Genetic and phenotypic parameters for feed efficiency and component traits in American mink

Abstract

Feed cost is the largest expense of mink production systems, and therefore, improvement of feed efficiency (FE) through selection for high feed efficient mink is a practical way to increase the mink industry's sustainability. In this study, we estimated the heritability, phenotypic and genetic correlations for different FE measures and component traits, including harvest weight (HW), harvest length (HL), final body length (FBL), final body weight (FBW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR), using data from 2,288 American mink (for HW and HL), and 1,038-1,906 American mink (for other traits). Significance (P < 0.05) of fixed effects (farm, sex, and color-type), a covariate (age of animal), and random effects (additive genetic, maternal, and common litter) were evaluated through univariate models implemented in ASReml-R version 4. Genetic parameters were estimated via fitting a set of bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.28±0.06, 0.23±0.06, 0.28±0.10, 0.27±0.11, 0.25±0.09, 0.26±0.09, 0.20±0.09, 0.23±0.09, 0.21±0.10, 0.25±0.10, and 0.26±0.10 for HW, HL, FBL, FBW, ADG, DFI, FCR, RFI, RG, RIG, and KR, respectively. RIG had favorable genetic correlations with DFI (-0.62±0.24) and ADG (0.58±0.21), and non-significant (P > 0.05) genetic correlations with FBW (0.14±0.31) and FBL (-0.15±0.31). These results revealed that RIG might be superior trait as it guarantees reduced feed intake with faster-growing mink yet with no negative impacts on body weight and length. In addition, the strong positive genetic correlations (±SE) between KR with component traits (0.88±0.11 with FBW; 0.68±0.17 with FBL; and 0.97±0.02 with ADG) suggested KR as an applicable indirect measure of FE for improvement of component traits as it did not require the individual feed intake to be measured. Overall, our results confirmed the possibility of including FE traits in mink breeding programs to effectively select feed-efficient animals.

Genome-wide association studies for economically important traits in mink using copy number variation

“…The Aleutian disease tests include counterimmunoelectrophoresis (CIEP), the Aleutian mink disease virus (AMDV) capsid protein-based enzyme-linked immunosorbent assay (ELISA-P), and the AMDV antigen-based enzyme-linked immunosorbent assay (ELISA-G). Breeding values were estimated for all individuals using different animal models for growth and feed efficiency described in detail by 4 , for reproduction traits described in detail by 13 , for pelt quality traits described in detail by 10 , and for Aleutian disease tests described in detail by 7 .…”

“…American mink ( Neogale vison ) is one of the most important animals in the global fur industry, yet requires highly efficient breeding programs to deal with challenges faced in mink production systems 1 . Several studies have been carried out to discern the genetics of complex traits affecting the sustainability of mink production, including growth 2 , 3 , feed efficiency 4 , 5 , disease resistance 6 – 9 , pelt quality 10 – 12 , and reproduction traits 13 – 15 . Understanding the genetic architecture underlying such traits using genome-wide association studies (GWAS) might contribute to expediting the genetic progress through selection, and therefore enhance the production efficiency of the mink industry.…”

“…The availability of a high-quality chromosome-based genome assembly 1 and a genome-wide SNP array for American mink facilitates the identification of genetic variations underlying economically important traits. Recently, Davoudi et al 4 characterized the CNVs in American mink using whole-genome sequencing data. However, up to now, the CNV-based GWAS with economically important traits has not been reported in mink.…”

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

“…The IATs were conducted at the CCFAR to measure the serum gamma globulin level in the serum, and the results were scored into four categories from 0 (low) to 4 (high). All bodyweight (BW), growth parameters, and feed intake data were collected using the established protocols described by Do et al [ 34 ] and Davoudi et al [ 35 ], respectively. The mink were housed individually in single cages, and the feed was distributed to each pen every morning.…”

“…The average daily feed intake (ADFI) was calculated by averaging the DFI records obtained during the test period. The average daily gain (ADG), feed conversion ratio (FCR), Kleiber ratio (KR), residual feed intake (RFI), residual gain (RG), and residual intake and gain (RIG) were derived from the body weight and daily feed intake data [ 35 ]. The growth curve parameters, including asymptotic weight (α), growth rate at mature (k), shape parameter (m), weight at the inflection point (WIP), and age at the inflection point (AIP), were obtained from the body weights of mink using the Richard growth model [ 34 ].…”