A relationship matrix including full pedigree and genomic information

Potential of anogenital distance as a genetic selection trait in Canadian Holsteins

Maintaining optimal fertility in dairy cattle herds is a global challenge that is typically addressed through the genetic selection of fertility indicator traits. However, many of the traits currently implemented in breeding programs are heavily influenced by environmental factors, resulting in a slow rate of genetic improvement. Anogenital distance (AGD) has recently emerged as a promising fertility indicator trait due to its association with favorable reproductive outcomes and its higher heritability estimates compared with currently evaluated traits. This study aimed to enhance the understanding of AGD's genetic potential by estimating its genetic parameters in Canadian Holsteins, assessing the reliability of breeding values, comparing pedigree BLUP to single-step genomic BLUP approaches, and estimating the correlation between AGD breeding values and those of currently evaluated traits. The dataset used in this study comprised 5,541 Canadian Holstein cows and heifers from 20 herds, collected between 2015 and 2020. The final dataset consisted of 4,988 animals with AGD phenotypes after filtering. The pedigree-based heritability estimate for AGD was 0.39 ± 0.04, whereas the incorporation of genomics resulted in a lower estimate of 0.37 ± 0.03. The reliability of estimated breeding values ranged from 0.49 ± 0.03 for phenotyped animals to 0.81 ± 0.05 for proven sires with at least 30 phenotyped daughters. The integration of genomic information improved the reliability of breeding values, with gains ranging from 0.01 gain for proven sires to 0.14 relative gain for unproven sires. High gain in observed reliability for females without records was demonstrated when genomic information was included, using both split forward validation (0.26) and 5-fold cross-validation (0.14). The AGD breeding values showed moderate unfavorable correlations with relative breeding values of age at first service and production traits including milk yield, fat yield, and protein yield. This suggests that AGD may influence reproductive maturity in heifers but could also have an unfavorable association with production traits, highlighting the need for balanced breeding strategies that consider both fertility and production outcomes. Future studies should aim to expand phenotype data across lifetimes and breeds and estimate genetic correlations with traditional reproduction and production traits using multitrait models.

A genome-wide association study on rumination time in first-lactation dairy cattle

Rumination time (RT) in dairy cattle is a crucial indicator of health, production, reproduction, and greenhouse gas emissions. With moderate heritability estimates for RT, there is potential for further analyses regarding the genetic architecture of the trait. To identify genomic regions associated with RT, we conducted a GWAS on SNPs in a cohort of 452 mid-first-lactation Canadian Holstein cows, followed by the annotation of genes and enrichment analyses of quantitative trait loci (QTL). Animals were genotyped using a medium-density SNP panel (50 K). Quality control measures were used to remove markers residing on nonautosomal chromosomes or with minor allele frequencies <5%, and SNP or animals with call rates lower than 90%. The SNP effects were estimated using single-step genomic BLUP. Significant markers were identified using a chromosome-wise modified Bonferroni correction, based on the expected number of independent chromosome segments. We identified 35 SNPs significantly associated with RT, mapping 34 genes within a 50-kbp interval up and downstream from these SNPs. Additionally, 19 QTL were found enriched in these genomic regions. Notably, genes such as ATP2B4, LDB3,WARS2, and PTPRO were identified, suggesting potential links to muscle fiber activity and milk solids percentage. The enriched QTL were associated with traits related to fat and protein synthesis and deposition in both milk and muscle tissues. Gene Ontology analysis highlighted terms related to muscle contraction and neuronal communication, consistent with the physiological processes underlying RT. Our findings offer new insights into the genetic architecture of RT, advancing the understanding of the physiological mechanisms governing this complex trait.

Genomic selection and weighted single-step genome-wide association study of sheep body weight and milk yield: Imputing low-coverage sequencing data with similar genetic background panels

Low-coverage whole-genome sequencing (LcWGS), a cost-effective genotyping method, offers greater flexibility in variant detection than SNP chips. However, to our knowledge, no studies have explored the application of LcWGS in sheep. This study aimed to evaluate the feasibility of implementing LcWGS and genotype imputation and assess their applicability in genomic studies of body weight and milk yield in sheep. A total of 45,787 birth weight (BiW), 31,135 weaning daily gain (WDG), 8,928 milk yield (MY), and 4,918 milk yield per unit of metabolic body weight (MWMY) data records were analyzed. Among these, 2,366 sheep had imputed high-density genotypes. Simulated sequencing depths from 0.1× to 3× were imputed using reference panels of 100 to 600 individuals. Genotype concordance with true data improved from 0.8875 to 0.9852 as the sequencing depth and panel size increased. The single-step GBLUP method applied to the imputed data yielded higher accuracy for BiW, WDG, MY, and MWMY than the classical pedigree-based BLUP, and notably increased MY accuracy from 0.61 to 0.66. Furthermore, a weighted single-step genome-wide association study identified key genes associated with BiW (ANKS1B, OPRM1, CSMD1), WDG (TKDP5, GRP, RAX, IGFBP7), MY (CCSER1, FGGY, HOOK1), and MWMY (NDUFA10, ZNF385D, NWD1), revealing the importance of multiple pathways in sheep growth and milk production. This is the first study to assess the feasibility of combining LcWGS with genotype imputation for sheep genomic selection, balancing economic costs and imputation efficiency. Furthermore, we demonstrate an effective approach for identifying genetic variants linked to body weight and milk production, offering a cost-effective strategy for dairy sheep breeding.

Genomic Evaluation of Harvest Weight Uniformity in Penaeus vannamei Under a 3FAM Design Incorporating Indirect Genetic Effect

Harvest weight uniformity is a critical economic trait in the production of Pacific white shrimp (Penaeus vannamei). Social interactions among individuals can significantly influence both uniformity and productivity in aquaculture. To improve harvest weight uniformity through selective breeding, it is essential to accurately partition the genetic component of social effects, known as an indirect genetic effect (IGE), from purely environmental factors. Since IGEs cannot be estimated when all individuals are kept in a single group, a specialized experimental design, such as the grouping design with three families per group (3FAM), is required. With this experimental design, the shrimp population is divided into multiple groups (cages), each containing three families. Individuals from each family are then evenly subdivided and placed in three cages, thereby enabling the estimation of both direct and social genetic effects. Additionally, integrating genomic information instead of relying solely on pedigree data improves the accuracy of genetic relatedness among individuals, leading to more precise genetic evaluation. This study employed a 3FAM experimental design involving 40 families (36 individuals per family) to estimate the contribution of direct and indirect genetic effects on harvest weight uniformity. The genotypes of all tested individuals obtained using the 55K SNP panel were incorporated into a hierarchical generalized linear model to predict direct genetic effects and indirect genetic effects (IGE) separately. The results revealed that the heritability of harvest weight uniformity was low (0.005 to 0.017). However, the genetic coefficient of variation (0.340 to 0.528) indicates that using the residual variance in harvest weight as a selection criterion for improving uniformity is feasible. Incorporating IGE into the model increased heritability estimates for uniformity by 150% to 240% and genetic coefficient of variation for uniformity by 32.11% to 55.29%, compared to the model without IGE. Moreover, the genetic correlation between harvest weight and its uniformity shifted from a strongly negative value (-0.862 to -0.683) to a weakly positive value (0.203 to 0.117), suggesting an improvement in the genetic relationship between the traits and better separation of genetic and environmental effects. The inclusion of genomic data enhanced the prediction ability of single-step best linear unbiased prediction for both harvest weight and uniformity by 6.35% and 10.53%, respectively, compared to the pedigree-based best linear unbiased prediction. These findings highlight the importance of incorporating IGE and utilizing genomic selection methods to enhance selection accuracy for obtaining harvest weight uniformity. This approach provides a theoretical foundation for guiding uniformity improvements in shrimp breeding programs and offers potential applications in other food production systems.

Genome-wide association studies of dairy cattle resistance to digital dermatitis recorded at four distinct lactation stages

Digital dermatitis (DD) is an endemic infectious hoof disease causing lameness in dairy cattle. The aim of the present study was to investigate the genetic profile of DD development using phenotypic and genotypic data on 2192 Holstein cows. The feet of each cow were clinically examined four times: pre-calving, shortly after calving, near peak of milk production, and in late lactation. Presence or absence of disease and proportion of healthy feet per cow constituted two DD phenotypes of study. For each phenotype and timepoint of clinical examination, we conducted single-step genome-wide association analyses to identify individual markers and genomic regions linked to DD. We focused on the ten 1-Mb windows that explained the largest proportion of the total genetic variance as well as windows that enclosed significant markers. Functional enrichment analysis was also applied to determine functional candidate genes for DD. Significant (P < 0.05) genomic heritability estimates were derived ranging from 0.21 to 0.25. Results revealed two markers on chromosomes 7 and 15 that were related to both disease phenotypes. Furthermore, we identified three genomic windows on chromosome 14 and one window on chromosome 7 each explaining more than 1% of the trait additive genetic variance. Functional enrichment analysis revealed multiple promising candidate genes implicated in hoof health, wound healing, and inflammatory skin diseases. Collectively, our results provide novel insights into the biological mechanism of host resistance to DD development in dairy cattle and support genomic selection towards improving foot health.

The effect of different approaches to determining the regularization parameter of bayesian LASSO on the accuracy of genomic prediction

Using dense genomic markers opens up new opportunities and challenges for breeding programs. The need to penalize marker-specific regression coefficients becomes particularly important when dense markers are available. Therefore, fitting the marker effects to observations using a regularization technique, such as Bayesian LASSO (BL) regression, is of great interesting. When the Laplace prior distribution is applied to the regression coefficients, BL can be interpreted as a regularization of theL 1 norm based on the Bayesian approach. A critical issue is the appropriate selection of hyperparameters values in the prior distributions of regularization techniques, as these values essentially control the sparsity in the estimated model. The purpose of this study was to evaluate different approaches for selecting the regularization parameter in BL, based on fully Bayesian approaches-such as gamma prior (BL_Gamma), beta prior (BL_Beta) and fixed prior (BL_Fixed) as well as data-driven approaches like cross-validation based on mean square error (BL_CV_MSE) and prediction accuracy (BL_CV_PA). Additionally, information-criteria-based methods including Akaike's information criterion (BL_AIC), Bayesian information criterion (BL_BIC) and Deviance information criterion (BL_DIC), were explored. For this purpose, a genome containing eight chromosomes (each 1 Morgan in length) with 100 randomly distributed quantitative trait loci was simulated. The studied scenarios were as follows: Scenario 1 involved 4000 markers and heritability of 0.2, scenario 2 involved 4000 markers and heritability of 0.6, scenario 3 involved 16,000 markers and heritability of 0.2; and scenario 4 involved 16,000 markers and heritability of 0.6. The results showed that among the fully Bayesian and cross-validation approaches, BL_Gamma, BL_Beta, and BL_CV_MSE provided the highest prediction accuracy (PA) in scenario 1 and 3. With increased marker density and heritability (scenario 4), the cross-validation approaches performed slightly better. The information-criteria-based methods demonstrated the lowest PA. Increasing heritability and marker density led to a decrease and an increase in the model penalty on the regression coefficients, respectively. The PA obtained in the target population ranged from 0.210 to 0.413 in Scenario 1, 0.402 to 0.600 in Scenario 2, 0.256 to 0.442 in Scenario 3, and 0.478 to 0.653 in Scenario 4. In generally, fully Bayesian approaches based on random priors for the regularization parameter are recommended for BL, as they provide acceptable PA with lower computational loads.

Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation

OBJECTIVE

This study assessed the impact of incorporating imputed single nucleotide polymorphism (SNP) information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy.

METHODS

Data encompassed pedigree and phenotypic records for 305-day milk yield (MY), 305-day fat (Fat), and age at first calving (AFC) from 12,859 first-lactation cows, and genotypic records of various densities from 4,364 animals. A set of 64 animals genotyped with GeneSeek Genomic Profiler 80K and with four or more genotyped progenies was defined as target animals to simulate imputation scenarios for non-genotyped individuals. Actual and imputed genotypes were utilized to construct three SNP sets. All SNP Sets contained actual and imputed SNP markers from genotyped animals. SNP Set 1 contained no SNPs from target animals, whereas SNP Set 2 incorporated imputed SNPs from target animals, and SNP Set 3 added actual SNPs from target animals. Genomic-polygenic evaluations were conducted using a 3-trait single-step model that included contemporary group, calving age, and heterozygosity as fixed effects and animal additive genetic and residual as random effects.

RESULTS

The imputation accuracy was similar across non-genotyped animals irrespective of the number of genotyped progenies (average: 40.55%; range: 34.68% to 53.82%). Estimates of additive genetic and environmental variances and covariances for MY and AFC varied across SNP sets. SNP Sets 1 and 2 had slightly higher additive genetic and lower environmental variances and covariances than SNP Set 3. Heritabilities and additive genetic, environmental, and phenotypic correlations between MY, Fat, and AFC were similar across all SNP Sets. Spearman rank correlations between genomic-polygenic estimated breeding values from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003).

CONCLUSION

Utilization of phenotypic and pedigree data from imputed non-genotyped animals enhanced the efficiency and cost-effectiveness of the genetic improvement program in the Thai multibreed dairy cattle population.

Genetic parameters and single-step genome-wide association analysis for trematode (Fasciola hepatica and Calicophoron/Paramphistomum spp.) infections in German dairy cows

Infections with the liver fluke (Fasciola hepatica) cause economic losses in cattle production worldwide. Also, infections with rumen flukes (Calicophoron/Paramphistomum spp.) are gaining importance in grazing cattle in Europe. However, increasing resistance of helminth parasites against anthelmintics and limitations in treatment emphasize the need for alternative breeding approaches. This study included 1602 dairy cows kept on 29 farms with 2423 observations for F. hepatica and Calicophoron/Paramphistomum spp. egg counts per gram faeces (EPG). The EPGs were binary defined (infected: EPG > 0; non-infected: EPG = 0) and logarithmically transformed. The pedigree included 7939 cows. Genotypes (777 k) were available for 214 cows. A single-step GBLUP (ssGBLUP) model was applied to estimate genetic parameters for infection traits. Genomic breeding values from ssGBLUP were used in a single-step genome-wide association study (ssGWAS) to identify genetic variants associated with helminth infections. The heritability for liver fluke infections was up to 0.09, and up to 0.34 for rumen fluke infections. The genetic correlations between liver and rumen fluke infections ranged from 0.49 to 0.53, indicating that breeding for improved resilience to both helminth taxa is possible simultaneously. The ssGWAS revealed four SNPs for liver fluke infections on BTA 5, 13, 26 and 29, and 17 SNPs for rumen fluke infections on BTA 3 and 23. The SNPs for liver fluke infections were annotated to 12 potential candidate genes, most of which involved in liver fibrosis and immunity. The LRRC8B gene was found to be involved in host-rumen fluke interactions.

Genetic parameters, genomic prediction, and identification of regulatory regions located on chromosome 14 for weight traits in Nellore cattle

This study aimed to investigate functional variants in chromosome 14 (BTA14) and its impact in genomic selection for birth weight (BW), weaning weight (WW), and yearling weight (YW) in Nellore cattle. Genetic parameter estimation and the weighted single-step genomic best linear unbiased prediction (WssGBLUP) analyses were performed. Direct additive heritability estimates were high for WW and YW, and moderate for BW. Trait-associated variants distributed across multiple regions on BTA14 were observed in the weighted single-step genome-wide association studies (WssGWAS) results, implying a polygenic genetic architecture for weight in different ages. Several genes have been found in association with the weight traits, including the CUB And Sushi multiple domains 3 (CSMD3), thyroglobulin (TG), and diacylglycerol O-acyltransferase 1 (DGAT1) genes. The variance explained per SNP was higher in six functional classes of gene regulatory regions (5UTR, CpG islands, downstream, upstream, long non-coding RNA, and transcription factor binding sites (TFBS)), highlighting their importance for weight traits in Nellore cattle. A marginal increase in accuracy was observed when the selected functional variants (SV) information was considered in the WssGBLUP method, probably because of the small number of SV available on BTA14. The identified genes, pathways, and functions contribute to a better understanding of the genetic and physiological mechanisms regulating weight traits in the Nellore breed.

GOplan: an R package for animal breeding program design via integrating Gene Flow and Bayesian optimization methods

The design of breeding programs is crucial for maximizing economic gains. Simulation provides the most efficient measures to test these programs, as real-world trials are often costly and time-consuming. We developed GOplan, a comprehensive and user-friendly R package designed to develop animal breeding programs considering pure-bred populations and crossbreeding systems. Compared with other traditional simulators, it has mainstream crossbreeding frameworks that streamline modeling and use Gene Flow and Bayesian optimization methods to enhance breeding program efficiency. GOplan includes 3 key functions: runCore() to evaluate the effects of nucleus breeding programs, runWhole() to predict economic outcomes and the production performance of crossbreeding systems, and runOpt() to optimize crossbreeding structures for greater profitability. These functions support breeders in better planning and accelerating breeding goals. Additionally, the application of Bayesian optimization algorithms in this study provides valuable insights for developing new optimization algorithms in the future. The software is available at https://github.com/CAU-TeamLiuJF/GOplan.

Optimizing fully-efficient two-stage models for genomic selection using open-source software

Genomic-assisted breeding has transitioned from theoretical concepts to practical applications in breeding. Genomic selection (GS) predicts genomic breeding values (GEBV) using dense genetic markers. Single-stage models predict GEBVs from phenotypic observations in one step, fully accounting for the entire variance-covariance structure among genotypes, but face computational challenges. Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. However, unweighted (UNW) two-stage models assume independent errors among adjusted means, neglecting correlations among estimation errors. Here, we show that fully-efficient two-stage models perform similarly to UNW models for randomized complete block designs but substantially better for augmented designs. Our simulation studies demonstrate the impact of the fully-efficient methodology on prediction accuracy across different implementations and scenarios. Incorporating non-additive effects and augmented designs significantly improved accuracy, emphasizing the synergy between design and model strategy. Consistent performance requires the estimation error covariance to be incorporated into a random effect (Full_R model) rather than into the residuals. Our results suggest that the fully-efficient methodology, particularly the Full_R model, should be more prevalent, especially as GS increases the appeal of sparse designs. We also provide a comprehensive theoretical background and open-source R code, enhancing understanding and facilitating broader adoption of fully-efficient two-stage models in GS. Here, we offer insights into the practical applications of fully-efficient models and their potential to increase genetic gain, demonstrating a 13.80 % improvement after five selection cycles when moving from UNW to Full_R models.

Performance of weighted genomic BLUP and Bayesian methods for Hanwoo carcass traits

To improve the quality and yield of the Korean beef industry, selection criteria often focus on estimated breeding values for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study estimated genetic parameters and assessed the accuracy of genomic estimated breeding values (GEBVs) using SNP weighting methods. We compared the accuracy of these methods with the genomic best linear unbiased prediction (GBLUP) and various Bayesian approaches (BayesA, BayesB, BayesC, and BayesCPi) for the specified traits. The study used single-trait animal models, including GBLUP, weighted GBLUP (WGBLUP), and the Bayesian methods to predict genomic breeding values in a population of Hanwoo steers. A total of 19154 phenotypes were collected with all animals genotyped using the Illumina Bovine 50 K SNP chip. The average heritability for the carcass traits was 0.33 (GBLUP) and 0.35 (Bayesian), with Bayesian methods yielding heritability estimates that were on average 0.02 points (6.1%) higher than GBLUP. The accuracy of genomic predictions ranged from 0.7-0.83 (GBLUP), 0.83-0.87 (WGBLUP), and 0.81-0.87 across the Bayesian methods. WGBLUP accuracies for the carcass traits were, on average 8.97% higher than the GBLUP accuracies and 1.80% higher than the Bayesian alphabets. The Bayesian alphabet's accuracy is also, on average 6.00% higher than the GBLUP accuracy. According to these findings, the weighting GBLUP approach provides higher prediction accuracy for Hanwoo carcass traits than the Bayesian alphabet. Therefore, WGBLUP can be used for genomic selection in the Hanwoo evaluation program.

Transgenerational epigenetic heritability for growth, body composition, and reproductive traits in Landrace pigs

Epigenetics is an important source of variation in complex traits that is not due to changes in DNA sequences, and is dependent on the environment the individuals are exposed to. Therefore, we aimed to estimate transgenerational epigenetic heritability, percentage of resetting epigenetic marks, genetic parameters, and predicting breeding values using genetic and epigenetic models for growth, body composition, and reproductive traits in Landrace pigs using routinely recorded datasets. Birth and weaning weight, backfat thickness, total number of piglets born, and number of piglets born alive (BW, WW, BF, TNB, and NBA, respectively) were investigated. Models including epigenetic effects had a similar or better fit than solely genetic models. Including genomic information in epigenetic models resulted in large changes in the variance component estimates. Transgenerational epigenetic heritability estimates ranged between 0.042 (NBA) to 0.336 (BF). The reset coefficient estimates for epigenetic marks were between 80% and 90%. Heritability estimates for the direct additive and maternal genetic effects ranged between 0.040 (BW) to 0.502 (BF) and 0.034 (BF) to 0.134 (BW), respectively. Repeatability of the reproductive traits ranged between 0.098 (NBA) to 0.148 (TNB). Prediction accuracies, bias, and dispersion of breeding values ranged between 0.199 (BW) to 0.443 (BF), -0.080 (WW) to 0.034 (NBA), and -0.134 (WW) to 0.131 (TNB), respectively, with no substantial differences between genetic and epigenetic models. Transgenerational epigenetic heritability estimates are moderate for growth and body composition and low for reproductive traits in North American Landrace pigs. Fitting epigenetic effects in genetic models did not impact the prediction of breeding values.

All-breed single-step genomic best linear unbiased predictor evaluations for fertility traits in US dairy cattle

The US dairy cattle genetic evaluation is currently a multistep process, including multibreed traditional BLUP estimations followed by single-breed SNP effects estimation. Single-step GBLUP (ssGBLUP) combines pedigree and genomic data for all breeds in one analysis. Unknown parent groups (UPG) or metafounders (MF) can be used to address missing pedigree information. Fertility traits are notably difficult to evaluate due to low heritabilities, changing management, and a higher recent emphasis on selection to move in a favorable direction. We assessed bias, dispersion, and accuracy of fertility traits in all-breed US dairy cattle using pedigree-based BLUP (PBLUP) and ssGBLUP with UPG or MF; with 5% or 10% residual polygenic effect. Validation methods included the linear regression method and comparison of early and late deregressed proofs for Holstein and Jersey breeds. By comparing MF or UPG in PBLUP, we observed similar results in terms of bias, dispersion, and correlations between early and recent predictions. When genomics was used, ssGBLUP with MF and 10% residual polygenic effect consistently outperformed other models regarding bias, dispersion, and correlations. Compared with multistep results, ssGBLUP with MF and 10% residual polygenic effect showed less bias and increased correlations but slightly overdispersed estimates. Overall, genomic prediction of fertility traits using ssGBLUP was accurate and unbiased, more so with MF than with UPG.

Weighted single-step genomic best linear unbiased predictor enhances the genomic prediction accuracy for milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Previous studies have shown that milk citrate predicted by milk mid-infrared (MIR) spectra is strongly affected by a few genomic regions. This study aimed to explore the effect of weighted single-step GBLUP on the accuracy of genomic prediction (GP) for MIR-predicted milk citrate in early-lactation Holstein cows. A total of 134,517 test-day predicted milk citrate collected within the first 50 DIM on 52,198 Holstein cows from the first 5 parities were used. There were 122,218 animals in the pedigree, of which 4,479 had genotypic data for 566,170 SNPs. Two datasets (partial and whole datasets) were used to verify whether the accuracy of GP is improved using the following different methods. The (genomic) estimated breeding values (EBV or GEBV) in the partial and whole datasets were estimated by pedigree-based BLUP (ABLUP), single-step GBLUP (ssGBLUP, pedigree-genomic combined using no weight for SNP), and weighted ssGBLUP (WssGBLUP, pedigree-genomic combined using weighted SNP), respectively. The difference between the 2 datasets is that the phenotypic data from 2017 to 2019 in the partial dataset were set as missing values. One hundred eighty-one youngest cows with genomic data were selected as the validation population. A linear regression method was used to compare EBV (GEBV) predicted for partial and whole datasets. The accuracies of GP for ABLUP and ssGBLUP were 0.42 and 0.70, respectively. The accuracies of GP for WssGBLUP in the 5 iterations with different CT (constant) values (determines departure from normality for SNP effects) ranged from 0.70 to 0.86. This study showed that weighted SNP is beneficial in improving prediction accuracy for predicted milk citrate.

Cross-ancestry meta-genome-wide association studies provide insights to the understanding of semen traits in pigs

Semen traits play a crucial role in pig reproduction and fertility. However, limited data availability hinder a comprehensive understanding of the genetic mechanisms underlying these traits. In this study, we integrated 597 299 ejaculates and 3 596 sequence data to identify genetic variants and candidate genes related to four semen traits, including sperm progressive motility (MOT), semen volume, sperm concentration (CON), and effective sperm count (SUM). A cross-ancestry meta-genome-wide association study was conducted to detect 163 lead single nucleotide polymorphisms (SNPs) associated with MOT, CON, and SUM. Subsequently, transcriptome-wide association studies and colocalisation analyses were integrated to identify 176 candidate genes, many of which have documented roles in spermatogenesis or male mammal semen traits. Our analysis highlighted the potential involvement of CSM5, PDZD9, and LDAF1 in regulating semen traits through multiple methods. Finally, to validate the function of significant SNPs, we performed genomic feature best linear unbiased prediction in 348 independent pigs using identified trait-related SNP subsets as genomic features. We found that integrating the top 0.1, 1, and 5% significant SNPs as genomic features could enhance genomic prediction accuracy for CON and MOT compared to traditional genomic best linear unbiased prediction. This study contributes to a comprehensive understanding of the genetic mechanisms of boar semen traits and provides insight for developing genomic selection models.

Genetic parameters and genome-wide association analyses for lifetime productivity in Chinese Holstein cattle

Lifetime productivity is a trait of great importance to dairy cattle populations, as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics datasets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the restricted maximum likelihood method. Genome-wide association studies and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-sequencing data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE), whereas the genetic correlation with 305-d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization, and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle, and important candidate genes were identified by integrating genomic and transcriptomic datasets.

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

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

Beef Genetic Evaluations

Genetic evaluations in beef cattle have evolved over the past 50 years relative to the hardware or software used, the statistical methodology underpinning them, and the traits evaluated. However, the underlying premise has remained the same; to generate predictions of genetic merit such that selection decisions can be made that materialize as phenotypic changes in commercial animals. The wide-spread availability and adoption of genomic technology has enabled more accurate genetic predictions of young animals albeit with the requirement of continual collection and reporting of phenotypic data.

Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models

Random-regression models (RRM) are used in national genetic evaluations for longitudinal traits. The outputs of RRM are an index based on random-regression coefficients and its reliability. The reliabilities are obtained from the inverse of the coefficient matrix of mixed model equations (MME). The reliabilities must be approximated for large datasets because it is impossible to invert the MME. There is no extensive literature on methods to approximate the reliabilities of RRM when genomic information is included by single-step GBLUP. We developed an algorithm to approximate such reliabilities. Our method combines the reliability of the index without genomic information with the reliability of a GBLUP model in terms of effective record contributions. We tested our algorithm in the 3-lactation model for milk yield from the Czech Republic. The data had 30 million test-day records, 2.5 million animals in the pedigree, and 54,000 genotyped animals. The correlation between our approximation and the reliabilities obtained from the inversion of the MME was 0.98, and the slope and intercept of the regression were 0.91 and 0.02, respectively. The elapsed time to approximate the reliabilities for the Czech data was 21 min.

Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generation Eucalyptus grandis population

Genomic Selection (GS) in tree breeding optimizes genetic gains by leveraging genomic data to enable early selection of seedlings without phenotypic data reducing breeding cycle and increasing selection intensity. Traditional assessments of the potential of GS in forest trees have typically focused on model performance using cross-validation within the same generation but evaluating effectively realized predictive ability (RPA) across generations is crucial. This study estimated RPAs for volume growth (VOL), wood density (WD), and pulp yield (PY) across four generations breeding of Eucalyptus grandis. The training set spanned three generations, including 34,461 trees with three-year growth data, 6,014 trees with wood quality trait data, and 1,918 trees with 12,695 SNPs (single nucleotide polymorphisms) data. Employing single-step genomic BLUP, we compared the genomic predictions of breeding values (GEBVs) for 1,153 fourth-generation full-sib seedlings in the greenhouse with their later-collected phenotypic estimated breeding values (EBVs) at age three years. RPAs were estimated using three GS targets (individual trees, trees within families, and families), two selection criteria (single- and multiple-trait), and training populations of either all 1,918 genotyped trees or the 67 direct ancestors of the selection candidates. RPAs were higher for wood quality traits (0.33 to 0.59) compared to VOL (0.14 to 0.19) and improved for wood traits (0.42 to 0.75) but not for VOL when trained only with direct ancestors, highlighting the challenges in accurately predicting growth traits. GS was more effective at excluding bottom-ranked candidates than selecting top-ranked ones. The between-family GS approach outperformed individual-tree selection for VOL (0.11 to 0.16) and PY (0.72 to 0.75), but not for WD (0.43 vs. 0.42). Furthermore, higher levels of relatedness and lower genotype by environment (G × E) interaction between training and testing populations enhanced RPAs for VOL (0.39). In summary, despite limited effectiveness in ranking top VOL individuals, GS effectively identified low-performing individuals and families. These multi-generational findings underscore GS's potential in tree breeding, stressing the importance of considering relatedness and G × E interaction for optimal performance.

Bibliometric analysis of genomic selection in breeding of animal from 1993 to 2024: global trends and advancements

Animal breeding became a difficult science when numerous genes influenced economically significant features. The major source of genetic improvement is selection, and as such, the large generation intervals in these strategies lead to reduced rates of improvement. Therefore, breeding control, genetic improvement research, and selection processes are accelerated by genomic selection. This article regarding global research interest trends in genomic selection in animal breeding themes was examined using bibliometric analysis, which employed papers from 1993 to 2024 from the SCI-Expanded, SSCI, AHCI, and E-SCI indexes. Over the period of 31 years, the first 3,181 published articles on genomic selection in animal breeding were gathered. Additionally, the study displays trends in co-authorships according to nations and academic institutions as well as co-occurrences of author keywords. There have been more articles since 2010 about the use of genomic selection in animal breeding, building up a sizable library of work that will last until 2024. Among the top academics in the field are Calus MPL, Li J, and Wang Y. The most productive institutions were The United Kingdom's University of Edinburgh, Aarhus University (Denmark) and China Agricultural University. The current hotspots in this field of study include "selection," and "association," according to keyword co-occurrence and frequency analysis. China, the United States, Brazil, Canada, and United Kingdom are the top five countries that produced the most papers with the highest levels of international collaboration and networking. The main topics of current study include prediction, accuracy, association, traits, and selection. New techniques for selection, prediction, accuracy, traits, and association were developed as the discipline matured. Research collaborations across countries, institutions, and writers promote knowledge sharing, effective issue resolution, and superior outcomes.

Parameter Estimation of Host Genomic and Gut Microbiota Contribution to Growth and Feed Efficiency Traits in Meat Rabbits

Rabbits can efficiently utilize plant fibers that are indigestible to humans, and hence may contribute to the alleviation of feed-food competition. Therefore, it is economically and ecologically important to genetically improve the growth performance and feed efficiency of meat rabbits. In this study, we combined pedigree, genomic, and gut microbiota data to estimate genetic and microbial parameters for nine growth and feed efficiency traits of 739 New Zealand White rabbits, including body weight (BW) at 35 (BW35), 70 (BW70), and 84 (BW84) days of age, and average daily gain (ADG), feed conversion ratio (FCR), and residual feed intake (RFI) within two age intervals of 35-70 days (ADG70, FCR70, and RFI70) and 35-84 days (ADG84, FCR84, and RFI84). Based on single-step genomic best linear unbiased prediction, three BW traits and two ADG traits had the high estimates (±standard error, SE) of heritability, ranging from 0.44 ± 0.13 of BW35 to 0.66 ± 0.08 of BW70. Moderate heritabilities were observed for RFI70 (0.22 ± 0.07) and RFI84 (0.29 ± 0.07), whereas the estimates did not significantly deviate from zero for the two FCR traits. There was moderate positive genetic correlation (±SE) between BW70 and ADG70 (0.579 ± 0.086), but BW70 did not correlate with RFI70. Based on microbial best linear unbiased prediction, the estimates of microbiability did not significantly deviate from zero for any trait. Based on the combined use of genomic and gut microbiota data, the parameters obtained in this study could help us to implement efficient breeding schemes in meat rabbits.

Optimizing purebred selection to improve crossbred performance

Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations (r p c ). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels ofr p c . Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios asr p c values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy whenr p c was less than 0.5. However, atr p c = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across allr p c levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of  r p c . Furthermore, whenr p c was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, ther p c value emerged as a critical factor in predicting CP based on purebred data. However, the optimal model to maximize CP depends on the factors influencingr p c . Consequently, ongoing research aims to develop models that optimize purebred selection, further enhancing CP.

Genetic parameters of milk mid-infrared spectra and their genetic relationships with milk production and feed efficiency traits in French Lacaune dairy sheep

In French dairy sheep, Fourier transform infrared (FTIR) milk spectral data routinely predict the major milk components used in national genetic evaluations. The direct influence of genetic and environmental factors on milk FTIR spectra has been widely studied in dairy cattle, and relatively little in dairy ewes. In this study, 36,873 milk test-day records were available for 4,712 French Lacaune ewes farmed on 8 commercial farms. Our main goals were to provide the first description of spectral data and estimate the genetic parameters of French Lacaune dairy sheep during lactation. Principal component analysis (PCA) results demonstrated the impact of the lactation period on specific wavenumbers, allowing the identification of FTIR spectra collected at early (mo 2-4) and late (mo 5-7) lactation stages. The average estimated heritability (±mean SE) of the FTIR milk spectra from 2,971 to 926 cm-1 (446 wavenumbers) was 0.29 ± 0.02, ranging from 0.13 ± 0.01 to 0.42 ± 0.02. Furthermore, the heritabilities of spectra collected at the beginning or end of lactation changed at each point of the spectrum. However, at each wavenumber, the genomic correlation of transmittance values between these 2 lactation periods was high (>0.77), indicating the absence of a genotype-environment interaction. The genomic correlations between spectral regions and milk production traits (i.e., daily milk yield, fat and protein content, somatic cell score) varied from moderate to high. The results suggested that the most heritable areas of the spectrum were also genetically associated with dairy traits. Finally, the genomic correlations observed between the ewes' feed efficiency traits and the FTIR spectrum were moderate to high, while the genomic correlations between the change in body condition score and spectral data were rather low to moderate. This study confirmed that spectral data from Lacaune ewe milk were heritable, evolved phenotypically and genetically during lactation and were genetically correlated with traits included in breeding goals or traits of interest to the dairy industry.

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Prediction of resistance, virulence, and host-by-pathogen interactions using dual-genome prediction models

KEY MESSAGE

Integrating disease screening data and genomic data for host and pathogen populations into prediction models provides breeders and pathologists with a unified framework to develop disease resistance. Developing disease resistance in crops typically consists of exposing breeding populations to a virulent strain of the pathogen that is causing disease. While including a diverse set of pathogens in the experiments would be desirable for developing broad and durable disease resistance, it is logistically complex and uncommon, and limits our capacity to implement dual (host-by-pathogen)-genome prediction models. Data from an alternative disease screening system that challenges a diverse sweet corn population with a diverse set of pathogen isolates are provided to demonstrate the changes in genetic parameter estimates that result from using genomic data to provide connectivity across sparsely tested experimental treatments. An inflation in genetic variance estimates was observed when among isolate relatedness estimates were included in prediction models, which was moderated when host-by-pathogen interaction effects were incorporated into models. The complete model that included genomic similarity matrices for host, pathogen, and interaction effects indicated that the proportion of phenotypic variation in lesion size that is attributable to host, pathogen, and interaction effects was similar. Estimates of the stability of lesion size predictions for host varieties inoculated with different isolates and the stability of isolates used to inoculate different hosts were also similar. In this pathosystem, genetic parameter estimates indicate that host, pathogen, and host-by-pathogen interaction predictions may be used to identify crop varieties that are resistant to specific virulence mechanisms and to guide the deployment of these sources of resistance into pathogen populations where they will be more effective.

Assessing genotype-environment interactions in Atlantic salmon reared in freshwater loch and recirculating systems

The interest in recirculating aquaculture systems (RAS) is growing due to their benefits such as increased productivity, better control over animal care, reduced environmental effects, and less water consumption. However, in some regions of the world, traditional aquaculture methods remain prevalent, and selective breeding has often been designed for performance within these systems. Therefore, it is important to evaluate how current fish populations fare in RAS to guide future breeding choices. In a commercial setting, we explore the genetic structure of growth characteristics, measure genotype-environment interactions (GxE) in salmon smolts, and examine genetic markers related to growth in freshwater lochs and RAS. Young salmon were raised together until they reached the parr stage, after which they were divided equally between freshwater net-pens and RAS. After an 8-week period, we sampled fish from each environment and genotyped them. Our findings revealed that fish reared in RAS were generally smaller in weight and length but exhibited a higher condition factor and uniformity. We found a notably smaller component of unexplained variance in the RAS, leading to higher heritability estimates. We observed a low GxE effect for length and condition factor, but significant re-ranking for whole-body weight, as well as noticeable differences in trait associations across environments. Specifically, a segment of chromosome 22 was found to be linked with the condition factor in the RAS population only. Results suggests that if the use of RAS continues to expand, the efficiency of existing commercial populations may not reach its full potential unless breeding programs specific to RAS are implemented.

Assessing different metrics of pedigree and genomic inbreeding and inbreeding effect on growth, fertility, and feed efficiency traits in a closed-herd Nellore cattle population

BACKGROUND

The selection of individuals based on their predicted breeding values and mating of related individuals can increase the proportion of identical-by-descent alleles. In this context, the objectives of this study were to estimate inbreeding coefficients based on alternative metrics and data sources such as pedigree (FPED), hybrid genomic relationship matrix H (FH), and ROH of different length (FROH); and calculate Pearson correlations between the different metrics in a closed Nellore cattle population selected for body weight adjusted to 378 days of age (W378). In addition to total FROH (all classes) coefficients were also estimated based on the size class of the ROH segments: FROH1 (1-2 Mb), FROH2 (2-4 Mb), FROH3 (4-8 Mb), FROH4 (8-16 Mb), and FROH5 (> 16 Mb), and for each chromosome (FROH_CHR). Furthermore, we assessed the effect of each inbreeding metric on birth weight (BW), body weights adjusted to 210 (W210) and W378, scrotal circumference (SC), and residual feed intake (RFI). We also evaluated the chromosome-specific effects of inbreeding on growth traits.

RESULTS

The correlation between FPED and FROH was 0.60 while between FH and FROH and FH and FPED were 0.69 and 0.61, respectively. The annual rate of inbreeding was 0.16% for FPED, 0.02% for FH, and 0.16% for FROH. A 1% increase in FROH5 resulted in a reduction of up to -1.327 ± 0.495 kg in W210 and W378. Four inbreeding coefficients (FPED, FH, FROH2, and FROH5) had a significant effect on W378, with reductions of up to -3.810 ± 1.753 kg per 1% increase in FROH2. There was an unfavorable effect of FPED on RFI (0.01 ± 0.0002 kg dry matter/day) and of FROH on SC (-0.056 ± 0.022 cm). The FROH_CHR coefficients calculated for BTA3, BTA5, and BTA8 significantly affected the growth traits.

CONCLUSIONS

Inbreeding depression was observed for all traits evaluated. However, these effects were greater for the criterion used for selection of the animals (i.e., W378). The increase in the genomic inbreeding was associated with a higher inbreeding depression on the traits evaluated when compared to pedigree-based inbreeding. Genomic information should be used as a tool during mating to optimize control of inbreeding and, consequently, minimize inbreeding depression in Nellore cattle.

Genetic parameters and genome-wide association analyses for lifetime productivity in Chinese Holstein cattle

Lifetime productivity is a trait of great importance to dairy cattle populations as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics data sets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the Restricted Maximum Likelihood (REML) method. Genome-wide association studies (GWAS) and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-seq data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE) while the genetic correlation with 305d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle and important candidate genes were identified by integrating genomic and transcriptomic data sets.

Genetic parameters and trends for Johne's disease in US Holsteins: An updated study

Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States. The data were extracted from a JD data repository, maintained at the Council on Dairy Cattle Breeding, and initially supplied by 2 dairy record processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, IDEXX Paratuberculosis Screening Ab Test (IDX) and Parachek 2 (PCK), were used to analyze milk samples with the ELISA technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of EBVs of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant. The correlations of bulls' PTA with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a nonoverlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.

Exploring unknown parent groups and metafounders in single-step genomic BLUP: Insights from a simulated cattle population

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

Breeding for resilience in finishing pigs can decrease tail biting, lameness and mortality

BACKGROUND

Previous research showed that deviations in longitudinal data are heritable and can be used as a proxy for pigs' general resilience. However, only a few studies investigated the relationship between these resilience traits and other traits related to resilience and welfare. Therefore, this study investigated the relationship between resilience traits derived from deviations in longitudinal data and traits related to animal resilience, health and welfare, such as tail and ear biting wounds, lameness and mortality.

RESULTS

In our experiment, 1919 finishing pigs with known pedigree (133 Piétrain sires and 266 crossbred dams) were weighed every 2 weeks and scored for physical abnormalities, such as lameness and ear and tail biting wounds (17,066 records). Resilience was assessed via deviations in body weight, deviations in weighing order and deviations in observed activity during weighing. The association between these resilience traits and physical abnormality traits was investigated and genetic parameters were estimated. Deviations in body weight had moderate heritability estimates (h2 = 25.2 to 36.3%), whereas deviations in weighing order (h2 = 4.2%) and deviations in activity during weighing (h2 = 12.0%) had low heritability estimates. Moreover, deviations in body weight were positively associated and genetically correlated with tail biting wounds (rg = 0.22 to 0.30), lameness (rg = 0.15 to 0.31) and mortality (rg = 0.19 to 0.33). These results indicate that events of tail biting, lameness and mortality are associated with deviations in pigs' body weight evolution. This relationship was not found for deviations in weighing order and activity during weighing. Furthermore, individual body weight deviations were positively correlated with uniformity at the pen level, providing evidence that breeding for these resilience traits might increase both pigs' resilience and within-family uniformity.

CONCLUSIONS

In summary, our findings show that breeding for resilience traits based on deviations in longitudinal weight data can decrease pigs' tail biting wounds, lameness and mortality while improving uniformity at the pen level. These findings are valuable for pig breeders, as they offer evidence that these resilience traits are an indication of animals' general health, welfare and resilience. Moreover, these results will stimulate the quantification of resilience via longitudinal body weights in other species.

Estimation of Genetic Parameters for Growth and WSSV Resistance Traits in Litopenaeus vannamei

The current study aimed to provide a precise assessment of the genetic parameters associated with growth and white spot syndrome virus (WSSV) resistance traits in Pacific white shrimp (Litopenaeus vannamei). This was achieved through a controlled WSSV challenge assay and the analysis of phenotypic values of five traits: body weight (BW), overall length (OL), body length (BL), tail length (TL), and survival hour post-infection (HPI). The analysis included test data from a total of 1017 individuals belonging to 20 families, of which 293 individuals underwent whole-genome resequencing, resulting in 18,137,179 high-quality SNP loci being obtained. Three methods, including pedigree-based best linear unbiased prediction (pBLUP), genomic best linear unbiased prediction (GBLUP), and single-step genomic BLUP (ssGBLUP) were utilized. Compared to the pBLUP model, the heritability of growth-related traits obtained from GBLUP and ssGBLUP was lower, whereas the heritability of WSSV resistance was higher. Both the GBLUP and ssGBLUP models significantly enhanced prediction accuracy. Specifically, the GBLUP model improved the prediction accuracy of BW, OL, BL, TL, and HPI by 4.77%, 21.93%, 19.73%, 19.34%, and 63.44%, respectively. Similarly, the ssGBLUP model improved prediction accuracy by 10.07%, 25.44%, 25.72%, 19.34%, and 122.58%, respectively. The WSSV resistance trait demonstrated the most substantial enhancement using both genomic prediction models, followed by body size traits (e.g., OL, BL, and TL), with BW showing the least improvement. Furthermore, the choice of models minimally impacted the assessment of genetic and phenotypic correlations. Genetic correlations among growth traits ranged from 0.767 to 0.999 across models, indicating high levels of positive correlations. Genetic correlations between growth and WSSV resistance traits ranged from (-0.198) to (-0.019), indicating low levels of negative correlations. This study assured significant advantages of the GBLUP and ssGBLUP models over the pBLUP model in the genetic parameter estimation of growth and WSSV resistance in L. vannamei, providing a foundation for further breeding programs.

Weighted single-step genome-wide association study for direct and maternal genetic effects associated with birth and weaning weights in sheep

Body weight is an important economic trait for sheep meat production, and its genetic improvement is considered one of the main goals in the sheep breeding program. Identifying genomic regions that are associated with growth-related traits accelerates the process of animal breeding through marker-assisted selection, which leads to increased response to selection. In this study, we conducted a weighted single-step genome-wide association study (WssGWAS) to identify potential candidate genes for direct and maternal genetic effects associated with birth weight (BW) and weaning weight (WW) in Baluchi sheep. The data used in this research included 13,408 birth and 13,170 weaning records collected at Abbas-Abad Baluchi Sheep Breeding Station, Mashhad-Iran. Genotypic data of 94 lambs genotyped by Illumina 50K SNP BeadChip for 54,241 markers were used. The proportion of variance explained by genomic windows was calculated by summing the variance of SNPs within 1 megabase (Mb). The top 10 window genomic regions explaining the highest percentages of additive and maternal genetic variances were selected as candidate window genomic regions associated with body weights. Our findings showed that for BW, the top-ranked genomic regions (1 Mb windows) explained 4.30 and 4.92% of the direct additive and maternal genetic variances, respectively. The direct additive genetic variance explained by the genomic window regions varied from 0.31 on chromosome 1 to 0.59 on chromosome 8. The highest (0.84%) and lowest (0.32%) maternal genetic variances were explained by genomic windows on chromosome 10 and 17, respectively. For WW, the top 10 genomic regions explained 6.38 and 5.76% of the direct additive and maternal genetic variances, respectively. The highest and lowest contribution of direct additive genetic variances were 1.37% and 0.42%, respectively, both explained by genomic regions on chromosome 2. For maternal effects on WW, the highest (1.38%) and lowest (0.41%) genetic variances were explained by genomic windows on chromosome 2. Further investigation of these regions identified several possible candidate genes associated with body weight. Gene ontology analysis using the DAVID database identified several functional terms, such as translation repressor activity, nucleic acid binding, dehydroascorbic acid transporter activity, growth factor activity and SH2 domain binding.

Definition of metafounders based on population structure analysis

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 matrixA Γ using the metafounder methodology. Improvements in consistency between matrix G andA 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 forA 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 resultingA 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.

Genetic evaluations and genome-wide association studies for specific digital dermatitis diagnoses in dairy cows considering genotype × housing system interactions

The present study aimed to use detailed phenotyping for the claw disorder digital dermatitis (DD) considering specific DD stages in 2 housing systems (conventional cubicle barns [CON] and compost-bedded pack barns [CBPB]) to infer possible genotype × housing system interactions. The DD stages included 2,980 observations for the 3 traits DD-sick, DD-acute, and DD-chronic from 1,311 Holstein-Friesian and 399 Fleckvieh-Simmental cows. Selection of the 5 CBPB and 5 CON herds was based on a specific protocol to achieve a high level of herd similarity with regard to climate, feeding, milking system, and location, but with pronounced housing-system differences. Five other farms had a "mixed system" with 2 subherds, one representing CBPB and the other one CON. The CBPB system was represented by 899 cows (1,530 observations), and 811 cows (1,450 observations) represented the CON system. The average disease prevalence was 20.47% for DD-sick, 13.88% for DD-acute, and 5.34% for DD-chronic, with a higher prevalence in CON than in CBPB. After quality control of 50K genotypes, 38,495 SNPs from 926 cows remained for the ongoing genomic analyses. Genetic parameters for DD-sick, DD-acute, and DD-chronic were estimated by applying single-step approaches for single-trait repeatability animal models considering the whole dataset, and separately for the CON and CBPB subsets. Genetic correlations between same DD traits from different housing systems, and between DD-sick, DD-chronic, and DD-acute, were estimated via bivariate animal models. Heritabilities based on the whole dataset were 0.16 for DD-sick, 0.14 for DD-acute, and 0.11 for DD-chronic. A slight increase of heritabilities and genetic variances was observed in CON compared with the "well-being" CBPB system, indicating a stronger genetic differentiation of diseases in a more challenging environment. Genetic correlations between same DD traits recorded in CON or CBPB were close to 0.80, disproving obvious genotype × housing system interactions. Genetic correlations among DD-sick, DD-acute and DD-chronic ranged from 0.58 to 0.81. SNP main effects and SNP × housing system interaction effects were estimated simultaneously via GWAS, considering only the phenotypes from genotyped cows. Ongoing annotations of potential candidate genes focused on chromosomal segments 100 kb upstream and downstream from the significantly associated candidate SNP. GWAS for main effects indicated heterogeneous Manhattan plots especially for DD-acute and DD-chronic, indicating particularities in disease pathogenesis. Nevertheless, a few shared annotated potential candidate genes, that is, METTL25, AFF3, PRKG1, and TENM4 for DD-sick and DD-acute, were identified. These genes have direct or indirect effects on disease resistance or immunology. For the SNP × housing system interaction, the annotated genes ASXL1 and NOL4L on BTA 13 were relevant for DD-sick and DD-acute. Overall, the very similar genetic parameters for the same traits in different environments and negligible genotype × housing system interactions indicate only minor effects on genetic evaluations for DD due to housing-system particularities.

How pedigree errors affect genetic evaluations and validation statistics

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

Unknown parent groups and truncated pedigree in single-step genomic evaluations of Murrah buffaloes

Missing pedigree may produce bias in genomic evaluations. Thus, strategies to deal with this problem have been proposed as using unknown parent groups (UPG) or truncated pedigrees. The aim of this study was to investigate the impact of modeling missing pedigree under ssGBLUP evaluations for productive and reproductive traits in dairy buffalos using different approaches: 1) traditional BLUP without UPG (BLUP), 2) traditional BLUP including UPG (BLUP/UPG), 3) ssGBLUP without UPG (ssGBLUP), 4) ssGBLUP including UPG in the A and A22 matrices (ssGBLUP/A_UPG), 5) ssGBLUP including UPG in all elements of the H matrix (ssGBLUP/H_UPG), 6) BLUP with pedigree truncation for the last 3 generations (BLUP/truncated), and 7) ssGBLUP with pedigree truncation for the last 3 generations (ssGBLUP/ truncated). UPGs were not used in the scenarios with truncated pedigree. A total of 3,717, 4,126 and 3,823 records of the first lactation for accumulated 305 d milk yield (MY), age at first calving (AFC) and lactation length (LL), respectively were used. Accuracies ranged from 0.27 for LL (BLUP) to 0.46 for MY (BLUP), bias ranged from -0.62 for MY (ssGBLUP) to 0.0002 for AFC (BLUP/truncated), and dispersion ranged from 0.88 for MY (BLUP/ A_UPG) to 1.13 for LL (BLUP). Genetic trend showed genetic gains for all traits across 20 years of selection and the impact of including either genomic information, UPG or pedigree truncation under GEBV accuracies ranged among the evaluated traits. Overall, methods using UPGs, truncation pedigree and genomic information exhibited potential to improve GEBV accuracies, bias and dispersion for all traits compared with other methods. Truncated scenarios promoted high genetic gains. In small populations with few genotyped animals, combining truncated pedigree or UPG with genomic information is a feasible approach to deal with missing pedigrees.

Estimating genomic relationships of metafounders across and within breeds using maximum likelihood, pseudo-expectation-maximization maximum likelihood and increase of relationships

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.

Development of an index that decreases birth weight, promotes postnatal growth and yet minimizes selection intensity in beef cattle

OBJECTIVE

The main goal of our current study was to improve the growth curve of meat animals by decreasing the birth weight while achieving a finishing weight that is the same as that before selection but at younger age.

METHODS

Random regression model was developed to derive various selection indices to achieve desired gains in body weight at target time points throughout the fattening process. We considered absolute and proportional gains at specific ages (in weeks) and for various stages (i.e., early, middle, late) during the fattening process.

RESULTS

The point gain index was particularly easy to use because breeders can assign a specific age (in weeks) as a time point and model either the actual weight gain desired or a scaled percentage gain in body weight.

CONCLUSION

The point gain index we developed can achieve the desired weight gain at any given postnatal week of the growing process and is an easy-to-use and practical option for improving the growth curve.

Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats

The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Genetic Background of Blood β-Hydroxybutyrate Acid Concentrations in Early-Lactating Holstein Dairy Cows Based on Genome-Wide Association Analyses

Ketosis is a common metabolic disorder in the early lactation of dairy cows. It is typically diagnosed by measuring the concentration of β-hydroxybutyrate (BHB) in the blood. This study aimed to estimate the genetic parameters of blood BHB and conducted a genome-wide association study (GWAS) based on the estimated breeding value. Phenotypic data were collected from December 2019 to August 2023, comprising blood BHB concentrations in 45,617 Holstein cows during the three weeks post-calving across seven dairy farms. Genotypic data were obtained using the Neogen Geneseek Genomic Profiler (GGP) Bovine 100 K SNP Chip and GGP Bovine SNP50 v3 (Illumina Inc., San Diego, CA, USA) for genotyping. The estimated heritability and repeatability values for blood BHB levels were 0.167 and 0.175, respectively. The GWAS result detected a total of ten genome-wide significant associations with blood BHB. Significant SNPs were distributed in Bos taurus autosomes (BTA) 2, 6, 9, 11, 13, and 23, with 48 annotated candidate genes. These potential genes included those associated with insulin regulation, such as INSIG2, and those linked to fatty acid metabolism, such as HADHB, HADHA, and PANK2. Enrichment analysis of the candidate genes for blood BHB revealed the molecular functions and biological processes involved in fatty acid and lipid metabolism in dairy cattle. The identification of novel genomic regions in this study contributes to the characterization of key genes and pathways that elucidate susceptibility to ketosis in dairy cattle.

Genetic gains underpinning a little-known strawberry Green Revolution

The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%. Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%. Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification. Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California.

Confidence intervals for validation statistics with data truncation in genomic prediction

BACKGROUND

Validation by data truncation is a common practice in genetic evaluations because of the interest in predicting the genetic merit of a set of young selection candidates. Two of the most used validation methods in genetic evaluations use a single data partition: predictivity or predictive ability (correlation between pre-adjusted phenotypes and estimated breeding values (EBV) divided by the square root of the heritability) and the linear regression (LR) method (comparison of "early" and "late" EBV). Both methods compare predictions with the whole dataset and a partial dataset that is obtained by removing the information related to a set of validation individuals. EBV obtained with the partial dataset are compared against adjusted phenotypes for the predictivity or EBV obtained with the whole dataset in the LR method. Confidence intervals for predictivity and the LR method can be obtained by replicating the validation for different samples (or folds), or bootstrapping. Analytical confidence intervals would be beneficial to avoid running several validations and to test the quality of the bootstrap intervals. However, analytical confidence intervals are unavailable for predictivity and the LR method.

RESULTS

We derived standard errors and Wald confidence intervals for the predictivity and statistics included in the LR method (bias, dispersion, ratio of accuracies, and reliability). The confidence intervals for the bias, dispersion, and reliability depend on the relationships and prediction error variances and covariances across the individuals in the validation set. We developed approximations for large datasets that only need the reliabilities of the individuals in the validation set. The confidence intervals for the ratio of accuracies and predictivity were obtained through the Fisher transformation. We show the adequacy of both the analytical and approximated analytical confidence intervals and compare them versus bootstrap confidence intervals using two simulated examples. The analytical confidence intervals were closer to the simulated ones for both examples. Bootstrap confidence intervals tend to be narrower than the simulated ones. The approximated analytical confidence intervals were similar to those obtained by bootstrapping.

CONCLUSIONS

Estimating the sampling variation of predictivity and the statistics in the LR method without replication or bootstrap is possible for any dataset with the formulas presented in this study.

A comparison of five sets of overlapping and non-overlapping sliding windows for semen production traits in the Thai multibreed dairy population

OBJECTIVE

This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population.

METHODS

The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows.

RESULTS

Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population.

CONCLUSION

This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.

GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values

BACKGROUND

Since the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP.

RESULTS

GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis.

CONCLUSIONS

GWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.

Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population

Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.

MAGE: metafounders-assisted genomic estimation of breeding value, a novel additive-dominance single-step model in crossbreeding systems

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.