Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS B2N 5E3, Canada.
Department of Pathobiology, University of Guelph, Guelph, ON N1G 2W1, Canada.
Genes (Basel). 2021 Feb 11;12(2):258. doi: 10.3390/genes12020258.
Characterizing the genetic structure and population history can facilitate the development of genomic breeding strategies for the American mink. In this study, we used the whole genome sequences of 100 mink from the Canadian Centre for Fur Animal Research (CCFAR) at the Dalhousie Faculty of Agriculture (Truro, NS, Canada) and Millbank Fur Farm (Rockwood, ON, Canada) to investigate their population structure, genetic diversity and linkage disequilibrium (LD) patterns. Analysis of molecular variance (AMOVA) indicated that the variation among color-types was significant ( < 0.001) and accounted for 18% of the total variation. The admixture analysis revealed that assuming three ancestral populations (K = 3) provided the lowest cross-validation error (0.49). The effective population size () at five generations ago was estimated to be 99 and 50 for CCFAR and Millbank Fur Farm, respectively. The LD patterns revealed that the average reduced to <0.2 at genomic distances of >20 kb and >100 kb in CCFAR and Millbank Fur Farm suggesting that the density of 120,000 and 24,000 single nucleotide polymorphisms (SNP) would provide the adequate accuracy of genomic evaluation in these populations, respectively. These results indicated that accounting for admixture is critical for designing the SNP panels for genotype-phenotype association studies of American mink.
对遗传结构和种群历史进行特征描述有助于制定美国水貂的基因组育种策略。在这项研究中,我们使用了来自加拿大农业达尔豪西学院(特鲁罗,新斯科舍省,加拿大)和米尔班克皮毛农场(罗克伍德,安大略省,加拿大)的 100 只水貂的全基因组序列,以研究它们的种群结构、遗传多样性和连锁不平衡(LD)模式。分子方差分析(AMOVA)表明,颜色类型之间的变异是显著的(<0.001),占总变异的 18%。混合分析表明,假设三个祖先群体(K=3)提供了最低的交叉验证错误(0.49)。CCFAR 和 Millbank Fur Farm 的五个世代前的有效种群大小(Ne)分别估计为 99 和 50。LD 模式表明,在 CCFAR 和 Millbank Fur Farm 中,平均 r2 在基因组距离>20kb 和>100kb 时降至<0.2,这表明在这些群体中,密度为 120000 和 24000 的单核苷酸多态性(SNP)将分别提供基因组评估的足够准确性。这些结果表明,在设计美国水貂基因型-表型关联研究的 SNP 面板时,考虑混合是至关重要的。