Prediction of genomic breeding values for reproductive traits in Nellore heifers

Evaluation of the productive and reproductive performance of sexual precocity at different ages in Nellore heifers

This study aimed to investigate the differences between productive and reproductive performance traits of sexually precocious and non-sexually precocious Nellore heifers and to evaluate the genetic correlation of sexual precocity with traits of economic importance. For this purpose, 300,000 Nellore heifers were evaluated for reproductive traits: heifer pregnancy (HP) at 14 (HP14), 18 (HP18), and 24 (HP24) months; heifer rebreeding (HR); number of progenies up to 53 months (NP53); and probability of the cow remaining in the herd until 76 months with at least 3 progenies (Stay). The growth-related traits evaluated included female yearling weight (YW); average daily gain from weaning to yearling (ADGW-Y); weight at maturity (MW); weaning weight of first progeny (WWprog); and female visual scores at yearling for conformation (Conf), precocity (Prec) and muscling (Musc). The effects of female YW and ADGW-Y in six categories on HP14, HP18, and HP24 were analyzed using Generalized linear mixed models (GLMM). Furthermore, a linear mixed model was used to evaluate the impact of HP on WWprog, MW, and reproductive performance (NP53 and Stay). Genetic correlations of HP evaluated in different months with growth and reproductive traits were estimated using a bivariate animal model. Precocious heifers (HP14) were lighter for YW and MW but had greater ADGW-Y than HP18 and HP24. The probability for HP14, HP18, and HP24 increased as the classes of YW and ADGW-Y increased. However, heifers weighing more than 326 kg had a slight reduction in the probability of becoming pregnant at HP14 and HP18. Precocious heifers (HP14 and HP18) produced their first progeny by 3 % lighter than HP24, although they had a greater NP53. Precocious heifers at 18 months (HP18) were 3 % and 6.8 % more likely to remain in the herd than HP14 and HP24 heifers, respectively. Genetic correlations between growth traits (WW, YW, ADGW-Y, and MW) and heifer pregnancy (HP14, HP18, and HP24) ranged from weak (rg = 0.27 ± 0.05) to moderate (rg = -0.47 ± 0.07). The genetic correlation between HR and HP was stronger for HP24 (0.75) against HP14 (0.58) and HP18 (0.64). Although, the genetic correlation between NP53 and Stay with HP14 was higher (rg = 0.53 and 0.45) than those observed for HP18 (rg = 0.46 and 0.38) and HP24 (rg = 0.35 and 0.39). The genetic correlation estimates between HP and visual scores were moderate and favorable for HP14. Selecting HP14 is beneficial for production systems because it increases the NP53 during the productive life without compromising heifer productivity or reproductive performance. However, attention should be given to improving the HR of heifers who become pregnant early.

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

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

Genomic Predictions in Korean Hanwoo Cows: A Comparative Analysis of Genomic BLUP and Bayesian Methods for Reproductive Traits

This study aimed to predict the accuracy of genomic estimated breeding values (GEBVs) for reproductive traits in Hanwoo cows using the GBLUP, BayesB, BayesLASSO, and BayesR methods. Accuracy estimates of GEBVs for reproductive traits were derived through fivefold cross-validation, analyzing a dataset comprising 11,348 animals and employing an Illumina Bovine 50K SNP chip. GBLUP showed an accuracy of 0.26 for AFC, while BayesB, BayesLASSO, and BayesR demonstrated values of 0.28, 0.29, and 0.29, respectively. For CI, GBLUP attained an accuracy of 0.19, whereas BayesB, BayesLASSO, and BayesR scored 0.21, 0.24, and 0.25, respectively. The accuracy for GL was uniform across GBLUP, BayesB, and BayesR at 0.31, whereas BayesLASSO showed a slightly higher accuracy of 0.33. For NAIPC, GBLUP showed an accuracy of 0.24, while BayesB, BayesLASSO, and BayesR recorded 0.22, 0.27, and 0.30, respectively. The variation in genomic prediction accuracy among methods indicated Bayesian approaches slightly outperformed GBLUP. The findings suggest that Bayesian methods, notably BayesLASSO and BayesR, offer improved predictive capabilities for reproductive traits. Future research may explore more advanced genomic approaches to enhance predictive accuracy and genetic gains in Hanwoo cattle breeding programs.

Comparison of methods for predicting genomic breeding values for growth traits in Nellore cattle

The objective of this study was to evaluate the accuracy of genomic predictions of growth traits in Nellore cattle. Data from 5064 animals belonging to farms that participate in the Conexão DeltaGen and PAINT breeding programs were used. Genotyping was performed with the Illumina BovineHD BeadChip (777,962 SNPs). After quality control of the genomic data, 412,993 SNPs were used. Deregressed EBVs (DEBVs) were calculated using the estimated breeding values (EBVs) and accuracies of birth weight (BW), weight gain from birth to weaning (GBW), postweaning weight gain (PWG), yearling height (YH), and cow weight (CW) provided by GenSys. Three models were used to estimate marker effects: genomic best linear unbiased prediction (GBLUP), BayesCπ, and improved Bayesian least absolute shrinkage and selection operator (IBLASSO). The prediction ability of genomic estimated breeding value (GEBVs) was estimated by the average Pearson correlation between DEBVs and GEBVs, predicted with the different methodologies in the validation populations. The regression coefficients of DEBVs on GEBVs in the validation population were calculated and used as indicators of prediction bias of GEBV. In general, the Bayesian methods provided slightly more accurate predictions of genomic breeding values than GBLUP. The BayesCπ and IBLASSO were similar for all traits (BW, GBW, PWG, and YH), except for CW. Thus, there does not seem to be a more suitable method for the estimation of SNP effects and genomic breeding values. Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.

Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations

Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc (n = 6,649), Landrace (n = 5,362), and Yorkshire (n = 5,008) breeds, respectively. The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.

Prediction of genomic breeding values of milk traits in Brazilian Saanen goats

The study's objective was to compare the genomic prediction ability methods for the traits milk yield, milk composition and somatic cell count of Saanen Brazilian goats. Nine hundred forty goats, genotyped with an Axiom_OviCap (Caprine) panel, Affimetrix customized array with 62,557 single nucleotide polymorphisms (SNPs), were used for the genomic selection analyses. The genomic methods studied to estimate the effects of SNPs and direct genomic values (DGV) were as follows: (a) genomic BLUP (GBLUP), (b) Bayes Cπ and (c) Bayesian Lasso (BLASSO). Estimated breeding values (EBV) and deregressed estimated breeding values (dEBV) were used as response variables for the genomic predictions. The prediction ability was assessed by Pearson's correlation between DGV and response variables (EBV and dEBV). Regression coefficients of the response variables on the DGV were obtained to verify if the genomic predictions were biased. In addition, the mean square error of prediction (MSE) was used as a measure of verification of model fit to the data. The means of prediction accuracy, when EBV was used as a response variable, were 0.68, 0.68 and 0.67 for GBLUP, Bayes Cπ and BLASSO, respectively. With dEBV, the mean prediction accuracy was 0.50 for all models. The averages of the EBV regression coefficients on DGV were 1.08 for all models (GBLUP, Bayes Cπ and BLASSO), higher than those obtained for the regression coefficient of dEBV on DGV, which presented values of 1.05, 1.05 and 1.08 for GBLUP, Bayes Cπ and BLASSO, respectively. None of the methods stood out in terms of prediction ability; however, the GBLUP method was the most appropriate for estimating the DGV, in a slightly more reliable and less biased way, besides presenting the lowest computational cost. In the context of the present study, EBV was the preferred response variables considering the genomic prediction accuracy despite dEBV also presented lower bias.

Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo

Bovine and buffalo are important livestock species that have contributed to human lives for more than 1000 years. Improving fertility is very important to reduce the cost of production. In the current review, we classified reproductive traits into three categories: ovulation, breeding, and calving related traits. We systematically summarized the heritability estimates, molecular markers, and genomic selection (GS) for reproductive traits of bovine and buffalo. This review aimed to compile the heritability and genome-wide association studies (GWASs) related to reproductive traits in both bovine and buffalos and tried to highlight the possible disciplines which should benefit buffalo breeding. The estimates of heritability of reproductive traits ranged were from 0 to 0.57 and there were wide differences between the populations. For some specific traits, such as age of puberty (AOP) and calving difficulty (CD), the majority beef population presents relatively higher heritability than dairy cattle. Compared to bovine, genetic studies for buffalo reproductive traits are limited for age at first calving and calving interval traits. Several quantitative trait loci (QTLs), candidate genes, and SNPs associated with bovine reproductive traits were screened and identified by candidate gene methods and/or GWASs. The IGF1 and LEP pathways in addition to non-coding RNAs are highlighted due to their crucial relevance with reproductive traits. The distribution of QTLs related to various traits showed a great differences. Few GWAS have been performed so far on buffalo age at first calving, calving interval, and days open traits. In addition, we summarized the GS studies on bovine and buffalo reproductive traits and compared the accuracy between different reports. Taken together, GWAS and candidate gene approaches can help to understand the molecular genetic mechanisms of complex traits. Recently, GS has been used extensively and can be performed on multiple traits to improve the accuracy of prediction even for traits with low heritability, and can be combined with multi-omics for further analysis.

Genomic evaluation for novel stayability traits in Nellore cattle

Cow stayability plays a major role on the overall profitability of the beef cattle industry, as it is directly related to reproductive efficiency and cow's longevity. Stayability (STAY63) is usually defined as the ability of the cow to calve at least three times until 76 months of age. This is a late-measured and lowly heritable trait, which consequently constrains genetic progress per time unit. Thus, the use of genomic information associated with novel stayability traits measured earlier in life will likely result in higher prediction accuracy and faster genetic progress for cow longevity. In this study, we aimed to compare pedigree-based and single-step GBLUP (ssGBLUP) methods as well as to estimate genetic correlations between the proposed stayability traits: STAY42, STAY53 and STAY64, which are measured at 52, 64 and 76 months of cow's age, considering at least 2, 3 and 4 calving, respectively. ssGBLUP yielded the highest prediction accuracy for all traits. The heritability estimates for STAY42, STAY53, STAY63 and STAY64 were 0.090, 0.151, 0.152 and 0.143, respectively. The genetic correlations between traits ranged from 0.899 (STAY42 and STAY53) to 0.985 (STAY53 and STAY63). The high genetic correlation between STAY42 and STAY53 suggests that besides being related to cow longevity, STAY53 is also associated with the early-stage reproductive efficiency. Thus, STAY53 is recommended as a suitable selection criterion for reproductive efficiency due to its higher heritability, favourable genetic correlation with other traits, and measured earlier in life, compared with the conventional stayability trait, that is STAY63.