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使用插补法进行蛋鸡基因组评估的意义。

Interest of using imputation for genomic evaluation in layer chicken.

机构信息

NOVOGEN, 5 rue des compagnons, Secteur du Vau Ballier, 22960 Plédran, France; PEGASE, INRAE, Agrocampus Ouest, 35590 Saint-Gilles, France.

PEGASE, INRAE, Agrocampus Ouest, 35590 Saint-Gilles, France.

出版信息

Poult Sci. 2020 May;99(5):2324-2336. doi: 10.1016/j.psj.2020.01.004. Epub 2020 Mar 18.

Abstract

With the availability of the 600K Affymetrix Axiom high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop a low-density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low-density SNP chips in genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line. Two low-density SNP chip designs were compared: an equidistant methodology and a methodology based on linkage disequilibrium. Egg weight, egg shell color, egg shell strength, and albumen height were evaluated with single-step genomic best linear unbiased prediction methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV) obtained with imputed HD genotypes or low-density genotypes were compared with GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on the offspring. A limited reordering of the breeders, selected on a multitrait index, was observed. Spearman correlations between GEBV on HD genotypes and GEBV on low-density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNP. For the genetically closer, top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNP. Without imputation, the correlations remained lower than 0.85 with less than 3K and 16K SNP for equidistant and linkage disequilibrium methodology, respectively. The differences in GEBV correlations between both methodologies were never significant. The conclusions were the same for all studied traits.

摘要

随着 600K Affymetrix Axiom 高密度 (HD) 单核苷酸多态性 (SNP) 芯片的出现,基因组选择已在肉鸡和蛋鸡中得到实施。然而,这种 SNP 芯片的成本太高,无法对所有选择对象进行基因分型。一种解决方案是开发一种价格更低的低密度 SNP 芯片,并对所有缺失的标记进行估算。但是,要常规实施该解决方案,必须研究估算对基因组评估准确性的影响。研究在基因组评估准确性中使用低密度 SNP 芯片的后果也很有趣。在这种情况下,在纯层线中研究了在基因组选择中使用估算的兴趣。比较了两种低密度 SNP 芯片设计:等距方法和基于连锁不平衡的方法。使用一步法基因组最佳线性无偏预测方法评估了蛋重、蛋壳颜色、蛋壳强度和蛋白高度。使用基于祖先的基因组评估评估了估算错误或缺乏估算对雄性选择对象排名的影响。因此,将使用 HD 基因型或低密度基因型估算的基因组估计育种值 (GEBV) 与使用 HD SNP 芯片获得的 GEBV 进行了比较。还通过考虑在后代中估计的 GEBV 来研究 GEBV 的相对准确性。在多性状指数上选择的繁殖者的排序略有变化。在 HD 基因型上获得的 GEBV 与在低密度基因型(带或不带估算)上获得的 GEBV 之间的 Spearman 相关系数始终高于 0.94,并且 SNP 超过 3K。对于特定性状的遗传上更接近的前 150 名个体,带有估算值的排序减少了,相关系数高于 0.94,SNP 超过 3K。没有估算值,两种方法的相关系数仍然低于 0.85,分别使用等距和连锁不平衡方法的 SNP 少于 3K 和 16K。两种方法之间的 GEBV 相关性差异从未显著。对于所有研究的性状,结论均相同。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d28f/7597443/f9dafa5ad982/gr1.jpg

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