Comparison of various models for the estimation of heritability and breeding values.

The exploration of additive genetic variance for the selection of animals is the central paradigm in quantitative genetics and it is important to use appropriate animal models considering important factors. This study compares various factor effects for heritability and breeding values estimations on data collected on the Corriedale. Overall, the heritability estimates were the highest for birthweight (BW). Both the additive genetic variance and the heritability values were reduced upon adding more random effects. Year-effect as random reduced the heritability except for BW. The highest average accuracy for breeding values for birth, weaning, 6, 9, and 12-month-weights for were 0.656, 0.567, 0.519, 0.486, and 0.515. These were observed for models having sex or sex and parity as fixed effects. It is concluded that fixed and random factors have a significant impact on genetic-factors and breeding-value accuracies and should be considered during selection programs and make the selection program more effective.

Morphometric indices of native sheep breeds of the Himalayan region of India using multivariate principal component analysis.

This study was performed to analyze the morphometric traits and indices in 3000 animals of five registered sheep breeds in the Himalayan region under a multivariate approach. Data were recorded under field conditions with equal coverage of the five breeds, viz., Karnah, Gurez, Poonchi, Bakerwal and Changthangi on body length (BL), height at withers (HW), chest girth (CG), ear length (EL), and tail length (TL). Furthermore, four derived traits (indices) were studied, which included an index of body frame (IBF), an index of thorax development (ITD), a Baron-Crevat index (BCI), and an index of body weight (IBW). Multivariate principal component analysis (PCA) was undertaken on nine morphometric traits. Kaiser's criterion was used to reduce the number of principal components for further analysis and interpretation. The adequacy of sampling was evaluated using Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. The mean BL ranged from 52.15 (Changthangi) to 71.13 (Gurez). The estimates of HW, CG, EL and TL were highest in Gurez (63.49), Bakerwal (84.82), Bakerwal (7.26), and Karnah (8.18) breeds, respectively. Among the derived traits, the highest IBF was observed in the Gurez breed with an estimate of 112.22. Upon multivariate PCA on the dataset, the first four principal components were able to explain 92.117% of the total variance. The KMO test, Bartlett's test of sphericity and estimated communalities showed the appropriateness of PCA on the evaluated traits. Four eigenvalues were greater than one and were extracted for further analysis. Morphometric traits were highly correlated, except for EL and TL that showed lower correlation estimates with other traits. The Changthangi population showed the lowest estimates of BL, HW, CG and rectangular body frame. The present study ascertained important morphometric traits/indices that can help in developing selection criteria and formulating sustainable breeding and conservation plans vis-à-vis the unique sheep breeds of the temperate Himalayas.

Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 12 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through principal component analysis (PCA) was 1. The correlation coefficients between true and predicted values for artificial neural networks, Bayesian ridge regression, classification and regression trees, gradient boosting algorithm, K nearest neighbours, multivariate adaptive regression splines (MARS) algorithm, polynomial regression, principal component regression (PCR), random forests, support vector machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. Among the prediction equations generated using OLS, the highest coefficient of determination was 0.569. A total of 12 machine learning models were developed from the prediction of breeding values in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies for breeding value prediction.

Multivariate quantitative genetic analysis of body weight traits in Corriedale sheep.

In the present study, an attempt was made to elucidate the genetic parameters for body weight traits of lambs from Corriedale sheep population at different ages. Data were collected from 6874 lambs born over a span of 49 years from 1969 to 2017. The traits under study included body weight at birth (BW), weaning (WW), 6 months of age (6MW), 9 months of age (9MW) and yearling stage (YW). Data were statistically analyzed using restricted maximum likelihood (REML) algorithm in WOMBAT program. A multi-variate animal model was fitted to the data incorporating season and period of lambing, sex of lamb and litter size as fixed effects. Variance and covariance components were estimated using the animal model after incorporating direct additive genetic effect of animal as random factor. Genetic and phenotypic correlations with corresponding standard errors were also estimated. The heritability estimates for BW, WW, 6MW, 9MW and YW were 0.130 ± 0.023, 0.300 ± 0.029, 0.292 ± 0.030, 0.191 ± 0.025 and 0.169 ± 0.024, respectively. The genetic correlation between different traits under study was high, except between BW and 9MW for which the estimate was moderate. Phenotypic correlation ranged from low to high for different trait combinations. Among different traits under study, only two traits showed moderate heritability i.e. WW and 6MW while heritability of other traits was low. Both these traits showed high correlation with all subsequent traits. Selection programme for Corriedale sheep should be based on WW which is expressed early in life and shall lead to moderate genetic response to selection.

Estimation of (Co)Variance components and genetic parameters of fibre traits in Rambouillet sheep using multi-trait analysis.

The present study aimed to estimate the genetic parameters of different fibre traits, viz., greasy fleece weight, staple length, and fibre diameter in Rambouillet sheep population using a multi-trait animal model. Data, spanning over 10 years (1998-2007) and pertaining to fibre traits at first clip, were collected for a total of 4186 Rambouillet sheep maintained at an organized farm. (Co)Variance structure and genetic parameters were estimated using a multi-trait animal model. The genetic analysis of data was performed based on restricted maximum likelihood (REML) procedure using WOMBAT software. The model incorporated sex of lamb (i = 1, 2), year of birth (j = 1-10), season of birth (k = 1-2), and litter size (l = 1-2) as fixed effects while direct additive genetic and maternal genetic effects were included as random effects. The direct additive genetic heritability estimates were 0.120±0.034, 0.136±0.037, and 0.356±0.070 for greasy fleece weight, staple length, and fibre diameter, respectively. The maternal genetic heritability of all fibre traits under study was very low. Additive genetic correlation was positive and low between greasy fleece weight and staple length; and between staple length and fibre diameter. In conclusion, fibre diameter was moderately heritable which implies that selection may lead to moderate improvement in this trait. The results from the present study will help in formulating optimal breeding plans for improvement of fibre traits in Rambouillet sheep.

Zingerone prevents lead-induced toxicity in liver and kidney tissues by regulating the oxidative damage in Wistar rats.

Among the heavy metal poisonings, lead is considered as a major toxic metal causing hematological, neurological, immunological, hepatic, and renal dysfunctions. Lead causes inhibition of ALAD leading to the ALA accumulation inside the cells. Lead also leads to disruption of the anti-oxidative enzyme system, organ function, and lipid membranes of the cell causing oxidative stress. Zingerone, a phenolic alkanone, is an active edible ingredient present in the ginger that possess varied pharmacological properties. The aim of our study was to evaluate the protective effect of zingerone in lead-induced toxicity in wistar rats. ALAD concentration was improved in kidney and liver tissues treated with zingerone. Protective effect of zingerone was observed in terms of significant improvement in kidney and liver histology, anti-oxidant enzyme activity (CAT, SOD, GPx, and GR), organ function parameters, lipid profile, and decreased level of LPO. Therefore, zingerone pretreatment can be a promising agent for alleviation of lead-induced oxidative damage in cells. PRACTICAL APPLICATIONS: Published reports have revealed that consumption of certain bioactive nutrients for example, flavonoids, mineral elements, and vitamins can offer defense from the environmental lead contamination. Zingerone is a strong anti-oxidant, with very less side effects and has exceptional property of scavenging free radicals, hence reducing the oxidative stresses. This fundamental property of zingerone can alone help in countering the heavy metal toxicity. Different groups have published reported numerous properties of zingerone but as per our understanding till date no study about alleviation of lead toxicity by zingerone in animal model has been undertaken. Hence, we conducted this research to explore the preventive effect of zingerone in lead induced kidney and liver toxicity. The outcome of our study shows potent anti-oxidant effect and ALAD modulatory property of zingerone which makes it suitable edible candidate for use in countering lead toxicity.

Comparative transcriptome analysis reveals the genetic basis of coat color variation in Pashmina goat.

The genetics of coat color variation remains a classic area. Earlier studies have focused on a limited number of genes involved in color determination; however, the complete set of trait determinants are still not well known. In this study, we used high-throughput sequencing technology to identify and characterize intricate interactions between genes that cause complex coat color variation in Changthangi Pashmina goats, producer of finest and costly commercial animal fiber. We systematically identified differentially expressed mRNAs and lncRNAs from black, brown and white Pashmina goat skin samples by using RNA-sequencing technique. A pairwise comparison of black, white and brown skin samples yielded 2479 significantly dysregulated genes (2422 mRNA and 57 lncRNAs). Differentially expressed genes were enriched in melanin biosynthesis, melanocyte differentiation, developmental pigmentation, melanosome transport activities GO terms. Our analysis suggested the potential role of lncRNAs on color coding mRNAs in cis and trans configuration. We have also developed online data repository as a component of the study to provide a central location for data access, visualization and interpretation accessible through http://pcd.skuastk.org/ .