Study of Population Structure and Genetic Prediction of Buffalo from Different Provinces of Iran using Machine Learning Method

Considering breeding livestock programs to milk production and type traits based on existence two different ecotypes of Iranian’s buffalo, a study carried out to investigate the population structure of Iranian buffalo and validate its classification accuracy according to different ecotypes from Iran (Azerbaijan and North) using data SNP chip 90K by means Support vector Machine (SVM), Random Forest (RF) and Discriminant Analysis Principal Component (DAPC) methods. A total of 258 buffalo were sampled and genotyped. The results of admixture, multidimensional scaling (MDS), and DAPC showed a close relationship between the animals of different provinces. Two ecotypes indicated higher accuracy of 96% that the Area Under Curve (AUC) confirmed the obtained result of the SVM approach while the DAPC and RF approach demonstrated lower accuracy of 88% and 80 %, respectively. SVM method proved high accuracy compared with DAPC and RF methods and assigned animals to their herds with more accuracy. According to these results, buffaloes distributed in two different ecotypes are one breed, and therefore the same breeding program should be used in the future. The water buffalo ecotype of the northern provinces of Iran and Azerbaijan seem to belong to the same population

Genetic analysis of semen from different origins and their impact on production traits: A single and multiple trait approach

This study aims to evaluate the genetics of imported semen and assess the genetic trend of production traits in Holstein cows during their first lactation in Iran. The data was collected from 28 different herds in Isfahan province between 2011 and 2020. Variance-covariance components were estimated by the restricted maximum likelihood method and the single and multi-trait animal model. The correlation between breeding values for bulls reported in catalogs and estimated in this was calculated. The mean of the least squares by countries shows that the daughters of Spanish bulls have the highest average for milk production, and the daughters of German, French, Spanish, and American bulls have the highest average percentage of fat and protein and the amount of fat and protein, respectively. Estimated heritability for milk production, fat, and protein percentage, and the amount of fat and protein were 0.34±0.011, 0.48±0.021, 0.41±0.016, 0.40±0.090, and 0.39±0.010 respectively. The mean genetic trend of milk production, fat percentage, protein percentage, fat content, and protein content were 92, 0.010, 0.004, 1.73, and 2.52, respectively. The correlation between the estimated and reported breeding value of bulls for milk production trait, percentage of fat and protein, and the amount of fat and protein was estimated at 0.48, 0.67, 0.69, 0.14, and 0.26, and all of the estimated correlations are statistically significant at the level of 0.05. Based on the results for the most critical production trait in Isfahan herds, milk production, American bulls have the best performance and genetic trend

Comprehensive Gene Expression Profiling Analysis of Adipose Tissue in Male Individuals from Fat- and Thin-Tailed Sheep Breeds

It has been shown that tail fat content varies significantly among sheep breeds and plays a significant role in meat quality. Recently, significant efforts have been made to understand the physiological, biochemical, and genomic regulation of fat deposition in sheep tails in order to unravel the mechanisms underlying energy storage and adipose tissue lipid metabolism. RNA-seq has enabled us to provide a high-resolution snapshot of differential gene expression between fat- and thin-tailed sheep breeds. Therefore, three RNA-seq datasets were meta-analyzed for the current work to elucidate the transcriptome profile differences between them. Specifically, we identified hub genes, performed gene ontology (GO) analysis, carried out enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and validated hub genes using machine learning algorithms. This approach revealed a total of 136 meta-genes, 39 of which were not significant in any of the individual studies, indicating the higher statistical power of the meta-analysis. Furthermore, the results derived from the use of machine learning revealed POSTN, K35, SETD4, USP29, ANKRD37, RTN2, PRG4, and LRRC4C as substantial genes that were assigned a higher weight (0.7) than other meta-genes. Among the decision tree models, the Random Forest ones surpassed the others in adipose tissue predictive power fat deposition in fat- and thin-tailed breeds (accuracy > 0.85%). In this regard, combining meta-analyses and machine learning approaches allowed for the identification of three important genes (POSTN, K35, SETD4) related to lipid metabolism, and our findings could help animal breeding strategies optimize fat-tailed breeds’ tail sizes

Selective genotyping to implement genomic selection in beef cattle breeding

Genomic selection (GS) plays an essential role in livestock genetic improvement programs. In dairy cattle, the method is already a recognized tool to estimate the breeding values of young animals and reduce generation intervals. Due to the different breeding structures of beef cattle, the implementation of GS is still a challenge and has been adopted to a much lesser extent than dairy cattle. This study aimed to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef while some restrictions were assumed for the availability of phenotypic and genomic information. For this purpose, a multi-breed population of beef cattle was simulated by imitating the practical system of beef cattle genetic evaluation. Four genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed an improvement in prediction accuracy, albeit a limited number of animals being genotyped (i.e., 3% of total animals in genetic evaluation). The comparison of genotyping scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice covers traits that are expressed in either sex, it is recommended that genotyping covers animals from both sexes