pathways that can be involved in its expression throughout the female's productive life. RESULTS: The top 10 most important SNP windows explained, on average, 17.60% of the genetic additive variance for stayability, varying between 13.70% (at the eighth calving) and 21% (at the fifth calving). These SNP windows were located on 17 chromosomes (1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 19, 20, 27, and 28), and they harbored a total of 176 annotated genes. The functional analyses of these genes, in general, indicate that the expression of stayability from the second to the sixth calving is mainly affected by genetic factors related to reproductive performance, and nervous and immune systems. At the seventh and eighth calvings, genes and pathways related to animal health, such as density bone and cancer, might be more relevant. CONCLUSION: Our results indicate that part of the target genomic regions in selecting for stayability at earlier ages (from the 2th to the 6th calving) would be different than selecting for this trait at later ages (7th and 8th calvings). While the expression of stayability at earlier ages appeared to be more influenced by genetic factors linked to reproductive performance together with an overall health/immunity, at later ages genetic factors related to an overall animal health gain relevance. These results support that selecting for stayability at earlier ages (perhaps at the second calving) could be applied, having practical implications in breeding programs since it could drastically reduce the generation interval, accelerating the genetic progress.

Genomic evaluation for breeding and genetic management in Cordia africana, a multipurpose tropical tree species.

BACKGROUND: Planting tested forest reproductive material is crucial to ensure the increased resilience of intensively managed productive stands for timber and wood product markets under climate change scenarios. Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) analysis is a cost-effective option for using genomic tools to enhance the accuracy of predicted breeding values and genetic parameter estimation in forest tree species. Here, we tested the efficiency of ssGBLUP in a tropical multipurpose tree species, Cordia africana, by partial population genotyping. A total of 8070 trees from three breeding seedling orchards (BSOs) were phenotyped for height. We genotyped 6.1% of the phenotyped individuals with 4373 single nucleotide polymorphisms. The results of ssGBLUP were compared with pedigree-based best linear unbiased prediction (ABLUP) and genomic best linear unbiased prediction (GBLUP), based on genetic parameters, theoretical accuracy of breeding values, selection candidate ranking, genetic gain, and predictive accuracy and prediction bias. RESULTS: Genotyping a subset of the study population provided insights into the level of relatedness in BSOs, allowing better genetic management. Due to the inbreeding detected within the genotyped provenances, we estimated genetic parameters both with and without accounting for inbreeding. The ssGBLUP model showed improved performance in terms of additive genetic variance and theoretical breeding value accuracy. Similarly, ssGBLUP showed improved predictive accuracy and lower bias than the pedigree-based relationship matrix (ABLUP). CONCLUSIONS: This study of C. africana, a species in decline due to deforestation and selective logging, revealed inbreeding depression. The provenance exhibiting the highest level of inbreeding had the poorest overall performance. The use of different relationship matrices and accounting for inbreeding did not substantially affect the ranking of candidate individuals. This is the first study of this approach in a tropical multipurpose tree species, and the analysed BSOs represent the primary effort to breed C. africana.

Synthesis, anti-microbial, and docking studies of functionalized chromenyl phosphonates using ionic liquid catalyst.

Synthesis of functionalized chromenyl phosphonates by the reaction among 2-hydorxybenzaldehydes, dicyanoethane, and dialkyl phosphonates that was promoted by choline hydroxide ionic liquid catalyzes the simultaneous, Knoevenagel, Pinner, and phospha-Michael reactions, under neat condition at room temperature. Important phosphorus-containing compounds can be produced at a reasonable cost because of the mild reaction conditions and the inexpensive promoter choline hydroxide. Furthermore, the desired products can be obtained without the need for any extraction or chromatography steps. An alternate technique for the simple and high-yield synthesis of functionalized chromenyl phosphonates is offered by this protocol. The synthesized compounds were studied by anti-microbial activity and docking studies. The title compounds molecular docking investigations demonstrated their efficacy as therapeutic agents against DNA Gyrase B and Aspergillus niger endoglucanase in both antibacterial and antifungal inhibition, and they identified compounds 4a, 4d, 4l, 4p, and 4q as promising candidates for microbial treatment, with binding affinities ranging from - 6.9 to - 7.4 kcal/mol.

The marker effects of a single-step random regression model for 4 test-day traits in German Holsteins.

The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).

Exploring unknown parent groups and metafounders in single-step genomic best linear unbiased prediction: Insights from a simulated cattle population.

This study explores how the metafounder (MF) concept enhances genetic evaluations in dairy cattle populations using single-step genomic best linear unbiased prediction (ssGBLUP). By improving the consideration of relationships among founder populations, MF ensures accurate alignment of pedigree and genomic relationships. The research aims to propose a method for grouping MF based on genotypic information, assess different approaches for estimating the gamma matrix, and compare unknown parent groups (UPG) and MF methodologies across various scenarios, including those with low and high pedigree completeness based on a simulated dairy cattle population. In the scenario where unknown ancestors are rare, the effect of UPG or MF on breeding values is minimal but MF still performs slightly better compared with UPG. The scenario with lower genotyping rates and more unknown parents shows significant differences in evaluations with and without UPG and also compared with MF. The study shows that ssGBLUP evaluations where UPG are considered via Quaas-Pollak-transformation in the pedigree-based and genomic relationship matrix (UPG_fullQP) results in double counting and subsequently in a pronounced bias and overdispersion. Another focus is on the estimation of the gamma matrix, emphasizing the importance of crossbred genotypes for accuracy. Challenges emerge in classifying animals into subpopulations and further into MF or UPG, but the method used in this study, which is based on genotypes, results in predictions which are comparable to those obtained using the true subpopulations for the assignment. Estimated validation results using the linear regression method confirm the superior performance of MF evaluations, although differences compared with true validations are smaller. Notably, UPG_fullQP's extreme bias is less evident in routine validation statistics.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations.

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

How pedigree errors affect genetic evaluations and validation statistics.

Pedigrees used in genetic evaluations contain errors. Because of such errors, assumptions regarding the relatedness among individuals in genetic evaluation models are wrong. Consequences of that have been investigated in earlier studies focusing on models that did not account for genomic information yet. The objective of this work was to investigate the effects of pedigree errors on the results from genetic evaluations using the single-step model, and the effect of such effects on results from validation studies with forward prediction. We used a real pedigree (n = 361,980) and real genotypes (n = 25,950) of Fleckvieh cattle, sampled in a way to provide a good consistency between pedigree and genomic relationships. Given the real pedigree and genotypes, true breeding values (TBV) were simulated to have a covariance structure equal to the matrix H assumed in a single-step model. Based on TBV, phenotypes were simulated with a heritability of 0.25. Genetic evaluations were conducted with a conventional animal model (i.e., without genomic information) and a single-step animal model under scenarios using either the correct pedigree or a pedigree containing 5%, 10%, or 20% of wrong records. Wrong records were simulated by randomly assigning wrong sires to nongenotyped females. The increasing rates of pedigree errors led to decreasing correlations between TBV and EBV and lower standard deviations of predictions. Less variation was observed because pedigree errors operate actually as a random exchange of daughters among bulls, making them look more similar to each other than they actually are. This occurs of course only when animals have progeny. Therefore, this decreased variation was more pronounced for progeny tested bulls than for young selection candidates. In a forward prediction validation scenario, the stronger decrease in variation when animals get progeny caused an apparent inflation of early predictions. This phenomenon may contribute to the usually observed problem of inflation of early predictions observed in validation studies.

Live birth outcomes following single-step blastocyst warming technique - optimizing efficiency without impacting live birth rates.

PURPOSE: To evaluate the impact of a single-step (SS) warming versus standard warming (SW) protocol on the survival/expansion of vitrified blastocysts and their clinical outcomes post-frozen embryo transfer (FET). METHODS: Retrospective analysis was performed on 200 vitrified/warmed research blastocysts equally divided amongst two thawing protocols utilizing the Fujifilm Warming NX kits (Fujifilm, CA). SW utilized the standard 14-minute manufacturer's guidelines. SS protocol required only a one-minute immersion in thaw solution (TS) before the embryos were transferred to culture media. A time-interrupted study was performed evaluating 752 FETs (SW: 376 FETs, SS 376 FETs) between April 2021-December 2022 at a single academic fertility clinic in Boston, Massachusetts. Embryologic, clinical pregnancy, and live birth outcomes were assessed using generalized estimated equation (GEE) models, which accounted for potential confounders. RESULTS: There was 100% survival for all blastocysts (n = 952 embryos) with no differences in blastocyst re-expansion regardless of PGT status. Adjusted analysis showed no differences in implantation, clinical pregnancy, spontaneous abortion, or biochemical pregnancy rate. A higher odds of multiple gestation [AdjOR(95%CI) 1.06 (1.01, 1.11), p = 0.019] were noted, even when adjusting for number of embryos transferred [AdjOR(95%CI) 1.05 (1.01, 1.10)]. Live birth outcomes showed no differences in live birth rates or birthweight at delivery. CONCLUSIONS: The study found equivalent outcomes for SS and SW in all parameters except for a slight rise in the rate of multiple gestations. The results suggest that SS warming is an efficient, viable alternative to SW, reducing thaw times without adverse effects on live birth rates or neonatal birth weights.

Efficient AAV9 Purification Using a Single-Step AAV9 Magnetic Affinity Beads Isolation.

Adeno-associated viruses (AAVs) have emerged as promising tools for gene therapy due to their safety and efficacy in delivering therapeutic genes or gene editing sequences to various tissues and organs. AAV serotype 9 (AAV9), among AAV serotypes, stands out for its ability to efficiently target multiple tissues, thus holding significant potential for clinical applications. However, existing methods for purifying AAVs are cumbersome, expensive, and often yield inconsistent results. In this study, we explore a novel purification strategy utilizing Dynabeads™ CaptureSelect™ magnetic beads. The AAV9 magnetic beads capture AAV9 with high specificity and recovery between 70 and 90%, whereas the AAVX magnetic beads did not bind to the AAV9. Through continuous interaction with AAVs in solution, these beads offer enhanced clearance of genomic DNA and plasmids even in the absence of endonuclease. The beads could be regenerated at least eight times, and the used beads could be stored for up to six months and reused without a significant reduction in recovery. The potency of the AAV9-purified vectors in vivo was comparable to that of iodixanol purified vectors.

Deep learning-regularized, single-step quantitative susceptibility mapping quantification.

The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS-POCSnet, a data fidelity term based on a single-step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS-POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS-POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS-POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility-tuned structured similarity index measure of 0.823 ± 0.02, high-frequency error norm of 37.0 ± 5.7, and peak signal-to-noise ratio of 42.8 ± 1.1. SS-POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS-POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning-regularized, single-step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.

Effective isolation of cannabidiol and cannabidiolic acid free of psychotropic phytocannabinoids from hemp extract by fast centrifugal partition chromatography.

Cannabidiol (CBD), together with its precursor cannabidiolic acid (CBDA), is the major phytocannabinoid occurring in most hemp cultivars. To ensure the safe use of these compounds, their effective isolation from hemp extract is required, with special emphasis on the elimination of ∆9-tetrahydrocannabinol (∆9-THC) and ∆9-tetrahydrocannabinolic acid (∆9-THCA-A). In this study, we demonstrate the applicability of fast centrifugal partition chromatography (FCPC) as a challenging format of counter-current preparative chromatography for the isolation of CBD and CBDA free of psychotropic compounds that may occur in Cannabis sativa L. plant extracts. Thirty-eight solvent mixtures were tested to identify a suitable two-phase system for this purpose. Based on the measured partition coefficients (KD) and separation factors (α), the two-phase system consisting of n-heptane:ethyl acetate:ethanol:water (1.5:0.5:1.5:0.5; v:v:v:v) was selected as an optimal solvent mixture. Employing UHPLC-HRMS/MS for target analysis of collected fractions, the elution profiles of 17 most common phytocannabinoids were determined. Under experimental conditions, the purity of isolated CBD and CBDA was 98.9 and 95.1% (w/w), respectively. Neither of ∆9-THC nor of ∆9-THCA-A were present; only trace amounts of other biologically active compounds contained in hemp extract were detected by screening against in-house spectral library using UHPLC-HRMS.

Multiple test procedures of disease prevalence based on stratified partially validated series in the presence of a gold standard.

This paper discusses the problem of disease prevalence in clinical studies, focusing on multiple comparisons based on stratified partially validated series in the presence of a gold standard. Five test statistics, including two Wald-type test statistics, the inverse hyperbolic tangent transformation test statistic, likelihood ratio test statistic, and score test statistic, are proposed to conduct multiple comparisons. To control the overall type I error rate, several adjustment procedures are developed, namely the Bonferroni, Single-step adjusted MaxT, Single-step adjusted MinP, Holm's Step-down, and Hochberg's step-up procedures, based on these test statistics. The performance of the proposed methods is evaluated through simulation studies in terms of the empirical type I error rate and empirical power. Simulation results show that the Single-step adjusted MaxT procedure and Single-step adjusted MinP procedure generally outperform the other three procedures, and these two test procedures based on all test statistics have satisfactory performance. Notably, the Single-step adjusted MinP procedure tends to exhibit higher empirical power than the Single-step adjusted MaxT procedure. Furthermore, the Step-down and Step-up procedures show greater power compared to the Bonferroni method. The study also observes that as the validated ratio increases, the empirical type I errors of all test procedures approach the nominal level while maintaining higher power. Two real examples are presented to illustrate the proposed methods.

Invited review: Reliability computation from the animal model era to the single-step genomic model era.

The calculation of exact reliabilities involving the inversion of mixed model equations poses a heavy computational challenge when the system of equations is large. This has prompted the development of different approximation methods. We give an overview of the various methods and computational approaches in calculating reliability from the era before the animal model to the era of single-step genomic models. The different methods are discussed in terms of modeling, development, and applicability in large dairy cattle populations. The paper also describes the problems faced in reliability computation. Many details dispersed throughout the literature are presented in this paper. It is clear that a universal solution applicable to every model and input data may not be possible, but we point out several efficient and accurate algorithms developed recently for a variety of very large genomic evaluations.

Comparison of different validation methods for single-step genomic evaluations based on a simulated cattle population.

The validation of estimated breeding values from single-step genomic BLUP (ssGBLUP) is an important topic, as more and more countries and animal populations are currently changing their genomic prediction to single-step. The objective of this work was to compare different methods to validate single-step genomic breeding values (GEBV). The investigations were carried out using a simulation study based on the German-Austrian-Czech Fleckvieh population. To test the validation methods under different conditions, several biased and unbiased scenarios were simulated. The application of the widely used Interbull GEBV test to the single-step method is only possible to a limited extent, partly because of genomic preselection, which biases conventional estimated breeding values. Alternative validation methods considered in the study are the linear regression method proposed by Legarra and Reverter, the improved genomic validation including additional regressions as suggested by VanRaden and an adaptation of the Interbull GEBV test using daughter yield deviations (DYD) from ssGBLUP instead of pedigree BLUP. The comparison of the different methods for the different scenarios showed that for males the methods based on GEBV estimate the dispersion more accurate and less biased compared with the GEBV test using DYD from ssGBLUP, whereas the standard Interbull GEBV test is highly affected by genomic preselection for males. For females, the GEBV test using yield deviations from ssGBLUP results in better estimations for the true dispersion.

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats.

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

Estimation of genetic parameters and single-step genome-wide association studies for milk urea nitrogen in Holstein cattle.

The main objectives of this study were to estimate genetic parameters for milk urea nitrogen (MUN) in Holstein cattle and to conduct a single-step (ss)GWAS to identify candidate genes associated with MUN. Phenotypic measurements from 24,435 Holstein cows were collected from March 2013 to July 2019 in 9 dairy farms located in the Beijing area, China. A total of 2,029 cows were genotyped using the Illumina 150K Bovine Bead Chip, containing 121,188 SNP. A single-trait repeatability model was used to evaluate the genetic background of MUN. We found that MUN is a trait with low heritability (0.06 ± 0.004) and repeatability (0.12). Considering similar milk production levels, a lower MUN concentration indicates higher nitrogen digestibility. The genetic correlations between MUN and milk yield, net energy concentration, fat percentage, protein percentage, and lactose percentage were positive and ranged from 0.02 to 0.26. The genetic correlation between MUN and somatic cell score (SCS) was negative (-0.18), indicating that animals with higher MUN levels tend to have lower SCS. Both ssGWAS and pathway enrichment analyses were used to explore the genetic mechanisms underlying MUN. A total of 18 SNP (located on BTA11, BTA12, BTA14, BTA17, and BTA18) were found to be significantly associated with MUN. The genes CFAP77, CAMSAP1, CACNA1B, ADGRB1, FARP1, and INTU are considered to be candidate genes for MUN. These candidate genes are associated with important biological processes such as protein and lipid metabolism and binding to specific proteins. This set of candidate genes, metabolic pathways, and their functions provide a better understanding of the genomic architecture and physiological mechanisms underlying MUN in Holstein cattle.

Single-step genomic predictions for heat tolerance of production yields in US Holsteins and Jerseys.

The physiological stress caused by excessive heat affects dairy cattle health and production. This study sought to investigate the effect of heat stress on test-day yields in US Holstein and Jersey cows and develop single-step genomic predictions to identify heat tolerant animals. Data included 12.8 million and 2.1 million test-day records, respectively, for 923,026 Holstein and 153,710 Jersey cows in 27 US states. From 2015 through 2021, test-day records from the first 5 lactations included milk, fat, and protein yields (kg). Cow records were included if they had at least 5 test-day records per lactation. Heat stress was quantified by analyzing the effect of a 5-d hourly average temperature-humidity index (THI5d¯) on observed test-day yields. Using a multiple trait repeatability model, a heat threshold (THI threshold) was determined fowr each breed based on the point that the average adjusted yields started to decrease, which was 69 for Holsteins and 72 for Jerseys. An additive genetic component of general production and heat tolerance production were estimated using a multiple trait reaction norm model and single-step genomic BLUP methodology. Random effects were regressed on a function of 5-d hourly average (THI5d¯) and THI threshold. The proportion of test-day records that occurred on or above the respective heat thresholds was 15% for Holstein and 10% for Jersey. Heritability of milk, fat, and protein yields under heat stress for Holsteins increased, with a small standard error, indicating that the additive genetic component for heat tolerance of these traits was observed. This was not as evident in Jersey traits. For Jersey, the permanent environment explained the same or more of the variation in fat and protein yield under heat stress indicating that nongenetic factors may determine heat tolerance for these Jersey traits. Correlations between the general genetic merit of production (in the absence of heat stress) and heat tolerance genetic merit of production traits were moderate in strength and negative. This indicated that selecting for general genetic merit without consideration of heat tolerance genetic merit of production may result in less favorable performance in hot and humid climates. A general genomic estimated breeding value for genetic merit and a heat tolerance genomic estimated breeding value were calculated for each animal. This study contributes to the investigation of the impact of heat stress on US dairy cattle production yields and offers a basis for the implementation of genomic selection. The results indicate that genomic selection for heat tolerance of production yields is possible for US Holsteins and Jerseys, but a study to validate the genomic predictions should be explored.

Application of single-step single nucleotide polymorphism best linear unbiased predictor model with unknown-parent groups for type traits in Japanese Holsteins.

The objectives of this study were to investigate the computational performance and the predictive ability and bias of a single-step SNP BLUP model (ssSNPBLUP) in genotyped young animals with unknown-parent groups (UPG) for type traits, using national genetic evaluation data from the Japanese Holstein population. The phenotype, genotype, and pedigree data were the same as those used in a national genetic evaluation of linear type traits classified between April 1984 and December 2020. In the current study, 2 data sets were prepared: the full data set containing all entries up to December 2020 and a truncated data set ending with December 2016. Genotyped animals were classified into 3 types: sires with classified daughters (S), cows with records (C), and young animals (Y). The computing performance and prediction accuracy of ssSNPBLUP were compared for the following 3 groups of genotyped animals: sires with classified daughters and young animals (SY); cows with records and young animals (CY); and sires with classified daughters, cows with records, and young animals (SCY). In addition, we tested 3 parameters of residual polygenic variance in ssSNPBLUP (0.1, 0.2, or 0.3). Daughter yield deviations (DYD) for the validation bulls and phenotypes adjusted for all fixed effects and random effects other than animal and residual (Yadj) for the validation cows were obtained using the full data set from the pedigree-based BLUP model. The regression coefficients of DYD for bulls (or Yadj for cows) on the genomic estimated breeding value (GEBV) using the truncated data set were used to measure the inflation of the predictions of young animals. The coefficient of determination of DYD on GEBV was used to measure the predictive ability of the predictions for the validation bulls. The reliability of the predictions for the validation cows was calculated as the square of the correlation between Yadj and GEBV divided by heritability. The predictive ability was highest in the SCY group and lowest in the CY group. However, minimal difference was found in predictive abilities with or without UPG models using different parameters of residual polygenic variance. The regression coefficients approached 1.0 as the parameter of residual polygenic variance increased, but regression coefficients were mostly similar regardless of the use of UPG across the groups of genotyped animals. The ssSNPBLUP model, including UPG, was demonstrated as feasible for implementation in the national evaluation of type traits in Japanese Holsteins.

Culture conditions in the IVF laboratory: state of the ART and possible new directions.

In the last four decades, the assisted reproductive technology (ART) field has witnessed advances, resulting in improving pregnancy rates and diminishing complications, in particular reduced incidence of multiple births. These improvements are secondary to advanced knowledge on embryonic physiology and metabolism, resulting in the ability to design new and improved culture conditions. Indeed, the incubator represents only a surrogate of the oviduct and uterus, and the culture conditions are only imitating the physiological environment of the female reproductive tract. In vivo, the embryo travels through a dynamic and changing environment from the oviduct to the uterus, while in vitro, the embryo is cultured in a static fashion. Importantly, while culture media play a critical role in optimising embryo development, a large host of additional factors are equally important. Additional potential variables, including but not limited to pH, temperature, osmolality, gas concentrations and light exposure need to be carefully controlled to prevent stress and permit optimal implantation potential. This manuscript will provide an overview of how different current culture conditions may affect oocyte and embryo viability with particular focus on human literature.

Single-step SNPBLUP evaluation in six German beef cattle breeds.

The implementation of genomic selection for six German beef cattle populations was evaluated. Although the multiple-step implementation of genomic selection is the status quo in most national dairy cattle evaluations, the breeding structure of German beef cattle, coupled with the shortcoming and complexity of the multiple-step method, makes single step a more attractive option to implement genomic selection in German beef cattle populations. Our objective was to develop a national beef cattle single-step genomic evaluation in five economically important traits in six German beef cattle populations and investigate its impact on the accuracy and bias of genomic evaluations relative to the current pedigree-based evaluation. Across the six breeds in our study, 461,929 phenotyped and 14,321 genotyped animals were evaluated with a multi-trait single-step model. To validate the single-step model, phenotype data in the last 2 years were removed in a forward validation study. For the conventional and single-step approaches, the genomic estimated breeding values of validation animals and other animals were compared between the truncated and the full evaluations. The correlation of the GEBVs between the full and truncated evaluations in the validation animals was slightly higher in the single-step evaluation. The regression of the full GEBVs on truncated GEBVs was close to the optimal value of 1 for both the pedigree-based and the single-step evaluations. The SNP effect estimates from the truncated evaluation were highly correlated with those from the full evaluation, with values ranging from 0.79 to 0.94. The correlation of the SNP effect was influenced by the number of genotyped animals shared between the full and truncated evaluations. The regression coefficients of the SNP effect of the full evaluation on the truncated evaluation were all close to the expected value of 1, indicating unbiased estimates of the SNP markers for the production traits. The Manhattan plot of the SNP effect estimates identified chromosomal regions harbouring major genes for muscling and body weight in breeds of French origin. Based on the regression intercept and slope of the GEBVs of validation animals, the single-step evaluation was neither inflated nor deflated across the six breeds. Overall, the single-step model resulted in a more accurate and stable evaluation. However, due to the small number of genotyped individuals, the single-step method only provided slightly better results when compared to the pedigree-based method.