首页> 外文期刊>Livestock Science >Genomic-polygenic and polygenic predictions for milk yield, fat yield, and age at first calving in Thai multibreed dairy population using genic and functional sets of genotypes
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Genomic-polygenic and polygenic predictions for milk yield, fat yield, and age at first calving in Thai multibreed dairy population using genic and functional sets of genotypes

机译:基因组 - 多种基因和多种子基预测对泰国多毛细乳制品群首次使用基因型和功能基因型的乳质产量,脂肪产率和年龄

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The objectives of this research were to estimate genomic-polygenic and polygenic variance components and parameters, and to compare prediction accuracies and rankings of animals for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) using three genomic-polygenic models and a polygenic model (PM). Data came from 8361 first-lactation cows from the Thai multibreed dairy population. The genomic-polygenic models utilized the complete set of SNP from GeneSeek GGP80k (ssGBLUP), a genic set composed of SNP associated with MY, FY, and AFC located inside or within 2500 bp of genes in the NCBI database (ssGBLUPS1), and a functional set was a subset of the genic set that included only those SNP that were present in enriched pathways involving these traits (ssGBLUPS2). The 3-trait ssGBLUP, ssGBLUPS1, ssGBLUPS2, and PM included contemporary group (herd-year-season) and heterosis as fixed effects, and animal additive genetic and residual as random effects. REML genomic-polygenic and polygenic variance components and parameters were estimated with program AIREMLF90. MY and FY ssGBLUP heritabilities were higher than PM (0.25 vs. 0.15 for MY, 0.18 vs. 0.14 for FY), but lower than PM for AFC (0.16 vs. 0.19). Genetic correlations were positive between MY and FY (0.65 for ssGBLUP; 0.72 for PM), and negative between MY and AFC (- 0.11 for ssGBLUP; - 0.08 for PM) and between FY and AFC ( - 0.17 for ssGBLUP; - 0.22 for PM). Accuracies of the EBV for MY, FY, and AFC from ssGBLUP and ssGBLUPS1 were similar and higher than those from ssGBLUPS2 and PM. ssGBLUP and ssGBLUPS1 EBV yielded the highest rank correlations of all pairs of models among sires, cows, and all animals for all traits. The similarity between EBV accuracies and rank correlations from ssGBLUP and ssGBLUPS1 indicated that the set of SNP markers in the genic set could be a feasible alternative to the complete SNP set from GeneSeek GGP8Ok for genomic-polygenic evaluation and selection of animals in the Thai multibreed dairy population. However, an annual reassessment of the set of SNP markers included in the genic set would be advisable as additional phenotypic, pedigree, and genotypic information becomes available in future years.
机译:该研究的目的是估计基因组 - 多种基因和多基因差异组分和参数,并比较动物的预测精度和动物的排名305-D牛奶产量(我的),305-D脂肪产量(FY)和年龄使用三种基因组 - 多基因模型和多基因模型(PM)来捕获(AFC)。数据来自泰国多毛细乳制品人口的8361奶牛。基因组 - 多基因模型利用来自Geneseek GGP80K(SSGBLUP)的完整SNP,由与My,Fy和AFC相关的SNP组成的基因组,位于NCBI数据库(SSGBLUPS1)中的基因内部或在2500年内,功能组是基因组的子集,仅包括涉及这些特征的富集途径中存在的那些SNP(SSGBLUPS2)。 3特质SSGBLUP,SSGBLUPS1,SSGBLUPS2和PM包括当代组(群赛季)和杂种优势作为固定效果,动物添加剂遗传和残留为随机效果。 RemL基因组 - 多基因和多基因差分量和参数估计是通过Progrous AiroMLF90的。我和FY SSGBLUP Herititibilities高于PM(我的0.25与0.15,0.18与FY为0.14),但为AFC的PM(0.16与0.19)低。遗传相关性在我和FY之间是积极的(SSGBLUP的0.65; PM的0.72),我和AFC之间的负数(SSGBLUP - 0.08为0.08),FY和AFC(SSGBLUP的0.08)之间(0.08,PM为0.22 - 0.22 )。来自SSGBLUP和SSGBLUPS1的EBV的eBV的准确性和SSGBLUPS1的AFC类似,高于SSGBLUPS2和PM的AFC。 SSGBLUP和SSGBLUPS1 EBV产生了所有特征的所有特征的所有型号的所有模型的最高等级相关性。来自SSGBLUP和SSGBLUPS1的EBV精度和等级相关性的相似性表明,基因组中的SNP标记集可以是从Geneseek GGP8欧元的完整SNP进行的可行替代品,用于泰国多葡萄质乳制品中的基因组 - 多种子基评估和各种动物的选择人口。然而,在基因组中包含的SNP标记集的年重新评估是可取的额外表型,血统和基因型信息在未来几年中可用。

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