Genetic and phenotypic parameters for Aleutian disease tests and their correlations with pelt quality, reproductive performance, packed-cell volume, and harvest length in mink

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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 ± 0.003, specificity of 0.986 ± 0.005, accuracy of 0.962 ± 0.002, and F1 value of 0.961 ± 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.

Section: Resultssupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Ngoc

Davoudi

et al. 2022

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“…Infected mink that remained healthy (non-progressive infection) have long been known to have low antibody titers [ 15 , 16 , 17 ]. Antibody titer is also negatively associated with reproductive performance [ 18 , 19 ], thus, implying that selection for low antibody titers is a logical approach for establishing tolerant herds. Development of enzyme-linked immunosorbent assay (ELISA) systems [ 20 , 21 , 22 ] has facilitated antibody titer measurement for the identification of tolerant mink, and a combination of weak ELISA positive and normal litter size was effective in reducing the incidence of clinical forms of the disease [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Serum Analytes of American Mink (Neovison Vison) Challenged with Aleutian Mink Disease Virus

Farid

Rupasinghe

2022

Black American mink (Neovison vison), which had been selected for tolerance to Aleutian mink disease virus (AMDV) for more than 20 years (TG100) or were from herds that have been free of AMDV (TG0), along with their progeny and crosses with 50% and 75% tolerance ancestry, were inoculated with a local isolate of AMDV. Blood samples were collected from 493 mink between 120 and 1211 days post-inoculation, and concentrations of 14 serum analytes were measured. Distributions of all analytes significantly deviated from normality, and data were analyzed after Box–Cox power transformation. Significant differences were observed among tolerant groups in the concentrations of globulin (GLO), total protein (TP), alkaline phosphatase, urea nitrogen, and calcium. Concentrations of GLO and TP linearly and significantly decreased with an increasing percentage of tolerance ancestry. Eleven analytes had the smallest values in the tolerant groups (TG100 or TG75), and eight analytes had the greatest values in the non-selected groups (TG0 or TG50). Antibody titer had the greatest correlation coefficients with GLO (0.62), TP (0.53), and creatinine (0.36). It was concluded that selection for tolerance decreased the concentrations of most serum analytes, and TP and GLO were the most accurate biomarkers of tolerance to AMDV infection. Males had significantly greater values than females for phosphorus and total bilirubin concentrations, but females had significantly greater amylase, cholesterol, and BUN concentrations than males.

“…On the other hand, genetic selection for pelt quality traits requires estimating the heritabilities as well as the phenotypic and genetic correlations among these traits. Several studies have estimated the genetic parameters and heritabilities for live grading traits in American mink populations [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. However, heritabilities for dried pelt quality traits and their genetic correlations with live grading traits and body weight and length traits have rarely been reported [ 6 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Genetic and Phenotypic Parameters for Pelt Quality and Body Length and Weight Traits in American Mink

Valipour

Karimi

Barrett

et al. 2022

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Understanding the genetics of fur characteristics and skin size is important for developing effective breeding programs in the mink industry. Therefore, the objectives of this study were to estimate the genetic and phenotypic parameters for pelt quality traits including live grading overall quality (LQU), live grading nap size (LNAP), dried pelt size (DPS), dried pelt nap size (DNAP) and overall quality of dried pelt (DQU), and body length and weight traits, including November body weight (Nov_BW), November body length (Nov_BL), harvest weight (HW) and harvest length (HL) in American mink. Dried pelt quality traits on 1195 mink and pelt quality traits on live animals on 1680 were collected from mink raised at two farms, in Nova Scotia and Ontario. A series of univariate analyses were implemented in ASReml 4.1 software to identify the significance (p < 0.05) of random effects (maternal genetic effects, and common litter effects) and fixed effects (farm, sex, color type, year, and age) for each trait. Subsequently, bivariate models were used to estimate the genetic and phenotypic parameters using ASReml 4.1. Heritability (±SE) estimates were 0.41 ± 0.06 for DPS, 0.23 ± 0.10 for DNAP, 0.12 ± 0.04 for DQU, 0.28 ± 0.06 for LQU, 0.44 ± 0.07 for LNAP, 0.29 ± 0.10 for Nov_BW, 0.28 ± 0.09 for Nov_BL, 0.41 ± 0.07 for HW and 0.31 ± 0.06 for HL. DPS had high positive genetic correlations (±SE) with Nov_BW (0.89 ± 0.10), Nov_BL (0.81 ± 0.07), HW (0.85 ± 0.05) and HL (0.85 ± 0.06). These results suggested that body weight and length measured on live animals in November of the first year were reliable indicators of dried pelt size. DQU had favorable genetic correlations with Nov_BL (0.55 ± 0.24) and HL (0.46 ± 0.20), and nonsignificant genetic correlations with DNAP (0.13 ± 0.25), Nov_BW (0.25 ± 0.25) and HW (0.06 ± 0.20), which made body length traits an appealing trait for selection for increased pelt size. High positive genetic correlation (±SE) was observed between LNAP and DNAP (0.82 ± 0.22), which revealed that nap size measurement on live animals is a reliable indicator trait for dried pelt nap size. However, nonsignificant (p > 0.05) low genetic correlation (±SE) was obtained between LQU and DQU (0.08 ± 0.45), showing that indirect selection based on live grading might not lead to the satisfactory improvement of dried pelt overall quality. The estimated genetic parameters for live grading, dried pelt quality, and body weight and body length traits may be incorporated into breeding programs to improve fur characteristics in Canadian mink populations.