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This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low‐density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross‐validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low‐density and customized arrays using Markov blanket might be cost‐effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale. [ABSTRACT FROM AUTHOR]

Background: Nellore cows are well adapted to tropical conditions, and they have good maternal ability as well as long and prolific reproductive life. Objective: to estimate (co)variances and genetic parameters for calving interval (CI), age at first calving (AFC), gestation length (GL), and days open (DO) in Nellore cows. Methods: covariance components and genetic parameters were estimated using multi-trait Bayesian procedures. Results: three traits had low but statistically significant heritabilities, averaging 0.05, 0.10, and 0.04 for CI, GL, and DO, respectively, whereas age at first calving had a higher heritability (0.36). The permanent environmental effects for CI, GL, and DO were also low, averaging 0.08, 0.07, and 0.15, respectively. The genetic correlations between AFC and CI, AFC and GL, AFC and DO, GL and CI, CI and DO, GL and DO were 0.20, 0.12, 0.11, 0.02, 0.92, and -0.21, respectively. Selection for shorter CI would contribute towards decreasing DO. However, selection for decreased GL could result in a greater number of DO. Despite the favorable genetic correlations, the direct selection responses for these traits would be low. Conclusion: reproductive traits are strongly influenced by environmental effects. Changes in management and environmental factors could rapidly improve reproductive performance of Polled Nellore herds. Genetic selection for these traits should produce a much slower but permanent response. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência Agronômica is the property of Revista Ciencia Agronomica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ciência Agronômica is the property of Revista Ciencia Agronomica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Components of (co)variance and genetic parameters were estimated for adjusted weights at ages 120 (W120), 240 (W240), 365 (W365) and 450 (W450) days of Polled Nellore cattle raised on pasture and born between 1987 and 2010. Analyses were performed using an animal model, considering fixed effects: herd-year-season of birth and calf sex as contemporary groups and the age of cow as a covariate. Gibbs Samplers were used to estimate (co)variance components, genetic parameters and additive genetic effects, which accounted for great proportion of total variation in these traits. High direct heritability estimates for the growth traits were revealed and presented mean 0.43, 0.61, 0.72 and 0.67 for W120, W240, W365 and W450, respectively. Maternal heritabilities were 0.07 and 0.08 for W120 and W240, respectively. Direct additive genetic correlations between the weight at 120, 240, 365 and 450 days old were strong and positive. These estimates ranged from 0.68 to 0.98. Direct-maternal genetic correlations were negative for W120 and W240. The estimates ranged from −0.31 to −0.54. Estimates of maternal heritability ranged from 0.056 to 0.092 for W120 and from 0.064 to 0.096 for W240. This study showed that genetic progress is possible for the growth traits we studied, which is a novel and favorable indicator for an upcoming and promising Polled Zebu breed in Tropical regions. Maternal effects influenced the performance of weight at 120 and 240 days old. These effects should be taken into account in genetic analyses of growth traits by fitting them as a genetic or a permanent environmental effect, or even both. In general, due to a medium-high estimate of environmental (co)variance components, management and feeding conditions for Polled Nellore raised at pasture in tropical regions of Brazil needs improvement and growth performance can be enhanced. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study was to identify traits of socio-economic relevance for intensive and semi-intensive dairy goat production system, to derive economic weights for the breeding goals and to propose selection indices whose criteria are easily collected by dairy goat breeders. The economic value of each trait was calculated as the difference between the average profit before and after the improvement of criteria, after increasing each trait by 1%, keeping the average of other traits unchanged. Eight selection indices were proposed. Four indices (I–IV) were determined for the intensive system and four (V–VIII) for the semi-intensive system. The traits included in each index were: milk production (MP) and lactation length (LL) (I and V); MP, LL, and age at first kidding (AFC) (II and VI); MP, LL, AFC and kidding interval (CI) (III and VII); and MP, LL, AFC, CI, somatic cell count (SCC) and total solids (TS) (IV and VIII). The average profit of the semi-intensive system was higher than the intensive system, R$ 0.18 and R$ 0.14, respectively. The use of indices III and VI promoted simultaneous improvement in both productive and reproductive traits. With differential payment for better quality milk, TS and SCC levels, use of indices IV and VIII are indicated. The choice and use of these indices depend on the definition of objectives and of the measurement ease of selection criteria. [Copyright &y& Elsevier]

• Four different networks among tick infestation, B. bovis infection level, weight gain from birth to weaning, and weight gain from weaning to yearling were compared. • There is a potential causal relationship between tick count and weight gain from birth to weaning. • There is a negative causal effect of pre-weaning gain on post-weaning gain. Tick infestation and associated diseases (i.e., babesiosis) constitute major drawbacks for improvement of beef cattle productivity in the tropics, mainly when purebred and crossbred taurine animals are used. Host-parasite-pathogen interactions form complex biological systems that are poorly understood and which significantly affect production and quality traits in ways yet to be dissected and described. This research was carried out to investigate potential causal relationships, through the use of structural equation modeling (SEM), among tick counts (TC), Babesia bovis infection level (IB) and the gains in weight: from birth to adjusted weaning age (WG), and from weaning to yearling (YG). Statistical analyses were conducted in three steps: 1) Partition of (co)variances into genetic and residual components using Bayesian multiple-trait modeling (MTM); of 2) Search for plausible causal structures by applying the inductive causation (IC) algorithm to the residual (co)variances obtained in the first step; and 3) Final analysis using SEM, which was based on the causal network learned from the IC algorithm. The most plausible results comprised three direct links between traits: WG→YG, TC→WG, and WG→IB with structural coefficients posterior means equal to -0.3026, 6.3620, and 0.0004, respectively. The final inferred directed acyclic graph (DAG) suggests that interventions on TC would directly affect WG, which would then affected YG; moreover, WG could also present a small positive effect on IB. [ABSTRACT FROM AUTHOR]

The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P<5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males. [ABSTRACT FROM AUTHOR]

Highlights • Reproductive traits showed a low correlation with carcass traits. • The results of this study are encouraging for using the probability of precocious calving as selection criteria in Nelore breed. • Selection to increase growth should increase carcass yield and scrotal circumference. • The inclusion of subcutaneous backfat or rump fat thickness in selection indexes would increase the carcass grade. Abstract This study was carried out to estimate the heritability and genetic correlations between adjusted weights at 210 (W210) and 450 (W450) days of age, scrotal circumference adjusted at 365 (SC365) and 450 (SC450) days of age, age at first calving (AFC), probability of precocious calving (PPC), stayability (STAY), ribeye area (REA), subcutaneous backfat thickness (BF), and rump fat (RF) traits. The covariance components and genetic parameters for all traits but PPC and STAY were estimated considering a linear animal model, for the remaining traits a threshold animal model was used. The single-step genomic BLUP (ssGBLUP) procedure was used for all traits, and the correlation estimates were obtained by two-traits analysis. The heritability estimated by single-trait analysis were 0.21 ± 0.01, 0.43 ± 0.01, 0.47 ± 0.02, 0.52 ± 0.02, 0.11 ± 0.01, 0.12 (0.07–0.16), 0.37 (0.26–0.38), 0.33 ± 0.02, 0.17 ± 0.01 and 0.33 ± 0.02 for W210, W450, SC365, SC450, AFC, PPC, STAY, REA, BF and RF, respectively. Growth traits (W210 and W450) showed high (0.95) genetic correlation with one another, moderate (0.30 to 0.66) with REA and scrotal circumferences, and low (−0.07 to 0.20) with female reproductive traits (AFC, PPC, and STAY) and fat carcass indicator traits (BF and RF). Female reproductive traits displayed moderate (−0.29 to 0.52) genetic correlations with male reproductive traits, and varied from low to moderate (−0.02 to 0.35) with carcass traits. Additionally, carcass traits exhibited low to moderate (−0.02 to 0.48) genetic correlations with all traits, except for the genetic correlation between BF and RF (0.73). Hence, higher genetic gains by direct selection would be obtained for W450, SC365, SC450, STAY, REA, and RF. Selection to increase growth traits might increase carcass yield and scrotal circumference, and it would not affect carcass fat content, precocity, and cow longevity indicator traits. The results of this study encourage the use of PPC in Nelore cattle since the selection for such trait would improve the productivity and longevity of the cows. The inclusion of carcass quality indicator traits such as BF or RF in selection indices would increase the genetic gain for carcass grade, but it will require further studies to estimate the economic value of these traits. [ABSTRACT FROM AUTHOR]

This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits (CT1 and CT2) measured in purebred broiler chickens. The MT models also included information on indicator traits measured in vivo [Growth and feed efficiency trait (FE)]. We proposed an approach termed (quasi) multitask SVR (QMTSVR), with hyperparameter optimization performed via genetic algorithm. ST and MT Bayesian shrinkage and variable selection models [genomic best linear unbiased predictor (GBLUP), BayesC (BC), and reproducing kernel Hilbert space (RKHS) regression] were employed as benchmarks. MT models were trained using 2 validation designs (CV1 and CV2), which differ if the information on secondary traits is available in the testing set. Models' predictive ability was assessed with prediction accuracy (ACC; i.e. the correlation between predicted and observed values, divided by the square root of phenotype accuracy), standardized root-mean-squared error (RMSE*), and inflation factor (b). To account for potential bias in CV2-style predictions, we also computed a parametric estimate of accuracy (ACCpar). Predictive ability metrics varied according to trait, model, and validation design (CV1 or CV2), ranging from 0.71 to 0.84 for ACC, 0.78 to 0.92 for RMSE*, and between 0.82 and 1.34 for b. The highest ACC and smallest RMSE* were achieved with QMTSVR-CV2 in both traits. We observed that for CT1, model/validation design selection was sensitive to the choice of accuracy metric (ACC or ACCpar). Nonetheless, the higher predictive accuracy of QMTSVR over MTGBLUP and MTBC was replicated across accuracy metrics, besides the similar performance between the proposed method and the MTRKHS model. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike-slab multivariate priors., Competing Interests: Conflicts of interest The author(s) declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P &lt; 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Anterior cruciate ligament (ACL) rupture is a common, debilitating condition that leads to early-onset osteoarthritis and reduced quality of human life. ACL rupture is a complex disease with both genetic and environmental risk factors. Characterizing the genetic basis of ACL rupture would provide the ability to identify individuals that have high genetic risk and allow the opportunity for preventative management. Spontaneous ACL rupture is also common in dogs and shows a similar clinical presentation and progression. Thus, the dog has emerged as an excellent genomic model for human ACL rupture. Genome-wide association studies (GWAS) in the dog have identified a number of candidate genetic variants, but research in genomic prediction has been limited. In this analysis, we explore several Bayesian and machine learning models for genomic prediction of ACL rupture in the Labrador Retriever dog. Our work demonstrates the feasibility of predicting ACL rupture from SNPs in the Labrador Retriever model with and without consideration of non-genetic risk factors. Genomic prediction including non-genetic risk factors approached clinical relevance using multiple linear Bayesian and non-linear models. This analysis represents the first steps toward development of a predictive algorithm for ACL rupture in the Labrador Retriever model. Future work may extend this algorithm to other high-risk breeds of dog. The ability to accurately predict individual dogs at high risk for ACL rupture would identify candidates for clinical trials that would benefit both veterinary and human medicine., (Copyright © 2020 Baker et al.)