Genetic parameters of live body weight, body measurements, greasy fleece weight, and reproduction traits in Makuie sheep breed

Genetic parameters of production and reproduction traits were estimated using 39,926 records from 5,860 individual progenies of 163 sires and 1,558 dams. The data were collected at Makuie Sheep Breeding and Raising Station (Maku, Iran) from 1989 through 2013. Nineteen traits were classified in four main groups: a) live body weight traits, b) body measurement traits, c) greasy fleece weight traits, and d) reproduction traits. Year of birth, lamb sex, age of dam, and birth type were considered as fixed effects in the animal model. Four different animal models that are differentiated by including or excluding maternal effects were fitted for each trait. The Akaike information criterion was used to determine the most appropriate model for each trait. Parameters were overestimated substantially when maternal effects, either genetic or environmental, were ignored from the models. By ignoring the maternal effects, the traits could be classified into three main groups: body live weight traits with high heritability (0.34-0.46), body measurement and greasy fleece weight traits with medium heritability (0.11-0.27) and reproduction traits with low heritability (0.03-0.20). The genetic correlations among the traits ranged from-0.41 to 0.99. The estimated genetic parameters may be used to set up short/long term breeding program for the selection purpose of Makuie sheep breed.</p

Population genetic structure and performing assignment test on six Iranian native goats using simple sequence repeat markers

The native goat breeds could be better managed and their genetic diversity to be conserved through identification of population genetic structure. Total of 299 animals from six goat breeds, which are major native breeds of Iran, were used to study their genetic structure and understand relationship among the breeds using SSR markers on 13 microsatellite loci. The breeds were selected from different geographic regions of Iran. The results indicated that there is high genetic diversity at the population level (HS of 0.78) and at the species level (HT of 0.86). The level of inbreeding was low across the breeds and even genetic diversion was observed among them, indicating a low level of gene flow at the regional scale. Some level of admixtures was observed among breeds, which supported by clustering of the breeds based on their geographic origin. Analysis of the population genetic structure indicated that all breeds are grouped into four clusters. The assignment accuracy per locus ranged from 40.1% (BM4621) to 66.9% (oarJMP23). The assignment power of microsatellites based on the Bayesian method had positive correlation with the number of alleles and gene differentiation coefficient (Gst) per locus. In conclusion, this study provided a genetic profile for the conservation and improvement and origin of the studied breeds

Genetic parameters of live body weight, body measurements, greasy fleece weight, and reproduction traits in Makuie sheep breed

Genetic parameters of production and reproduction traits were estimated using 39,926 records from 5,860 individual progenies of 163 sires and 1,558 dams. The data were collected at Makuie Sheep Breeding and Raising Station (Maku, Iran) from 1989 through 2013. Nineteen traits were classified in four main groups: a) live body weight traits, b) body measurement traits, c) greasy fleece weight traits, and d) reproduction traits. Year of birth, lamb sex, age of dam, and birth type were considered as fixed effects in the animal model. Four different animal models that are differentiated by including or excluding maternal effects were fitted for each trait. The Akaike information criterion was used to determine the most appropriate model for each trait. Parameters were overestimated substantially when maternal effects, either genetic or environmental, were ignored from the models. By ignoring the maternal effects, the traits could be classified into three main groups: body live weight traits with high heritability (0.34-0.46), body measurement and greasy fleece weight traits with medium heritability (0.11-0.27) and reproduction traits with low heritability (0.03-0.20). The genetic correlations among the traits ranged from-0.41 to 0.99. The estimated genetic parameters may be used to set up short/long term breeding program for the selection purpose of Makuie sheep breed

Genetic parameters of live body weight, body measurements, greasy fleece weight, and reproduction traits in Makuie sheep breed

Genetic parameters of production and reproduction traits were estimated using 39,926 records from 5,860 individual progenies of 163 sires and 1,558 dams. The data were collected at Makuie Sheep Breeding and Raising Station (Maku, Iran) from 1989 through 2013. Nineteen traits were classified in four main groups: a) live body weight traits, b) body measurement traits, c) greasy fleece weight traits, and d) reproduction traits. Year of birth, lamb sex, age of dam, and birth type were considered as fixed effects in the animal model. Four different animal models that are differentiated by including or excluding maternal effects were fitted for each trait. The Akaike information criterion was used to determine the most appropriate model for each trait. Parameters were overestimated substantially when maternal effects, either genetic or environmental, were ignored from the models. By ignoring the maternal effects, the traits could be classified into three main groups: body live weight traits with high heritability (0.34-0.46), body measurement and greasy fleece weight traits with medium heritability (0.11-0.27) and reproduction traits with low heritability (0.03- 0.20). The genetic correlations among the traits ranged from-0.41 to 0.99. The estimated genetic parameters may be used to set up short/long term breeding program for the selection purpose of Makuie sheep bree

Coat color inheritance in American mink

Abstract Background Understanding the genetic mechanisms underlying coat color inheritance has always been intriguing irrespective of the animal species including American mink (Neogale vison). The study of color inheritance in American mink is imperative since fur color is a deterministic factor for the success of mink industry. However, there have been no studies during the past few decades using in-depth pedigree for analyzing the inheritance pattern of colors in American mink. Methods In this study, we analyzed the pedigree of 23,282 mink extending up to 16 generations. All animals that were raised at the Canadian Center for Fur Animal Research (CCFAR) from 2003 to 2021 were used in this study. We utilized the Mendelian ratio and Chi-square test to investigate the inheritance of Dark (9,100), Pastel (5,161), Demi (4,312), and Mahogany (3,358) colors in American mink. Results The Mendelian inheritance ratios of 1:1 and 3:1 indicated heterozygous allelic pairs responsible for all studied colors. Mating sire and dam of the same color resulted in the production of offspring with the same color most of the time. Conclusion Overall, the results suggested that color inheritance was complex and subjected to a high degree of diversity in American mink as the genes responsible for all four colors were found to be heterozygous

Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources

American mink (Neogale vison) is one of the major sources of fur for the fur industries worldwide, whereas Aleutian disease (AD) is causing severe financial losses to the mink industry. A counterimmunoelectrophoresis (CIEP) method is commonly employed in a test-and-remove strategy and has been considered a gold standard for AD tests. Although machine learning is widely used in livestock species, little has been implemented in the mink industry. Therefore, predicting AD without using CIEP records will be important for controlling AD in mink farms. This research presented the assessments of the CIEP classification using machine learning algorithms. The Aleutian disease was tested on 1157 individuals using CIEP in an AD-positive mink farm (Nova Scotia, Canada). The comprehensive data collection of 33 different features was used for the classification of AD-infected mink. The specificity, sensitivity, accuracy, and F1 measure of nine machine learning algorithms were evaluated for the classification of AD-infected mink. The nine models were artificial neural networks, decision tree, extreme gradient boosting, gradient boosting method, K-nearest neighbors, linear discriminant analysis, support vector machines, naive bayes, and random forest. Among the 33 tested features, the Aleutian mink disease virus capsid protein-based enzyme-linked immunosorbent assay was found to be the most important feature for classifying AD-infected mink. Overall, random forest was the best-performing algorithm for the current dataset with a mean sensitivity of 0.938 &plusmn; 0.003, specificity of 0.986 &plusmn; 0.005, accuracy of 0.962 &plusmn; 0.002, and F1 value of 0.961 &plusmn; 0.088, and across tenfold of the cross-validation. Our work demonstrated that it is possible to use the random forest algorithm to classify AD-infected mink accurately. It is recommended that further model tests in other farms need to be performed and the genomic information needs to be used to optimize the model for implementing machine learning methods for AD detection

Comparative analyses of enteric methane emissions, dry matter intake, and milk somatic cell count in different residual feed intake categories of dairy cows

This study compared the different residual feed intake (RFI) categories of lactating Holsteins with respect to methane (CH4) emissions, dry matter intake (DMI, kg), milk somatic cell count (SCC, 103∙mL−1), and β-hydroxybutyrate (BHB, mmol∙L−1). The RFI was calculated in 131 lactating Holstein cows that were then categorized into −RFI (RFI  0) and low- [RFI  0.5 SD) groups. Milk traits were recorded in 131 cows, whereas CH4 and carbon dioxide were measured in 83. Comparisons of −RFI vs. +RFI and low- vs. high-RFI showed 7.9% (22.3 ± 0.40 vs. 24.2 ± 0.39) and 12.8% (21.1 ± 0.40 vs. 24.2 ± 0.45) decrease (P  0.05) in −RFI vs. +RFI and low vs. high comparisons. The −RFI and low-RFI cows had lower (P < 0.05) SCC in −RFI vs. +RFI and low-RFI vs. high-RFI comparisons. The BHB was lower (P < 0.05) in low-RFI compared with the high-RFI group. Low-RFI dairy cows consumed less feed, emitted less CH4 (g∙d−1), and had lower milk SCC and BHB without differing in milk yield.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Optimizing feed intake recording and feed efficiency estimation to increase the rate of genetic gain for feed efficiency in beef cattle

Data from a total of 4,842 animals were used to test whether the regular DMI data collection and RFI estimation period could be decreased. Eighty-three shortened test periods were compared to the regular test period, and the results showed that the DMI data collection period could be decreased to 42 days without significantly compromising accuracy of feed efficiency testing. Competency of the selected shorter period (42 d with 30-42 d of valid feed intake days) to predict regular test period DMI (84 d with 60-84 d of valid feed intake days) was tested using a set of agreements criteria. The results showed that the selected shorter period can be used to accurately and precisely predict regular test DMI. The selected shorter test period combined with regular body weight measurements were used to estimate RFI adjusted for back-fat (RFIThe accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Methane and carbon dioxide emissions from yearling beef heifers and mature cows classified for residual feed intake under drylot conditions

This study quantified methane (CH4) and carbon dioxide (CO2) production from beef heifers and cows classified for residual feed intake adjusted for off-test backfat thickness (RFIfat) and reared in drylot during cold winter temperatures. Individual performance, daily feed intake, and RFIfat were obtained for 1068 crossbred and purebred yearling heifers (eight trials) as well as 176 crossbred mature cows (six trials) during the winters of 2015–2017 at two locations. A portion of these heifers (147 high RFIfat; 167 low RFIfat) and cows (69 high RFIfat; 70 low RFIfat) was monitored for enteric CH4 and CO2 emissions using the GreenFeed Emissions Monitoring (GEM) system (C-Lock Inc., Rapid City, SD, USA). Low RFIfat cattle consumed less feed [heifers, 7.80 vs. 8.48 kg dry matter (DM) d−1; cows, 11.64 vs. 13.16 kg DM d−1] and emitted less daily CH4 (2.5% for heifers; 3.7% for cows) and CO2 (1.4% for heifers; 3.4% for cows) compared with high RFIfat cattle. However, low RFIfat heifers and cows had higher CH4 (6.2% for heifers; 9.9% for cows) and CO2 yield (7.3% for heifers; 9.8% for cows) per kilogram DM intake compared with their high RFIfat pen mates. The GEM system performed at air temperatures between +20 and −30 °C. Feed intake of heifers and mature cows was differently affected by ambient temperature reduction between +20 and −15 °C and similarly increased their feed intake at temperatures below −15 °C. In conclusion, low RFIfat animals emit less daily enteric CH4 and CO2, due mainly to lower feed consumption at equal body weight, gain, and fatness.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author