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Key Message: A new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Breeding animals to produce more robust and disease-resistant pig populations becomes a complementary strategy to the more conventional methods of biosecurity and vaccination. The objective of this study was to explore the ability of a panel of genetic markers and immunity parameters to predict the survival rates during a natural PRRSV outbreak. Ten-week-old female Duroc pigs (n = 129), obtained from 61 sows and 20 boars, were naturally infected with a highly pathogenic PRRSV genotype 1 strain. Prior to infection, piglets were screened for immunity parameters (IgG levels in plasma and SOX13 mRNA expression in blood) and genetic markers previously associated to PRRSV immune response and immunity traits. Additionally, the 20 boars were genotyped with a panel of 132 single nucleotide polymorphisms (SNPs). Survival analysis showed that mortality was significantly higher for animals with low basal IgG levels in plasma and/or high SOX13 mRNA expression in blood. The genotypes of sires for SNPs associated with IgG plasma levels, CRP in serum, percentage of γδ T cells, lymphocyte phagocytic capacity, total number of lymphocytes and leukocytes, and MCV and MCH were significantly associated with the number of surviving offspring. Furthermore, CD163 and GBP5 markers were also associated to piglet survival. The effects of these SNPs were polygenic and cumulative, survival decreased from 94 to 21% as more susceptible alleles were accumulated for the different markers. Our results confirmed the existence of genetic variability in survival after PRRSV infection and provided a set of genetic markers and immunity traits associated with PRRS resistance. [ABSTRACT FROM AUTHOR]

Jeju black cattle (JBC) are recognised as one of Korea's native breeds that are well-suited to the local environmental conditions and known for their excellent meat quality. This study investigated the variance components and genomic prediction accuracy for carcase traits such as carcase weight (CWT), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) in JBC using various statistical models. We employed single-step genomic best linear unbiased prediction (ssGBLUP) and single-step Bayesian regression (SSBR) models, which utilised imputed genotypes for non-genotyped individuals. Phenotypic data were collected from 2045 JBC, and 3759 JBC individuals were genotyped using the Affymetrix 160K SNP Axiom array. Estimated heritability ranged from 0.24 to 0.40 for both single-trait and multi-trait models. Multi-trait models consistently demonstrated higher genomic prediction accuracy compared to single-trait models, underscoring the value of integrating multiple traits. For instance, CWT accuracy improved from 0.24 in single-trait PBLUP (st-PBLUP) to 0.47 in multi-trait ssGBLUP. Similarly, SSBR models showed significant improvements, with single-trait SSBR Bayes B achieving 0.46 and its multi-trait version reaching 0.49. EMA accuracy improved from 0.21 in st-PBLUP to 0.39 in the multi-trait SSBR Bayes C model. BF and MS also exhibited consistent accuracy improvements with multi-trait Bayesian regression models. These results demonstrate the superior accuracy of ssGBLUP and SSBR models compared to traditional BLUP, particularly for traits with higher heritability values. This findings offer valuable insights for enhancing genomic selection strategies and improving carcase traits in JBC breeding programs, contributing to the breed's economic sustainability and meat production efficiency. [ABSTRACT FROM AUTHOR]

Background: Routine genomic-estimated breeding values (gEBVs) are computed for the Israeli dairy cattle population by a two-step methodology in combination with the much larger Dutch population. Only sire genotypes are included. This work evaluated the contribution of cow genotypes obtained from the Israeli Holstein population to enhance gEBVs predictions via single-step genomic best-linear unbiased prediction (ssGBLUP). The gEBV values of 141 bulls with daughter information and high reliabilities for 305-day lactation yield of milk, fat, and protein were compared with the bulls' predicted ssGBLUP-gEBVs using a truncated dataset omitting production data of the last five years. We investigated how these sire gEBVs were affected by varying polygenic weights in the genomic relationship matrices and by deleting old phenotypic or genotypic records. Results: The correlations of the predicted gEBVs for milk, fat and protein computed from the truncated data with the current gEBVs based also on daughter records of the last five years were 0.64, 0.57, and 0.56, respectively, for a polygenic weight of 0.5, similar to the values achieved by the current two-step methodology. The regressions of the current gEBVs on the predicted values were 0.9 for milk and 0.7 for fat and protein. Genotyping of 1.8-5 cows had the approximate statistical power of one additional bull depending on the trait. Omitting phenotype records earlier than 2000 resulted in similar gEBV values. Omitting genotypes before 1995 improved the regression coefficients. For all experiments, varying the polygenic weights over the range of 0.1 to 0.9 resulted in a trade-off between correlations and overestimation of gEBVs for young bulls. Conclusions: The model suffers from overestimation of the predicted values for young bulls. The time interval used for inclusion of genotypic and phenotypic records and adjustment of the polygenic weight can improve gEBV predictions and should be tuned to fit the tested population. For relatively small populations, genotyping of cows can significantly increase the reliability of gEBVs computed by single-step methodology. By extrapolation of our results, records of ~ 13,000 genotyped cows should provide a sufficiently large training population to obtain reliable estimates of gEBVs using ssGBLUP. [ABSTRACT FROM AUTHOR]

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

Background: The theory of "metafounders" proposes a unified framework for relationships across base populations within breeds (e.g. unknown parent groups), and base populations across breeds (crosses) together with a sensible compatibility with genomic relationships. Considering metafounders might be advantageous in pedigree best linear unbiased prediction (BLUP) or single-step genomic BLUP. Existing methods to estimate relationships across metafounders Γ are not well adapted to highly unbalanced data, genotyped individuals far from base populations, or many unknown parent groups (within breed per year of birth). Methods: We derive likelihood methods to estimate Γ . For a single metafounder, summary statistics of pedigree and genomic relationships allow deriving a cubic equation with the real root being the maximum likelihood (ML) estimate of Γ . This equation is tested with Lacaune sheep data. For several metafounders, we split the first derivative of the complete likelihood in a term related to Γ , and a second term related to Mendelian sampling variances. Approximating the first derivative by its first term results in a pseudo-EM algorithm that iteratively updates the estimate of Γ by the corresponding block of the H-matrix. The method extends to complex situations with groups defined by year of birth, modelling the increase of Γ using estimates of the rate of increase of inbreeding ( Δ F ), resulting in an expanded Γ and in a pseudo-EM+ Δ F algorithm. We compare these methods with the generalized least squares (GLS) method using simulated data: complex crosses of two breeds in equal or unsymmetrical proportions; and in two breeds, with 10 groups per year of birth within breed. We simulate genotyping in all generations or in the last ones. Results: For a single metafounder, the ML estimates of the Lacaune data corresponded to the maximum. For simulated data, when genotypes were spread across all generations, both GLS and pseudo-EM(+ Δ F ) methods were accurate. With genotypes only available in the most recent generations, the GLS method was biased, whereas the pseudo-EM(+ Δ F ) approach yielded more accurate and unbiased estimates. Conclusions: We derived ML, pseudo-EM and pseudo-EM+ Δ F methods to estimate Γ in many realistic settings. Estimates are accurate in real and simulated data and have a low computational cost. [ABSTRACT FROM AUTHOR]

The study aimed at evaluating how sires, classified for their additive genetic effects on residual feed intake (RFI) of ad libitum-fed progeny, influence growth performance, tissue accretion, and gain composition in restricted-fed offspring (96–168 kg body weight, BW). A total of 416 purebred C21 Goland pigs, offspring of 23 sires, were randomly allocated to three feeding groups: ad libitum, restricted medium-protein, or restricted low-protein. Empty BW, body lipid mass and body protein mass were estimated from individual BW and backfat measures using literature equations. Residuals of a linear regression of average daily feed intake on average empty BW, body lipid and protein daily gains were used as estimates of individual RFI in ad libitum-fed pigs. Additive genetic effects of sires on RFI of ad libitum-fed pigs were estimated with a linear animal model and solutions of the model were used to allocate the sires to low- (LRFI), medium- (MRFI), or high-RFI (HRFI) groups. Restricted-fed progeny of LRFI sires exhibited reduced average daily feed intake (-3%) compared to MRFI and HRFI progeny. This indicates that LRFI progeny make a more efficient use of energy intake and implies that variation in RFI across families, assessed under ad libitum feeding, is related to the across-family variation in feed efficiency detected under restricted feeding. LRFI progeny exhibited also a lower feed conversion ratio (-11%), partially resulting from of a 3% increase in growth rate compared with HRFI. Thus, LRFI progeny consumed less feed, while growing at a similar or slightly higher rate than MRFI or HRFI. No significant differences across sire classes were observed for daily tissue accretion and gain composition. Hence, we can hypothesise that efficient sires would not affect carcass leanness of heavy pig progeny fed restricted. [ABSTRACT FROM AUTHOR]

Background: The Dohne Merino sheep was introduced to Australia from South Africa in the 1990s. It was primarily used in crosses with the Merino breed sheep to improve on attributes such as reproduction and carcass composition. Since then, this breed has continued to expand in Australia but the number of genotyped and phenotyped purebred individuals remains low, calling into question the accuracy of genomic selection. The Australian Merino, on the other hand, has a substantial reference population in a separate genomic evaluation (MERINOSELECT). Combining these resources could fast track the impact of genomic selection on the smaller breed, but the efficacy of this needs to be investigated. This study was based on a dataset of 53,663 genotypes and more than 2 million phenotypes. Its main objectives were (1) to characterize the genetic structure of Merino and Dohne Merino breeds, (2) to investigate the utility of combining their evaluations in terms of quality of predictions, and (3) to compare several methods of genetic grouping. We used the 'LR-method' (Linear Regression) for these assessments. Results: We found very low Fst values (below 0.048) between the different Merino lines and Dohne breed considered in our study, indicating very low genetic differentiation. Principal component analysis revealed three distinct groups, identified as purebred Merino, purebred Dohne, and crossbred animals. Considering the whole population in the reference led to the best quality of predictions and the largest increase in accuracy (from 'LR-method') from pedigree to genomic-based evaluations: 0.18, 0.14 and 0.16 for yearling fibre diameter (YFD), yearling greasy fleece weight (YGFW) and yearling liveweight (YWT), respectively. Combined genomic evaluations showed higher accuracies than the evaluation based on the Dohne reference only (accuracies increased by 0.16, 0.06 and 0.07 for YFD, YGFW, and YWT, respectively). For the combined genomic evaluations, metafounder models were more accurate than Unknown Parent Groups models (accuracies increased by 0.04, 0.04 and 0.06 for YFD, YGFW and YWT, respectively). Conclusions: We found promising results for the future transition of the Dohne breed from pedigree to genomic selection. A combined genomic evaluation, with the MERINOSELECT evaluation in addition to using metafounders, is expected to enhance the quality of genomic predictions for the Dohne Merino breed. [ABSTRACT FROM AUTHOR]

Background: Single-nucleotide polymorphism (SNP) effects can be backsolved from ssGBLUP genomic estimated breeding values (GEBV) and used for genome-wide association studies (ssGWAS). However, obtaining p-values for those SNP effects relies on the inversion of dense matrices, which poses computational limitations in large genotyped populations. In this study, we present a method to approximate SNP p-values for ssGWAS with many genotyped animals. This method relies on the combination of a sparse approximation of the inverse of the genomic relationship matrix ( G A P Y - 1 ) built with the algorithm for proven and young (APY ) and an approximation of the prediction error variance of SNP effects which does not require the inversion of the left-hand side (LHS) of the mixed model equations. To test the proposed p-value computing method, we used a reduced genotyped population of 50K genotyped animals and compared the approximated SNP p-values with benchmark p-values obtained with the direct inverse of LHS built with an exact genomic relationship matrix ( G - 1 ) . Then, we applied the proposed approximation method to obtain SNP p-values for a larger genotyped population composed of 450K genotyped animals. Results: The same genomic regions on chromosomes 7 and 20 were identified across all p-value computing methods when using 50K genotyped animals. In terms of computational requirements, obtaining p-values with the proposed approximation reduced the wall-clock time by 38 times and the memory requirement by ten times compared to using the exact inversion of the LHS. When the approximation was applied to a population of 450K genotyped animals, two new significant regions on chromosomes 6 and 14 were uncovered, indicating an increase in GWAS detection power when including more genotypes in the analyses. The process of obtaining p-values with the approximation and 450K genotyped individuals took 24.5 wall-clock hours and 87.66GB of memory, which is expected to increase linearly with the addition of noncore genotyped individuals. Conclusions: With the proposed method, obtaining p-values for SNP effects in ssGWAS is computationally feasible in large genotyped populations. The computational cost of obtaining p-values in ssGWAS may no longer be a limitation in extensive populations with many genotyped animals. [ABSTRACT FROM AUTHOR]

Background: Regions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights. Results: In this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy. Conclusions: We investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights. [ABSTRACT FROM AUTHOR]

Objectives: Litter size is a crucial economic factor in the sheep industry. Several factors and genes influence litter size, making the identification of genes or loci involved a genetic challenge. Gonadotropin-inhibitory hormone (GnIH) is one of several genes that influence sheep's reproductive traits. Thus, this study aimed to investigate whether variations in the GnIH gene affect the reproductive performance of Awassi and Hamdani ewes. Methods: DNA was extracted from 99 single-progeny ewes and 101 twin ewes. The polymerase chain reaction (PCR) produced amplicons of 262 bp, 275 bp, and 284 bp from exons 1, 2, and 3 of the GnIH gene. Single-strand conformational polymorphism (SSCP) technique was used for genotyping experiments. Sequencing and in silico analysis were performed on each set of SSCP-resolved bands. Results: Two genotypes of 262 bp amplicons were found: TT and TC. Sequence analysis revealed a novel missense mutation in the TC genotype at position c.122T>C. Five in silico tools were used to assess the impact of this mutation on GnIH protein structure, function, and stability, all of them demonstrated a deleterious effect. An analysis of statistical data revealed a strong correlation between the c.122T>C single-nucleotide polymorphism (SNP) and reproductive performance. Ewes with the SNP 122T>C exhibited a significant increase in litter size, twinning rates, lambing rates, and days to lambing when compared with ewes with the TT genotype. A lower number of lambs were born to ewes with the TT genotype than those with the TC genotype. Conclusion: These results concluded that the c.122T>C SNP variant positively influences the reproductive performance of Awassi and Hamdani sheep. Sheep that carry the c.122T>C SNP show higher litter size and increased productivity. [ABSTRACT FROM AUTHOR]

Objective: Litter size and piglet loss at birth significantly impact piglet production and are closely associated with sow parity. Understanding how these traits vary across different parities is crucial for effective herd management. This study investigates the patterns of the number of born alive piglets (NBA), number of piglet losses (NPL), and the proportion of piglet losses (PPL) at birth in Landrace sows under tropical conditions. Additionally, it aims to identify the most suitable model for describing these patterns. Methods: A dataset comprising 2,322 consecutive reproductive records from 258 Landrace sows, spanning parities from 1 to 9, was analyzed. Modeling approaches including 2nd and 3rd degree polynomial models, the Wood gamma function, and a longitudinal model were applied at the individual level to predict NBA, NPL, and PPL. The choice of the best-fitting model was determined based on the lowest mean and standard deviation of the difference between predicted and actual values, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results: Sow parity significantly influenced NBA, NPL, and PPL (p<0.0001). NBA increased until the 4th parity and then declined. In contrast, NPL and PPL decreased until the 2nd parity and then steadily increased until the 8th parity. The 2nd and 3rd degree polynomials, and longitudinal models showed no significant differences in predicting NBA, NPL, and PPL (p>0.05). The 3rd degree polynomial model had the lowest prediction standard deviation and yielded the smallest AIC and BIC. Conclusion: The 3rd degree polynomial model offers the most suitable description of NBA, NPL, and PPL patterns. It holds promise for applications in genetic evaluations to enhance litter size and reduce piglet loss at birth in sows. These findings highlight the importance of accounting for sow parity effects in swine breeding programs, particularly in tropical conditions, to optimize piglet production and sow performance. [ABSTRACT FROM AUTHOR]

Background: Teat number is a vital reproductive trait in sows, crucial for providing immunity and nutrition to piglets during lactation. However, "missing heritability" in Single Nucleotide Polymorphism (SNP)-based Genome-Wide Association Studies (GWAS) has led to an increasing focus on structural variations in the genetic analysis of complex biological traits. Results: In this study, we generated a comprehensive CNV map in a population of French Yorkshire pigs (n = 644) and identified 429 CNVRs. Notably, 44% (189 CNVRs) of these were detected for the first time. Subsequently, we conducted GWAS for teat number in the French Yorkshire pig population using both 80K chip and its imputed data, as well as a GWAS analysis based on CNV regions (CNVR). Interestingly, 80K chip GWAS identified two SNPs located on Sus scrofa chromosome 5 (SSC5) that were simultaneously associated with Total Teat Number (TTN), Left Teat Number (LTN), and Right Teat Number (RTN). The leading SNP (WU_10.2_5_76130558) explained 3.33%, 2.69%, and 2.67% of the phenotypic variance for TTN, LTN, and RTN, respectively. Moreover, through imputed GWAS, we successfully identified 30 genetic variants associated with TTN located within the 73.22 -73.30 Mb region on SSC5. The two SNPs identified in the 80K chip GWAS were also located in this region. In addition, CNVR-based GWAS revealed three significant CNVRs associated with TTN. Finally, through gene annotation, we pinpointed two candidate genes, TRIM66 and PRICKLE1, which are related to diverse processes such as breast cancer and abnormal vertebral development. Conclusions: Our research provides an in-depth analysis of the complex genetic structure underlying teat number, contributing to the genetic enhancement of sows with improved reproductive performance and, ultimately, bolstering the economic benefits of swine production enterprises. [ABSTRACT FROM AUTHOR]

A four-years crossbreeding experiment was performed involving two synthetic rabbit lines of APRI (A) and Moshtohor (M) where bucks of the APRI line were mated with does of the Moshtohor line to produce F 1 crossbred (½A½M), followed by inter-se mating to obtain F 2 crossbred (½A½M) 2 . A total of 669 litters produced from 184 bucks and 394 does were used in analyzing litter size at birth (LSB) and weaning (LSW), litter weight at birth (LWB) and weaning (LWW) and pre-weaning mortality (PM). Heritability estimates for the targeted traits were low to moderate, ranging from 0.11 to 0.27. APRI line had the lowest range in predicted breeding values (PBVs) for all litter traits. The ranges in PBV for the four genetic groups were high and ranging from 0.59 to 1.0 kit for LSB, 2.4 to 3.0 kit for LSW, 26.5 to 33.9 g for LWB, 411.6 to 515.5 g for LWW and 20.9 to 24.8% for PM. The estimates of direct additive effects (G I ) and maternal effects (G M ) for litter traits were in favour of the Moshtohor line with percentages ranging from 1.2 to 16.3% for G I and 1.4 to 20.5% for G M . The estimates of direct heterosis (H I ) and maternal heterosis (H M ) were significantly positive for most litter traits with percentages ranging from 2.4 to 55.8% for H I and 1.8 to 34.6% for H M . In practice, it is recommended to use the APRI line as a sire group and Moshtohor line as a dam group in the crossbreeding program to synthesize new synthetic rabbit lines in Egypt., Competing Interests: Declarations. Human and animal rights: All experimental procedures involving animal handling and treatment were approved by the Research Ethics Committee of the Faculty of Agriculture, Benha University, Egypt (Approval No. REC-FOABU 0.3/00029). Conflict of interest: The authors declare that they have no conflict of interest., (© 2025. The Author(s).)

This study aimed to identify splicing quantitative trait loci (cis-sQTL) in Nelore cattle muscle tissue and explore the involvement of spliced genes (sGenes) in immune system-related biological processes. Genotypic data from 80 intact male Nelore cattle were obtained using SNP-Chip technology, while RNA-Seq analysis was performed to measure gene expression levels, enabling the integration of genomic and transcriptomic datasets. The normalized expression levels of spliced transcripts were associated with single nucleotide polymorphisms (SNPs) through an analysis of variance using an additive linear model with the MatrixEQTL package. A permutation analysis then assessed the significance of the best SNPs for each spliced transcript. Functional enrichment analysis was performed on the sGenes to investigate their roles in the immune system. In total, 3,187 variants were linked to 3,202 spliced transcripts, with 83 sGenes involved in immune system processes. Of these, 31 sGenes were enriched for five transcription factors. Most cis-sQTL effects were found in intronic regions, with 27 sQTL variants associated with disease susceptibility and resistance in cattle. Key sGenes identified, such as GSDMA, NLRP6, CASP6, GZMA, CASP4, CASP1, TREM2, NLRP1, and NAIP, were related to inflammasome formation and pyroptosis. Additionally, genes like PIDD1, OPTN, NFKBIB, STAT1, TNIP3, and TREM2 were involved in regulating the NF-kB pathway. These findings lay the groundwork for breeding disease-resistant cattle and enhance our understanding of genetic mechanisms in immune responses., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The genotype-by-environment interaction (GxE) in beef cattle can be investigated using reaction norm models to assess environmental sensitivity and, combined with genome-wide association studies (GWAS), to map genomic regions related to animal adaptation. Including genetic markers from whole-genome sequencing in reaction norm (RN) models allows us to identify high-resolution candidate genes across environmental gradients through GWAS. Hence, we performed a GWAS via the RN approach using whole-genome sequencing data, focusing on mapping candidate genes associated with the expression of reproductive and growth traits in Nellore cattle. For this purpose, we used phenotypic data for age at first calving (AFC), scrotal circumference (SC), post-weaning weight gain (PWG), and yearling weight (YW). A total of 20,000 males and 7,159 females genotyped with 770k were imputed to the whole sequence (29 M). After quality control and linkage disequilibrium (LD) pruning, there remained ∼ 2.41 M SNPs for SC, PWG, and YW and ∼ 5.06 M SNPs for AFC. Results: Significant SNPs were identified on Bos taurus autosomes (BTA) 10, 11, 14, 18, 19, 20, 21, 24, 25 and 27 for AFC and on BTA 4, 5 and 8 for SC. For growth traits, significant SNP markers were identified on BTA 3, 5 and 20 for YW and PWG. A total of 56 positional candidate genes were identified for AFC, 9 for SC, 3 for PWG, and 24 for YW. The significant SNPs detected for the reaction norm coefficients in Nellore cattle were found to be associated with growth, adaptative, and reproductive traits. These candidate genes are involved in biological mechanisms related to lipid metabolism, immune response, mitogen-activated protein kinase (MAPK) signaling pathway, and energy and phosphate metabolism. Conclusions: GWAS results highlighted differences in the physiological processes linked to lipid metabolism, immune response, MAPK signaling pathway, and energy and phosphate metabolism, providing insights into how different environmental conditions interact with specific genes affecting animal adaptation, productivity, and reproductive performance. The shared genomic regions between the intercept and slope are directly implicated in the regulation of growth and reproductive traits in Nellore cattle raised under different environmental conditions. [ABSTRACT FROM AUTHOR]

Background: Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results. Results: Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (−log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability. Conclusion: Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds. [ABSTRACT FROM AUTHOR]

Background: Longitudinal records of automatically-recorded vaginal temperature (TV) could be a key source of data for deriving novel indicators of climatic resilience (CR) for breeding more resilient pigs, especially during lactation when sows are at an increased risk of suffering from heat stress (HS). Therefore, we derived 15 CR indicators based on the variability in TV in lactating sows and estimated their genetic parameters. We also investigated their genetic relationship with sows' key reproductive traits. Results: The heritability estimates of the CR traits ranged from 0.000 ± 0.000 for slope for decreased rate of TV (SlopeDe) to 0.291 ± 0.047 for sum of TV values below the HS threshold (HSUB). Moderate to high genetic correlations (from 0.508 ± 0.056 to 0.998 ± 0.137) and Spearman rank correlations (from 0.431 to 1.000) between genomic estimated breeding values (GEBV) were observed for five CR indicators, i.e. HS duration (HSD), the normalized median multiplied by normalized variance (Nor_medvar), the highest TV value of each measurement day for each individual (MaxTv), and the sum of the TV values above (HSUA) and below (HSUB) the HS threshold. These five CR indicators were lowly to moderately genetically correlated with shoulder skin surface temperature (from 0.139 ± 0.008 to 0.478 ± 0.048) and respiration rate (from 0.079 ± 0.011 to 0.502 ± 0.098). The genetic correlations between these five selected CR indicators and sow reproductive performance traits ranged from − 0.733 to − 0.175 for total number of piglets born alive, from − 0.733 to − 0.175 for total number of piglets born, and from − 0.434 to − 0.169 for number of pigs weaned. The individuals with the highest GEBV (most climate-sensitive) had higher mean skin surface temperature, respiration rate (RR), panting score (PS), and hair density, but had lower mean body condition scores compared to those with the lowest GEBV (most climate-resilient). Conclusions: Most of the CR indicators evaluated are heritable with substantial additive genetic variance. Five of them, i.e. HSD, MaxTv, HSUA, HSUB, and Nor_medvar share similar underlying genetic mechanisms. In addition, individuals with higher CR indicators are more likely to exhibit better HS-related physiological responses, higher body condition scores, and improved reproductive performance under hot conditions. These findings highlight the potential benefits of genetically selecting more heat-tolerant individuals based on CR indicators. [ABSTRACT FROM AUTHOR]

Background: Limitations of the concept of identity by descent in the presence of stratification within a breeding population may lead to an incomplete formulation of the conventional numerator relationship matrix (A ). Combining A with the genomic relationship matrix (G ) in a single-step approach for genetic evaluation may cause inconsistencies that can be a source of bias in the resulting predictions. The objective of this study was to identify stratification using genomic data and to transfer this information to matrix A , to improve the compatibility of A and G . Methods: Using software to detect population stratification (ADMIXTURE), we developed an iterative approach. First, we identified 2 to 40 strata (k ) with ADMIXTURE, which we then introduced in a stepwise manner into matrix A , to generate matrix A Γ using the metafounder methodology. Improvements in consistency between matrix G and A Γ were evaluated by regression analysis and through the comparison of the overall mean and mean diagonal values of both matrices. The approach was tested on genotype and pedigree information of European and North American Brown Swiss animals (85,249). Analyses with ADMIXTURE were initially performed on the full set of genotypes (S1). In addition, we used an alternative dataset where we avoided sampling of closely related animals (S2). Results: Results of the regression analyses of standard A on G were – 0.489, 0.780 and 0.647 for intercept, slope and fit of the regression. When analysing S1 data results of the regression for A Γ on G corresponding values were – 0.028, 1.087 and 0.807 for k =7, while there was no clear optimum k . Analyses of S2 gave a clear optimal k =24, with − 0.020, 0.998 and 0.817 as results of the regression. For this k differences in mean and mean diagonal values between both matrices were negligible. Conclusions: The derivation of hidden stratification information based on genotyped animals and its integration into A improved compatibility of the resulting A Γ and G considerably compared to the initial situation. In dairy breeding populations with large half-sib families as sub-structures it is necessary to balance the data when applying population structure analysis to obtain meaningful results. [ABSTRACT FROM AUTHOR]

Background: Livestock populations are under constant selective pressure for higher productivity levels for different selective purposes. This pressure results in the selection of animals with unique adaptive and production traits. The study of genomic regions associated with these unique characteristics has the potential to improve biological knowledge regarding the adaptive process and how it is connected to production levels and resilience, which is the ability of an animal to adapt to stress or an imbalance in homeostasis. Sheep is a species that has been subjected to several natural and artificial selective pressures during its history, resulting in a highly specialized species for production and adaptation to challenging environments. Here, the data from multiple studies that aim at mapping selective sweeps across the sheep genome associated with production and adaptation traits were integrated to identify confirmed selective sweeps (CSS). Results: In total, 37 studies were used to identify 518 CSS across the sheep genome, which were classified as production (147 prodCSS) and adaptation (219 adapCSS) CSS based on the frequency of each type of associated study. The genes within the CSS were associated with relevant biological processes for adaptation and production. For example, for adapCSS, the associated genes were related to the control of seasonality, circadian rhythm, and thermoregulation. On the other hand, genes associated with prodCSS were related to the control of feeding behaviour, reproduction, and cellular differentiation. In addition, genes harbouring both prodCSS and adapCSS showed an interesting association with lipid metabolism, suggesting a potential role of this process in the regulation of pleiotropic effects between these classes of traits. Conclusions: The findings of this study contribute to a deeper understanding of the genetic link between productivity and adaptability in sheep breeds. This information may provide insights into the genetic mechanisms that underlie undesirable genetic correlations between these two groups of traits and pave the way for a better understanding of resilience as a positive ability to respond to environmental stressors, where the negative effects on production level are minimized. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection., (© 2022. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.)

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance. [ABSTRACT FROM AUTHOR]

Context: Gir is a tropically adapted breed of Bos taurus indicus cattle that has been intensively selected for milk production in Brazil. Selection programs also consider body-conformation traits because these have been associated with health, welfare, and cow longevity, thus affecting productivity. Aims: This study focused on Gir cattle to estimate genetic and phenotypic parameters for body-conformation traits, as well as their correlations with milk production traits. Methods: Body-conformation traits were classified as 'frame', 'foot and leg', or 'mammary system' traits. The dataset contained 1105 cows recorded with body-conformation traits and 38 996 records of 305-day cumulative milk yield, from 24 489 Gir cows. The pedigree file included 38 571 animals, of which 1593 were genotyped using six different single-nucleotide polymorphism panels. After genotyping imputation, and quality control, 42 105 polymorphisms were available for analyses. Univariate and bivariate animal models, using Bayesian approach via the Gibbs sampling algorithm, were used to estimate genetic parameters, benefiting from both genotypes and pedigree data. Key results: The heritabilities for body-conformation traits ranged from a low of 0.08 ± 0.04 to a medium value of 0.35 ± 0.07, and the heritability for milk production was 0.23 ± 0.01. We identified unfavourable genetic correlations between milk yield and udder depth (0.63 ± 0.11) as well as body height (0.31 ± 0.13). In contrast, there was a favourable genetic correlation between milk yield and rear udder width (0.61 ± 0.14). Conclusion: The heritability estimates confirmed the possibility of selecting animals for milk yield and body conformation traits related to the frame and mammary system categories. Based on the estimated genetic correlations, considerable importance should be given to the emphasis on selection for increased milk yield, because in the long term, this may promote undesirable changes in some type traits as body height and udder depth. Implications: This information is relevant to selective breeding of Gir to enhance tropical milk production. Body-conformation traits in dairy cattle have been associated with health, welfare, and cow longevity, thus affecting herd productivity. The heritability estimates of this study indicated that body-conformations traits could respond to selection. Moreover, the correlations between these traits and milk yield highlighted the importance of considering the correlated response to selection, synergies, and trade-offs between body conformation traits and milk production in Gir cattle. This information is relevant for selective breeding in Gir cattle to enhance tropical milk production. [ABSTRACT FROM AUTHOR]

Motivation Utilizing both purebred and crossbred data in animal genetics is widely recognized as an optimal strategy for enhancing the predictive accuracy of breeding values. Practically, the different genetic background among several purebred populations and their crossbred offspring populations limits the application of traditional prediction methods. Several studies endeavor to predict the crossbred performance via the partial relationship, which divides the data into distinct sub-populations based on the common genetic background, such as one single purebred population and its corresponding crossbred descendant. However, this strategy makes prediction inaccurate due to ignoring half of the parental information of crossbreed animals. Furthermore, dominance effects, although playing a significant role in crossbreeding systems, cannot be modeled under such a prediction model. Results To overcome this weakness, we developed a novel multi-breed single-step model using metafounders to assess ancestral relationships across diverse breeds under a unified framework. We proposed to use multi-breed dominance combined relationship matrices to model additive and dominance effects simultaneously. Our method provides a straightforward way to evaluate the heterosis of crossbreeds and the breeding values of purebred parents efficiently and accurately. We performed simulation and real data analyses to verify the potential of our proposed method. Our proposed model improved prediction accuracy under all scenarios considered compared to commonly used methods. Availability and implementation The software for implementing our method is available at https://github.com/CAU-TeamLiuJF/MAGE. [ABSTRACT FROM AUTHOR]

Genotype-environment interaction is pervasive in forest genetics. Delineation of spatial breeding zones (BZs) is fundamental for accommodating genotype-environment interaction. Here we developed a BZ classification pipeline for the forest tree Eucalyptus globulus in 2 Australian regions based on phenotypic, genomic, and pedigree data, as well on a detailed environmental characterization ("envirotyping") and spatial mapping of BZs. First, the factor analytic method was used to model additive genetic variance and site–site genetic correlations (rB) in stem volume across 48 trials of 126,467 full-sib progeny from 2 separate breeding programs. Thirty-three trials were envirotyped using 145 environmental variables (EVs), involving soil and landscape (71), climate (73), and management (1) EVs. Next, sparse partial least squares-discriminant analysis was used to identify EVs that were required to predict classification of sites into 5 non-exclusive BZ classes based on rB. Finally, these BZs were spatially mapped across the West Australian and "Green Triangle" commercial estates by enviromic prediction using EVs for 80 locations and 15 sets of observed climate data to represent temporal variation. The factor analytic model explained 85.9% of estimated additive variance. Our environmental classification system produced within-zone mean rB between 0.76 and 0.84, which improves upon the existing values of 0.62 for Western Australia and 0.67 for Green Triangle as regional BZs. The delineation of 5 BZ classes provides a powerful framework for increasing genetic gain by matching genotypes to current and predicted future environments. [ABSTRACT FROM AUTHOR]

Background: There can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data. Results: Using threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory. Conclusions: Slope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation. [ABSTRACT FROM AUTHOR]

Context. The knowledge of the genetic variability of gestation length and its correlations with other traits is relevant for selection decisions in beef cattle. Aims. Genetic parameters and trends were estimated for gestation length (first (GL1), repeated measures range of 1--6 (GL)) and traits currently used as selection criteria (birth weight (BW), weight gain from birth to weaning (BWG), weight gain from weaning to yearling (WYG), scrotal circumference (SC), score of conformation (YC), score of precocity (YP), and score of muscling (YM)) in Brazilian Angus cattle. In addition, direct and correlated selection responses were predicted. Methods. A series of single- and two-trait Bayesian analyses were performed on beef cattle data from14 farms across different regions of Brazil, by using linear and threshold animal models. Key results. A moderate heritability was estimated for GL1 (0.356 ± 0.058); however, the estimate for GL was low (0.189 ± 0.028) as was that for repeatability (0.204 ± 0.027). Lower accuracies of breeding values were obtained for GL than for GL1, ranging from0.490 ± 0.161 to 0.687 ± 0.037. Direct heritability estimates for growth and body composition traits were low, except for SC and BWG (0.301 ± 0.060 and 0.241 ± 0.019 respectively). Genetic correlations of 0.558 ± 0.121 and 0.739 ± 0.131 were estimated between GL1 and BW, and between GL and BW respectively. The other traits studied were estimated to have weak or nearzero genetic correlations with gestation length traits. Positive and moderate genetic associations were estimated between BW and BWG, SC, and visual scores (ranging from 0.338 ± 0.095 to 0.458 ± 0.092), but a near-zero estimate was obtained with WYG (0.127 ± 0.150). Weight gains were more highly genetically correlated with visual scores than between themselves (BWG and WYG) and with SC. The SC was highly genetically correlated with visual scores (0.684 ± 0.054 to 0.714 ± 0.050). Genetic trends showed that all traits studied have been changing in a positive direction, being unfavourable for BW and GL. Higher genetic gains were expected from using direct selection than from using indirect selection for GL1. Conclusions. Rapid growth is genetically associated with superior conformation, precocity and musculature, as well as a large scrotal circumference in males. However, selection decisions that heavily focus on these traits are likely to lead to more calving difficulties in the longer term. Implications. Selection indices should incorporate traits that measure calving difficulty directly if relevant records are available. Currently, considering its practical implications in primiparous anoestrus, the first-gestation length could serve as a viable alternative for inclusion in selection indices. Due to the low repeatability of the gestation length, decisions to cull on the basis of only one or two records of GL are highly inaccurate. [ABSTRACT FROM AUTHOR]

This study evaluated the risk of using genomically-tested, young Holstein sires (GYS) to produce progeny with a low Nippon Total Profit index (NTP). The risk was defined as the probability that the progeny had a true NTP lower than the dam's estimated NTP. The service sires available as of August 2023 in Japan were divided into three classes : daughter-proven sires with 60 or more daughters with milk records (BULL-OLD), daughter-proven sires with fewer than 60 recorded daughters (BULL-NEW), and GYS with no daughters (BULL-GYS). Two classes of dams were considered : cows born in 2020 (COW-2020) and genomically-tested heifers born in 2022 (HEI-2022). For each class, we assumed a hypothetical animal with the average published NTP. We then used these animals as parents for mating. The true NTP of progeny was assumed to follow a normal distribution, with the mean as the parent average of estimated NTP and the variance as the sum of the prediction error variance of the parent average of estimated NTP and the Mendelian sampling variance. For COW-2020 (or HEI-2022), the risk for BULL-OLD was 14.4% (23.2%), for BULL-NEW, 10.2% (17.1%), and for BULL-GYS, 2.1% (4.1%). The result suggests that the genomically-tested young sires are more effective than daughter-proven sires in improving the average of the true NTP in dairy herds. [ABSTRACT FROM AUTHOR]

The evolution of body weight under the natural trypanosome challenge and its association with disease tolerance to trypanosomosis is of utmost economic importance in cattle. This study estimated heritability for growth traits and packed cell volume (PCV) and their genetic correlations in the N'Dama cattle in the Gambia. A total of 2,488, 2,442, 1,471, 1,934, and 1,452 bodyweight records at 12 months (WT12), 16 months (WT16), 18 months (WT18), 24 months (WT24), 36 months (WT36) and 50 months (WT50) and 1,782, 1,800, 1,844, 1,608, and 1,459 records for PCV at 12 months (PCV12) 18 months (PCV18), 24 months (PCV24), 36 months (PCV36), and 50 months (PCV50), respectively, were analysed. Genetic parameters were estimated using univariate and multivariate animal models using the GIBBSF90 software. Least square means for WT12, WT18, WT24, WT36 and WT50 were 75.08 ± 0.37 kg, 95.58 ± 0.52, 123.80 ± 0.52 kg, 149.90 ± 0.73 kg and 190.37 ± 0.68 kg, respectively. The Least square means for PCV declined from 24.56 ± 0.18 at 12 months of age to 23.18 ± 0.19 at 50 months. Heritability estimates for growth ranged from 0.47 ± 0.05 at 12 months to 0.31 ± 0.06 at 50 months, while for PCV the estimates were 0.09 ± 0.01 to 0.15 ± 0.01. Genetic correlations between bodyweights were high and positive (0.73 ± 0.01 to 0.96 ± 0.01) while those between PCV traits were low to medium and positive (0.16 ± 0.04 to 0.56 ± 0.02). Genetic correlations between bodyweight and PCV range from -0.14 ± 0.02 to 0.59 ± 0.02. BW18 and PCV18 had high heritability estimates and as well as the highest genetic correlation and therefore could be used as selection criteria for body weight and trypanotolerance, respectively., Competing Interests: Declarations. Statement of animal rights: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Egerton University (dated 20th February 2024 / No: EUISERC/APP/305/2024). The authors expressly declare that no live animals were involved in the study. Competing interests: The authors have no relevant financial or non-financial interests to disclose., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Objective: To map the disease locus for familial ocular melanosis (OM) in the Cairn Terrier., Animals Studied: Cairn Terriers with OM and normal control dogs., Procedure: A genome-wide association study (GWAS) was performed using 63 OM-affected and 31 control Cairn Terriers, followed by haplotype analysis. A significantly associated single-nucleotide polymorphism was genotyped in a larger group of OM-affected and control Cairn Terriers. The coding and splice-site regions of genes mapping within the confidence interval were sequenced., Results: A ~9.2 Mb region of chromosome 11 was significantly associated with OM. Haplotype analysis narrowed the region to 1.49 Mb. Genotyping of a SNP within the region showed 86% of OM-affected dogs were homozygous or heterozygous for the risk allele, whereas 78% of unaffected dogs were homozygous for the nonrisk allele. Sequencing of the coding regions and splice sites of four genes (c9orf72, IFNK, the 5' end of MOB3B, and the 3' end of LINGO2) and of a microRNA (MIR876) did not detect any genetic variants unique to OM-affected dogs., Conclusion: OM in Cairn Terriers maps to a 1.49 Mb region of chromosome 11. This accounts for 86% of OM cases in our DNA bank. A second locus may account for the OM phenotype in the remaining 14% of cases. Sequencing of coding regions and splice sites of positional candidate genes and a microRNA did not reveal any genetic variants unique to affected dogs. Further studies are required to elucidate the DNA variant causal for OM in Cairn Terriers and to understand the disease mechanism., (© 2024 The Author(s). Veterinary Ophthalmology published by Wiley Periodicals LLC on behalf of American College of Veterinary Ophthalmologists.)

A simple crossbreeding experiment between Gabali (G) bucks and Hyplus (H) does was performed to estimate crossbreeding effects for post-weaning growth traits (body weight at 5 (BW5), 7 (BW7), 9 (BW9), 11 (BW11), and 13 (BW13) weeks of age and daily weight gains (DG) during (DG5-7), (DG7-9), (DG9-11), (DG11-13) and (DG5-13), weeks of age). Estimates of heritability were mostly low or moderate for growth traits and ranged from 0.12 to 0.35 for body weights and 0.05 to 0.66 for daily weight gains. The percentages of direct additive genetic effects on body weights favored (p ≤ 0.01) the Gabali breed by 22.1, 31.2, 32.8, 31.1, and 25.2% at 5, 7, 9, 11, and 13 weeks and by 35.4, 38.1, 21.9, 3.7, and 27% for DG5-7, DG7-9, DG9-11, DG11-13, and DG5-13, respectively. The percentages of maternal effects ranged from 22 to 34.8% for body weights and ranged from 4.7 to 43.4% for daily weight gains. The percentages of direct heterosis were significantly positive by 11, 19.1, 20.3, 19.7, and 16.5% for body weights at 5, 7, 9, 11, and 13 weeks and by 25.6, 27.2, 17.3, 3.5, and 17.9% for DG5-7, DG7-9, DG9-11, DG11-13, and DG5-13, respectively. These results suggest that the Gabali breed could be used as a sire-group and the Hyplus line could be used as a dam-group to create new high growth rabbit lines in Egypt. [ABSTRACT FROM AUTHOR]

Background: Automatic and continuous recording of vaginal temperature (TV) using wearable sensors causes minimal disruptions to animal behavior and can generate data that enable the evaluation of temporal body temperature variation under heat stress (HS) conditions. However, the genetic basis of TV in lactating sows from a longitudinal perspective is still unknown. The objectives of this study were to define statistical models and estimate genetic parameters for TV in lactating sows using random regression models, and identify genomic regions and candidate genes associated with HS indicators derived from automatically-recorded TV. Results: Heritability estimates for TV ranged from 0.14 to 0.20 over time (throughout the day and measurement period) and from 0.09 to 0.18 along environmental gradients (EG, − 3.5 to 2.2, which correspond to dew point values from 14.87 to 28.19 ˚C). Repeatability estimates of TV over time and along EG ranged from 0.57 to 0.66 and from 0.54 to 0.77, respectively. TV measured from 12h00 to 16h00 had moderately high estimates of heritability (0.20) and repeatability (0.64), indicating that this period might be the most suitable for recording TV for genetic selection purposes. Significant genotype-by-environment interactions (GxE) were observed and the moderately high estimates of genetic correlations between pairs of extreme EG indicate potential re-ranking of selection candidates across EG. Two important genomic regions on chromosomes 10 (59.370–59.998 Mb) and16 (21.548–21.966 Mb) were identified. These regions harbor the genes CDC123, CAMK1d, SEC61A2, and NUDT5 that are associated with immunity, protein transport, and energy metabolism. Across the four time-periods, respectively 12, 13, 16, and 10 associated genomic regions across 14 chromosomes were identified for TV. For the three EG classes, respectively 18, 15, and 14 associated genomic windows were identified for TV, respectively. Each time-period and EG class had uniquely enriched genes with identified specific biological functions, including regulation of the nervous system, metabolism and hormone production. Conclusions: TV is a heritable trait with substantial additive genetic variation and represents a promising indicator trait to select pigs for improved heat tolerance. Moderate GxE for TV exist, indicating potential re-ranking of selection candidates across EG. TV is a highly polygenic trait regulated by a complex interplay of physiological, cellular and behavioral mechanisms. [ABSTRACT FROM AUTHOR]

The blood sample from 60 Damani does were collected and genomic DNA was extracted, and DNA integrity were investigated. A 447 bp promoter fragment of the GDF9 gene was amplified and Sanger sequenced for the identification of GDF9 gene polymorphism. Three novel SNPs were identified at positions g. 97(T > A), g. 142 (G > G) and g. 313(C > T) in the promoter region of the caprine GDF9 gene which significantly (P < 0.05) influenced litter size, body measurement, and milk production traits in Damani goats. The genotype CT of SNP1 significantly (P < 0.05) improved litter size, genotype GG of SNP2 significantly (P < 0.05) enhanced milk production, while the genotypes CC of SNP3 significant (P < 0.05) increased body measurement traits in Damani goats. Moreover, in SNP1 loss of 3 transcription factors (TF) binding sites occurred, SNP2 caused loss of two TFs binding sites, and SNP3 caused loss of a single TF binding site. Similarly, SNP1 and SNP2 caused the gain of three new potential TF binding sites, and SNP3 caused gain of two new TF binding sites. It is concluded that caprine GDF9 gene could be used as a candidate gene for litter size, milk production and body measurement traits in Damani goats through marker-assisted selection for future breeding program. [ABSTRACT FROM AUTHOR]

Individual methane emissions are a potential breeding goal for selection plans aimed at improving the sustainability of ruminant farming systems. However, a large-scale recording of individual methane emissions is hampered by the high costs of the equipment and the logistics of the experiments. Equations have been developed to predict methane emissions in dairy cattle based on variables such as dry matter intake (DMI), gross energy intake, body weight and milk fatty acids (FAs) profile. No equations are currently available for dairy sheep. The aims of this study were: (i) to estimate the enteric methane yield (eMY, expressed as g/kg of DMI) and enteric methane intensity (eMI, expressed as g/kg of FPCM) of dairy sheep using equations developed for dairy cattle that consider as predictors milk FA; (ii) to evaluate the effect of stage of lactation, parity and month of lambing on eMY and eMI; (iii) to estimate the heritability of eMY and eMI. For this purpose, 964 individual milk samples from Sarda dairy ewes were analysed for milk composition and FA profile. Nine different equations were used to predict eMY and eMI. Values of eMY ranged from 19.4 g/kg to 20.4 g/kg of DMI, whereas values of eMI ranged from 15.1 g/kg to 21.0 g/kg of DMI, respectively. Stage of lactation was an important factor in the variability of eMY (p <.01). Indeed, this trait increased towards the end of lactation. Both eMY and eMI were negatively correlated with milk yield, lactose concentration and urea and positively correlated with fat concentration and NaCl, respectively. Heritability of eMI and eMY estimated using a single trait model was 0.13 ± 0.05 and 0.05 ± 0.04, respectively. The values of heritability estimated for eMY using a bivariate models ranged from 0.10 to 0.16 and they were higher than the value estimated for eMI. Results of the present study, even if based on prediction equations developed for cows, increased the knowledge about the phenotypic and genetic background of methane emissions in Sarda dairy sheep. Methane emissions were predicted from fatty acids using equation developed for cows. Estimates were in agreement with the values reported in literature. Methane emissions were lowly heritable. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate genetic parameters and investigate the genomic background of scrotal circumference and semen parameters in the Italian Simmental bulls. Scrotal circumference, number of normal spermatozoa, ejaculate volume, spermatozoa motility, and total spermatozoa were measured on 622 bulls, of which 603 had genotypes for 42,141 SNP. Variance components of scrotal circumference were estimated with an animal model that included the fixed effects of birth year, animal age, and measurement method, and the random effects of day of measurement and animal. In the model for the other traits, the scrotal circumference was added as a covariate to account for its influence on the semen parameters. A genome-wide association study was carried out using the ssGBLUP-approach. Heritabilities ranged from 0.07 ± 0.05 (spermatozoa motility) to 0.50 ± 0.14 (scrotal circumference). A total of 13 SNP passed the Bonferroni correction threshold and the number of significantly associated markers ranged from 1 (ejaculate volume and spermatozoa motility) to 5 (total spermatozoa). Genes already associated with reproduction parameters were retrieved close to the significant SNP. Results of the present study gave preliminary insights about the genetic determinism of semen quality in Italian Simmental bulls. Low to moderate heritabilities were estimated for semen traits. Few markers were associated with the phenotypes, suggesting their polygenic determinism. Candidate genes already associated in literature with sperm traits were found. [ABSTRACT FROM AUTHOR]

Key message: Simultaneous improvement for GY and GPC by using GWAS and GBLUP suggested a significant application in durum wheat breeding. Despite the importance of grain protein concentration (GPC) in determining wheat quality, its negative correlation with grain yield (GY) is still one of the major challenges for breeders. Here, a durum wheat panel of 200 genotypes was evaluated for GY, GPC, and their derived indices (GPD and GYD), under eight different agronomic conditions. The plant material was genotyped with the Illumina 25 k iSelect array, and a genome-wide association study was performed. Two statistical models revealed dozens of marker-trait associations (MTAs), each explaining up to 30%. phenotypic variance. Two markers on chromosomes 2A and 6B were consistently identified by both models and were found to be significantly associated with GY and GPC. MTAs identified for phenological traits co-mapped to well-known genes (i.e., Ppd-1, Vrn-1). The significance values (p-values) that measure the strength of the association of each single nucleotide polymorphism marker with the target traits were used to perform genomic prediction by using a weighted genomic best linear unbiased prediction model. The trained models were ultimately used to predict the agronomic performances of an independent durum wheat panel, confirming the utility of genomic prediction, although environmental conditions and genetic backgrounds may still be a challenge to overcome. The results generated through our study confirmed the utility of GPD and GYD to mitigate the inverse GY and GPC relationship in wheat, provided novel markers for marker-assisted selection and opened new ways to develop cultivars through genomic prediction approaches. [ABSTRACT FROM AUTHOR]

In the present study, sequencing of TORC1 prompter region explored three SNPs at loci g.80G>T, g.93A>T, and g.1253G>A. The SNP1 produced GG, GT and TT, SNP2 AA, AT and TT, and SNP3 produced GG, GA and AA genotypes. Allelic and genotypic frequencies analysis exhibited that SNP1 is within Hardy-Weinberg equilibrium (HWE). All three SNPs were found highly polymorphic as PIC value (0.25 < PIC < 0.50). At loci g.80G>T the cattle with genotype GG showed significantly (P <0.01) larger body length (BL), Wither height (WH), Hip height (HH), Rump length (RL), Hip width (HW), Chest depth (CD), and Chest circumference (CC). The genotype AA at g.93A>T showed significantly (P< 0.01 and 0.05) Larger body length (BL), Wither height (WH), Hip height, Rump length (RL), Hip width (HW), Chest depth (CD), and Chest circumference (CC). Interestingly, the carcass quality parameters such as Ultrasound loin area (ULA) and Intramuscular fat percentage (IF%) was highest in genotype GG at loci g.1253G>A. These findings conclude that genotype GG at loci g.80 G>T and AA at loci g.93A>T could be used as genetic markers for body measurement and genotype GG at loci g.1253G>A for carcass quality traits of TORC1 gene in Qinchuan beef cattle. [ABSTRACT FROM AUTHOR]

The profitability of the beef cattle production system relies heavily on reproductive traits. Unfortunately, certain traits, such as age at first calving (AFC), calving interval (CI), and gestation length (GL), can pose challenges in traditional breeding programs because of their low heritability (0.01–0.12) and sex-limited characteristics. Another important aspect is the conservation of the genetic resources of animals adapted to the Colombian regions, which implies the preservation and rational use of the creole breeds in the country market. Therefore, this study aimed to identify genomic regions in the creole cattle breed Blanco Orejinegro (BON) that influence the reproductive traits in females. The dataset comprised 439 animals and 118,116 single-nucleotide polymorphisms’ (SNPs) markers. The GS3 program was used to identify the SNP effects employing the BAYES Cπ methodology. The number of SNPs with effect for AFC was 25, 1527 for CI, and 23 for GL. Some of the genes found associated with reproductive and growth traits as well as immune response and environmental adaptation ECE1, EPH, EPHB2, SMARCAL1, IGFBP5, IGFBP2, FCGRT, EGFR, MUL1, PINK1, STPG1, CNGB1, TGFB1, OXTR, IL22RA1, MYOM3, OXTR, CNR2, HIVEP3, CTPS1, CXCL8, FCGRT, MREG, TMEM169, PECR, and MC1R. Our results evidenced a high contribution of the genetic architecture of the Colombian creole cattle breed Blanco Orejinegro that may impact should be included in implementing genetic improvement and conservation programs. [ABSTRACT FROM AUTHOR]

The domestication of animals has rendered horns less necessary for survival. Moreover, the use of polled and disbudded animals is interesting in order to avoid injuries of animals and handlers, among other advantages. We therefore conducted a comparative economic analysis of different traditional disbudding techniques versus selective breeding for polledness in Nelore cattle, the main beef breed of tropical systems in Brazil. The cost to obtain animals without horns was estimated in three different scenarios: disbudding with hot iron, disbudding with caustic paste, and phenotypic selection for polled animals. Price quotations of the materials were obtained in different states of the country and averaged. An initial frequency of horned animals of 92.16% was obtained based on the records of the Brazilian Association of Zebu Breeders. Selective breeding was found to be the best cost-effective scenario. This result differs from intensive production systems of dairy cattle in which traditional disbudding continues to be the best cost-effective scenario. The main explanation is the lack of difference in the price of Nelore semen from polled and horned bulls. Phenotypic selection for polled animals is the best cost-effective method, and it is in accordance with welfare practices. Care should be taken regarding the intensive use of few polled breeding animals in order to avoid inbreeding depression in other traits. [ABSTRACT FROM AUTHOR]

Background: The single-step model is becoming increasingly popular for national genetic evaluations of dairy cattle due to the benefits that it offers such as joint breeding value estimation for genotyped and ungenotyped animals. However, the complexity of the model due to a large number of correlated effects can lead to significant computational challenges, especially in terms of accuracy and efficiency of the preconditioned conjugate gradient method used for the estimation. The aim of this study was to investigate the effect of pedigree depth on the model's overall convergence rate as well as on the convergence of different components of the model, in the context of the single-step single nucleotide polymorphism best linear unbiased prediction (SNP-BLUP) model. Results: The results demonstrate that the dataset with a truncated pedigree converged twice as fast as the full dataset. Still, both datasets showed very high Pearson correlations between predicted breeding values. In addition, by comparing the top 50 bulls between the two datasets we found a high correlation between their rankings. We also analysed the specific convergence patterns underlying different animal groups and model effects, which revealed heterogeneity in convergence behaviour. Effects of SNPs converged the fastest while those of genetic groups converged the slowest, which reflects the difference in information content available in the dataset for those effects. Pre-selection criteria for the SNP set based on minor allele frequency had no impact on either the rate or pattern of their convergence. Among different groups of individuals, genotyped animals with phenotype data converged the fastest, while non-genotyped animals without own records required the largest number of iterations. Conclusions: We conclude that pedigree structure markedly impacts the convergence rate of the optimisation which is more efficient for the truncated than for the full dataset. [ABSTRACT FROM AUTHOR]

Background: In beef cattle, more than 50% of the energy input to produce a unit of beef is consumed by the female that produced the calf. Development of genomic tools to identify females with high genetic merit for reproductive function could increase the profitability and sustainability of beef production. Results: Genome-wide association studies (GWAS) were performed using a single-step genomic best linear unbiased prediction approach on pregnancy outcome traits from a population of Angus–Brahman crossbred heifers. Furthermore, a validation GWAS was performed using data from another farm. Heifers were genotyped with the Bovine GGP F250 array that contains 221,077 SNPs. In the discovery population, heifers were bred in winter breeding seasons involving a single round of timed artificial insemination (AI) followed by natural mating for 3 months. Two phenotypes were analyzed: pregnancy outcome to first-service AI (PAI; n = 1,481) and pregnancy status at the end of the breeding season (PEBS; n = 1,725). The heritability was estimated as 0.149 and 0.122 for PAI and PEBS, respectively. In the PAI model, one quantitative trait locus (QTL), located between 52.3 and 52.5 Mb on BTA7, explained about 3% of the genetic variation, in a region containing a cluster of γ-protocadherin genes and SLC25A2. Other QTLs explaining between 0.5% and 1% of the genetic variation were found on BTA12 and 25. In the PEBS model, a large QTL on BTA7 was synonymous with the QTL for PAI, with minor QTLs located on BTA5, 9, 10, 11, 19, and 20. The validation population for pregnancy status at the end of the breeding season were Angus–Brahman crossbred heifers bred by natural mating. In concordance with the discovery population, the large QTL on BTA7 and QTLs on BTA10 and 12 were identified. Conclusions: In summary, QTLs and candidate SNPs identified were associated with pregnancy outcomes in beef heifers, including a large QTL associated with a group of protocadherin genes. Confirmation of these associations with larger populations could lead to the development of genomic predictions of reproductive function in beef cattle. Moreover, additional research is warranted to study the function of candidate genes associated with QTLs. [ABSTRACT FROM AUTHOR]

Background: Hoof structure and health are essential for the welfare and productivity of beef cattle. Therefore, we assessed the genetic and genomic background of foot score traits in American (US) and Australian (AU) Angus cattle and investigated the feasibility of performing genomic evaluations combining data for foot score traits recorded in US and AU Angus cattle. The traits evaluated were foot angle (FA) and claw set (CS). In total, 109,294 and ~ 1.12 million animals had phenotypic and genomic information, respectively. Four sets of analyses were performed: (1) genomic connectedness between US and AU Angus cattle populations and population structure, (2) estimation of genetic parameters, (3) single-step genomic prediction of breeding values, and (4) single-step genome-wide association studies for FA and CS. Results: There was no clear genetic differentiation between US and AU Angus populations. Similar heritability estimates (FA: 0.22–0.24 and CS: 0.22–0.27) and moderate-to-high genetic correlations between US and AU foot scores (FA: 0.61 and CS: 0.76) were obtained. A joint-genomic prediction using data from both populations outperformed within-country genomic evaluations. A genomic prediction model considering US and AU datasets as a single population performed similarly to the scenario accounting for genotype-by-environment interactions (i.e., multiple-trait model considering US and AU records as different traits), even though the genetic correlations between countries were lower than 0.80. Common significant genomic regions were observed between US and AU for FA and CS. Significant single nucleotide polymorphisms were identified on the Bos taurus (BTA) chromosomes BTA1, BTA5, BTA11, BTA13, BTA19, BTA20, and BTA23. The candidate genes identified were primarily from growth factor gene families, including FGF12 and GDF5, which were previously associated with bone structure and repair. Conclusions: This study presents comprehensive population structure and genetic and genomic analyses of foot scores in US and AU Angus cattle populations, which are essential for optimizing the implementation of genomic selection for improved foot scores in Angus cattle breeding programs. We have also identified candidate genes associated with foot scores in the largest Angus cattle populations in the world and made recommendations for genomic evaluations for improved foot score traits in the US and AU. [ABSTRACT FROM AUTHOR]

The fibroblast growth factor 10 (FGF10) gene regulates adipogenesis and myogensis. In this study, sequencing of FGF10 prompter region identified three SNPs at loci g.78G > A, g.116C > T and g.201A > T. Each SNP yields three genotypes as GG, GA and AA at loci g.78G > A, CC, CT and TT at loci g.116C > T and AA, AT and TT at loci g.201A > T. Allelic and genotypic frequencies of all three SNPs deviated from the Hardy–Weinberg equilibrium (HWE) (P < 0.05) and were found highly polymorphic as PIC (0.25 < PIC < 0.50). Moreover, we found highest LD (D′/γ2) between SNP2 and SNP3 (0.989/0.909), followed by SNP1 and SNP3 (0.944/0.796). Moreover, three variants of FGF10 gene promoter exhibited significant (P < 0.05) association with body measurement and carcass quality traits in Qinchuan beef cattle. At loci g.78G > A, the genotype GG showed significantly (P < 0.01) larger body length (BL), rump length (RL), chest depth (CD), chest circumference (CC) and ultrasound loin area (ULA). The genotype TC at loci g.116C > T showed significantly (P < 0.01 and 0.05) larger body measurement and intramuscular fat, and ultrasound loin area (ULA). In addition to that, at loci g.201A > T, genotype TT showed significantly (P < 0.01 and P < 0.05) larger body length (BL), rump length (RL), hip width (HW), chest circumference (CC) and ultrasound loin area (ULA). Additionally, screening of promoter sequence of FGF10 gene explored loss of four TFs binding sites (KLF3, ZNF37α, GLIS2 and BCL11A) at g.116C > T because of SNP2. However, a single TF binding site was lost at g.202A > T due to SNP3. Interestingly, none of TF binding site was lost at g.78G > A in SNP1; however, one new TF binding site was gained at this location due to SNP1. These findings conclude that genotype GG, TC and TT could be used as genetic markers of FGF10 gene for body measurement and carcass quality traits in Qinchuan beef cattle. [ABSTRACT FROM AUTHOR]

The paper deals with the genomic evaluation of retained placenta (RP), metritis/endometritis (MET), and cystic ovarian disease (CYS) and their involvement in the selection programme of Holstein cattle. A procedure for the estimation of genetic parameters and genomic breeding values was proposed. The dataset included farmers' records of 127 000 lactations from 109 farms for RP, 185 000 lactations from 131 farms for MET and 115 000 lactations from 87 farms for CYS. The lactational incidence was 6.9% (RP), 12.8% (MET) and 9.2% (CYS). The single-trait animal model equation included fixed effects of herd-year-season of calving and parity of the cow, random direct additive genetic effect, random permanent environmental effect and residual effect. The single-step genomic prediction (ssGBLUP) was based on more than 50 000 SNP-genotyped individuals. Heritability for all traits did not exceed 2%. Favourable correlations were found between reproductive disorders resistance, fertility and longevity traits. The correlation between CYS and milk protein yield indicated a genetic predisposition to cystic ovarian disease in high-producing cows. All significant correlations between health traits (clinical mastitis, claw and feet disorders, reproductive disorders) were positive, indicating that the selection for improvement of one health trait would induce a favourable selection response in the others. [ABSTRACT FROM AUTHOR]

Background: For years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a "combined" SS (CSS) evaluation that extends the "combined" BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added. Results: GEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival. Conclusions: A CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a "combined" evaluation of functional traits is more modest for these males. [ABSTRACT FROM AUTHOR]

Background: It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models. Results: Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30–0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented. Conclusions: When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate the effects of crossbreeding on milk production and milk components in crossbred Tsigai, Lacaune, and Improved Wallachian sheep used to create a synthetic Slovak dairy sheep population. The local breeds Tsigai and Improved Wallachian were crossed with the high-yielding breed Lacaune to increase milk yield. The effects of crossbreeding were evaluated based on breed and heterosis effects. The database used consisted of 23 018 lactations from 15 888 ewes. The effects of crossbreeding on milk production and fat and protein content were estimated using the restricted maximum likelihood method in REMLF90. Fixed effects on milk yield were also studied. In ewes with a 100% heterosis effect (ewes from purebred parents of different breeds), the heterosis effect increased milk production by 11.642 kg, milk fat production by 0.772 kg, and protein production by 0.565 kg per milking period. The effect of the Tsigai breed on milk production was -21.98 kg compared to Lacaune, while for the Improved Wallachian breed, the value was -38.58 kg. The Tsigai direct breed effect reduced fat production by 1.56 kg and protein production by 1.34 kg. The Improved Wallachian direct breed effect was -2.71 kg for fat production and -2.27 kg for protein production. [ABSTRACT FROM AUTHOR]

This research paper addresses the hypothesis that the peptidoglycan recognition protein 1 (PGLYRP1) gene polymorphism (Tyr76His; dbSNP ID: ss104796364) has an influence on some performance traits and causes of culling in Polish Holstein-Friesian red-white cows. The study involved 134 cows kept on a farm in the southwest of Poland. PGLYRP1 genotypes TT, CT, and CC were detected. It was shown that compared with cows with genotypes CT and TT, the individuals with genotype CC were characterised by higher lifetime yields and higher amounts of lactation milk, fat, and protein. A beneficial effect of genotype CC, compared with genotype TT, was also noted in the case of the lifespan and, consequently, the length of the productive life and the average number of lactations. Diseases of the musculoskeletal system (genotypes CC and TT) and disorders of the reproductive system (genotype CT) were the most common causes of culling. An essential practical observation was the potentially higher susceptibility of cows with genotype CC to mastitis, which resulted in the necessity to cull over one-fifth of the animals in this group. Simultaneously, no cows in this group were culled due to low performance or metabolic, gastrointestinal, and respiratory diseases. Therefore, the PGLYRP1 gene seems to be a promising potential herd health marker; however, to consider it the main gene, it is necessary to extend the investigations to include more individuals and other breeds of dairy cattle. [ABSTRACT FROM AUTHOR]