您好，欢迎访问中国水产科学研究院 机构知识库！
中国水产科学研究院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Accurate genomic selection using low-density SNP panels preselected by maximum likelihood estimation

文献类型： 外文期刊

作者： Qu, Shiyu 1 ; Lu, Sheng 1 ; Liu, Yang 1 ; Li, Ming 1 ; Chen, Songlin 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, State Key Lab Mariculture Biobreeding & Sustainabl, Qingdao 266071, Shandong, Peoples R China

2.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Key Lab Sustainable Dev Marine Fisheries, Minist Agr & Rural Affairs, Qingdao 266071, Shandong, Peoples R China

3.Laoshan Lab, Lab Marine Fisheries Sci & Food Prod Proc, Qingdao 266237, Shandong, Peoples R China

4.Yellow Sea Fisheries Res Inst, 106 Nanjing Rd, Qingdao 266071, Peoples R China

关键词： Maximum likelihood estimation; Genomic selection; SNP pre-selection; MLE-rank

期刊名称：AQUACULTURE （ 影响因子：4.5； 五年影响因子：4.6 ）

ISSN： 0044-8486

年卷期： 2024 年 579 卷

页码：

收录情况： SCI

摘要： Genomic selection (GS) poses a challenge for the prediction of the genomic estimated breeding value (GEBV) using a low-density SNP panel. Several methods have been proposed for SNP preselection. However, these methods often suffer from either significant computational complexity or erratic accuracy in GS. In this study, we developed an approach called MLE-rank based on maximum likelihood estimation to preselect a set of SNPs for GS. First, we generated 90 simulated datasets and compared the performance of MLE-rank with uniform dis-tribution and preselection based on a genome-wide association study (GWAS). For simulated datasets, compared to uniform distribution, both MLE-rank and GWAS preselection reduced the SNP density by a factor of 10 while maintaining prediction accuracy. Additionally, compared to the other two methods, MLE-rank's prediction ac-curacy was significantly improved with the medium-and high-heritability datasets. Then, we further evaluated these three preselection approaches using real disease-resistant phenotypes of leopard coral grouper (Plec-tropomus leopardus) and Japanese flounder (Paralichthys olivaceus). We found that the 3 k SNPs preselected by MLE-rank had a stable and effective prediction effect. The uniform distribution requires 70 k, while the GWAS preselection method requires 3 k (P. leopardus) and 50 k (P. olivaceus) to achieve similar prediction accuracy. Finally, we evaluated the prediction accuracy of MLE-rank using candidate populations of flounders and their progeny survival rates, with uniform distribution and GWAS preselection as benchmarks. In the results for this dataset, MLE-rank was found to have the same predictive effect for low-density SNP panels as it did for high-density SNPs, regardless of whether GWAS preselection or uniform distribution was used. Taken together, the results we have observed indicate that we have ensured that MLE-rank does not reduce prediction accuracy for any of the datasets. MLE-rank showed superior performance in reducing the number of SNPs. Moreover, we observed a relative standard deviation in prediction accuracy when using a low density of SNPs selected by MLE-rank compared to a high density determined through a uniform distribution strategy. In conclusion, MLE-rank not only reduces the number of SNPs used for GS but also exhibits high predictive accuracy. This could potentially lead to a decrease in genotyping costs and promote the wider application of GS in fish breeding.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国水产科学研究院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台