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全基因组关联研究及尼罗罗非鱼生长和鱼片产量的成本效益基因组预测()。

Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia ().

机构信息

Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, 8820808 Chile.

Benchmark Genetics Chile, Puerto Montt, Chile, and.

出版信息

G3 (Bethesda). 2019 Aug 8;9(8):2597-2607. doi: 10.1534/g3.119.400116.

Abstract

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

摘要

鱼片产量 (FY) 和收获体重 (HW) 是尼罗罗非鱼生产中经济上重要的性状。由于缺乏有效测量这些性状的方法(不牺牲鱼类)以及利用亲属信息进行选择,这些性状的遗传改良,特别是 FY,一直很缺乏。然而,基因组信息可以通过基因组选择用于改良那些在选择对象中难以直接测量的性状,就像 FY 一样。本研究的目的是:(i)进行全基因组关联研究 (GWAS) 以剖析 FY 和 HW 的遗传结构,(ii)评估基因型估计的准确性,以及(iii)使用真实和估计的低密度 (LD) 单核苷酸多态性 (SNP) 面板评估基因组选择的准确性,以确定在罗非鱼育种计划中实施基因组信息的经济有效的策略。该数据集由 5866 头表型动物和 1238 头基因型动物组成(108 头亲本和 1130 头后代),使用 50K SNP 面板。GWAS 是在所有基因型和表型动物上进行的。基因型估计是从 LD 面板(LD0.5K、LD1K 和 LD3K)到高密度面板(HD)进行的,使用参考组中亲本和 20%的后代的信息以及验证组中剩余的 80%的信息。此外,我们使用真实和估计的基因型测试了基因组选择的准确性,比较了从系谱最佳线性无偏预测 (PBLUP) 和基因组预测中获得的准确性。GWAS 的结果支持 FY 和 HW 具有多基因性质的证据。LD0.5K 和 LD3K 的估计准确性分别为 0.90 到 0.98。基因组预测的准确性优于 PBLUP 的估计育种值。基因组选择中使用估计基因型可提高相对准确性,与分析的性状和 LD 面板无关。本研究结果表明,基因型估计可能是尼罗罗非鱼育种计划中基因组选择的一种经济有效的策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94ad/6686944/724d34529de8/2597f1.jpg

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