Abstract

Abstract Feed efficiency (FE) traits contribute to the economic output of mink production systems as feed costs comprise the largest proportion of their variable expenses. However, the genetic architecture underlying FE-related traits is largely unknown in American mink. The objective of this study was to identify potential genetic variants and candidate genes associated with feed efficiency and growth traits, including body weight (BW), 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). Genome-wide association studies (GWAS) were performed using deregressed estimated breeding values (DEBVs) of 1,255 to 2,160 individuals (as pseudophenotypes), genotyped with the Affymetrix Mink 70K single nucleotide polymorphisms (SNP) array. Association analyses were performed using the mixed linear model in GCTA software. A total of 36 SNPs located on 11 different chromosomes were significantly (FDR < 0.01) associated with eight feed efficiency and growth traits, among which nine SNPs had pleiotropic effects on at least two analyzed traits. The phenotypic variance (of DEBVs) explained by all significant SNPs for BW, ADG, DFI, FCR, RFI, RG, RIG, and KR, were 0.54%, 2.35%, 0.51%, 2.45%, 0.03%, 3.67%, 0.21%, and 2.60%, respectively. Furthermore, 191 genes were identified within 1-Mb windows around these significant SNPs. These regions included candidate genes such as FABP6, ADAMTS18, ADGRB3, GRM8, DSCAM, COL9A1, and CSRP2, previously associated with feed efficiency and growth traits in other livestock species. Gene ontology analyses revealed that these genes were involved in molecular functions such as ATPase activity and ATP binding, which provide the direct energy source for the body. To our knowledge, this is the first GWAS to identify genetic variants and biological mechanisms associated with FE and growth traits in American mink. These findings provide a biological foundation for improving these traits using genomic selection programs to select more efficient mink.

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