Parent of origin genetic effects on milk production traits in a population of Iranian Holstein cows.

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability ( h mi 2 $$ {h}_{\mathrm{mi}}^2 $$ ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability ( h a 2 $$ {h}_{\mathrm{a}}^2 $$ ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (ra) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (rp) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

Relative contribution of Imprinting, X chromosome and Litter effects to phenotypic variation in economic traits of sheep.

Data on Zandi sheep were analysed to quantify maternal and paternal imprinting, X chromosome and litter effects' contribution to phenotypic variation in birth weight (BW), weaning weight (WW), growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR). To this end, a two-step approach was adopted. In the first step, each trait was analysed with a series of 16 animal models, which were identical for fixed and autosomal additive genetic effects but differed for combinations of maternal permanent environmental, maternal genetic, X chromosome and litter effects. For each trait, the best model was selected by the Akaike information criterion (AIC) and likelihood ratio tests (LRTs). In the second step, three additional models were fitted by adding maternal imprinting, paternal imprinting or both (models 17, 18 and 19) to the best model selected in the first step. Estimators of bias, dispersion and accuracy of breeding values estimated within 19 models with whole, and partial data were used to evaluate how well were the 19 models in estimating breeding values for the animals when their records were masked. For all traits studied, fitting the litter effect led to a better data fit. Also, it resulted in noticeable decreases in residual variance and other maternal variances. For growth traits, models containing the X-linked effects fitted the data substantially better than corresponding models without the X-linked effects. For BW, WW and GR, estimates of X-linked heritability ( h s 2 ) ranged between 0.09 (GR) and 0.14 (BW). Ignoring X-linked effects from the genetic evaluation model resulted in significant inflated autosomal additive genetic variance. For BW, WW, EF and RGR, models containing the imprinting effects provided a better fit of the data than otherwise identical models. Imprinting effects contributed significantly to the phenotypic variation of these traits in a range between 5% (RGR) and 8% (BW, WW). A sharp decline was observed in autosomal additive genetic variance following including imprinting effects in the model (27% to 40% depending on the trait). The least bias and dispersion, as well as greater accuracies for breeding values of focal animals, were for a model which included imprinting, X-linked and litter effects. It was concluded that imprinting, X-linked and litter effects need to be included in the genetic evaluation models for growth and efficiency-related traits of Zandi lambs.

Genetic evaluation of age at first calving and days open under different censoring scenarios in Iranian Holstein cows.

The present study aimed to investigate the effect of censoring, the situations in which incomplete at the time, out of range, and/or delayed records were considered, in the estimation of genetic parameters for age at first calving (AFC) and days open (DO) in Iranian Holstein cows. The dataset included 281,772 records collected from 1991 to 2019 by the Vahdat Cooperative Company, a pioneer dairy farm in Isfahan Province, the central part of Iran. Five animal models including linear model (LM), penalty model (PM), modified penalty model (MPM), linear-threshold model (LTM), and modified linear-threshold model (MLTM) were used for genetic evaluation of the trait studied. The predictive ability of the models was assessed using cross-validation. The lowest mean square of error and highest r(y,y) were obtained under MLTM for AFC and under LTM for DO, indicating that MLTM and LTM are recommended for genetic evaluation of AFC and DO with censored records in Iranian Holstein cows, respectively. The prediction accuracy of the models for AFC was relatively similar, ranging from 0.46 (under LM) to 0.48 (under PM, LTM, and MLTM). For DO, prediction accuracy values ranged from 0.36 (under LM) to 0.47 (under PM and LTM). The posterior mean for heritability of AFC under MLTM was 0.11. There was no significant difference among posterior means for the heritability of AFC under different models. Therefore, LM is preferred for genetic evaluation of AFC in Iranian Holsteins, and taking censored records into account is unnecessary. The posterior mean for heritability of DO under LTM was 0.09. There were no statistically significant differences among the heritability estimates of DO under LTM, PM, and MLTM. But considering censored records for genetic evaluation of DO affects the estimation of heritability and improved model accuracy for this trait. Therefore, LTM is preferred and recommended for genetic evaluation of DO in Iranian Holsteins.

Evaluation of non-linear models for genetic parameters estimation of growth curve traits in Kermani sheep.

In the present study, 10,116 body weight-age records were measured on 2537 Kermani lambs. The records were collected from Kermani Sheep Breeding Station, located in Shahrbabak city, Kerman Province, south-eastern part of Iran, between 1993 and 2013 and used for evaluation of non-linear models describing growth curve from birth to yearling age and estimation of genetic parameters for growth curve traits in Kermani sheep. Six non-linear models including Brody, negative exponential, von Bertalanffy, Richards, Verhulst, and Gompertz were compared applying Akaike's information criterion (AIC), root mean square error (RMSE) and Durbin-Watson statistic (DW) for determining the most appropriate model describing the growth curve in Kermani sheep. The von Bertalanffy model showed the lowest AIC and RMSE among the tested models. Furthermore, positive autocorrelations were found between residuals under the all tested model with the lowest value under the von Bertalanffy model. Therefore, von Bertalanffy model was selected as the best one for describing growth curve in Kermani sheep. A multivariate animal model was used for genetic analysis of the growth curve traits including parameters A (estimated mature weight), B (an integration constant related to initial animal weight), K (maturation rate), inflection age (IA), and inflection weight (IW) under a Bayesian approach. Posterior means for heritability estimates of A, B, K, IA, and IW were significant values of 0.10, 0.03, 0.04, 0.15, and 0.10, respectively. The parameter A had significant and positive genetic and phenotypic correlations with parameters B, IA, and IW. The posterior means for genetic and phenotypic correlations between parameters A and K were negative estimates of - 0.32 and - 0.22, respectively, implied that the lambs with slower maturation rate had higher mature weight. Positive and medium estimates were obtained for posterior means of phenotypic (0.31) and genetic (0.35) correlations between B and K. The posterior means for phenotypic and genetic correlations of B with IA and IW were not statistically significant. High and positive estimates were obtained for posterior means of genetic (0.6) and phenotypic (0.84) correlations between IA and IW. Generally, von Bertalanffy model showed high level of adequacy for describing the growth curve in Kermani sheep. Low additive genetic variations were found for all the studied growth curve traits. Therefore, the traits highly influenced by environmental which necessitate improving environmental influencing factors on the studied traits for achieving desired shape of growth curve and developing an efficient breeding strategy in Kermani sheep.

Identification of Hub Genes and Target miRNAs Crucial for Milk Production in Holstein Friesian Dairy Cattle.

Dairy milk production is a quantitative trait that is controlled by many biological and environmental factors. This study employs a network-driven systems approach and clustering algorithm to uncover deeper insights into its genetic associations. We analyzed the GSE33680 dataset from the GEO database to understand the biological importance of milk production through gene expression and modules. In this study, we employed CytoNCA and ClusterONE plugins within Cytoscape for network analysis. Moreover, miRWalk software was utilized to detect miRNAs, and DAVID was employed to identify gene ontology and pathways. The results revealed 140 up-regulated genes and 312 down-regulated genes. In addition, we have identified 91 influential genes and 47 miRNAs that are closely associated with milk production. Through our examination of the network connecting these genes, we have found significant involvement in important biological processes such as calcium ion transit across cell membranes, the BMP signaling pathway, and the regulation of MAPK cascade. The conclusive network analysis further reveals that GAPDH, KDR, CSF1, PYGM, RET, PPP2CA, GUSB, and PRKCA are closely linked to key pathways essential for governing milk production. Various mechanisms can control these genes, making them valuable for breeding programs aiming to enhance selection indexes.