• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>International Journal of Molecular Sciences >Genomic Prediction for Grain Yield and Yield-Related Traits in Chinese Winter Wheat
【2h】

Genomic Prediction for Grain Yield and Yield-Related Traits in Chinese Winter Wheat

机译：中国冬小麦籽粒产量及相关性状的基因组预测

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore–Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.
机译：基因组选择（GS）是一种使用全基因组标记预测个体遗传优势的策略。但是，GS预测的准确性受许多因素的影响，包括基因型数据的缺失率和次要等位基因频率（MAF），GS模型，性状特征等。在这项研究中，我们使用一个小麦群体调查了各种GS模型的预测准确性有关来自各种质量控制（QC）方案，缺失基因型归因和全基因组关联研究（GWAS）标记的产量和产量相关性状的研究。单核苷酸多态性（SNP）标记的缺失率和MAF是质量控制的两个主要因素。考虑了五个遗漏率级别（0％，20％，40％，60％和80％）和三个MAF级别（0％，5％和10％），并使用五重交叉验证来估计预测准确性。结果表明，适度的遗漏率水平（20％至40％）和MAF（5％）阈值可提供更好的预测准确性。在此QC方案下，进一步估算了估算和GWAS衍生标记的预测准确性。观察到这六个性状的准确性与其遗传力，遗传结构以及GS预测模型有关。 Moore-Penrose广义逆（GenInv），岭回归（RidgeReg）和随机森林（RForest）导致的性状预测精度高于其他GS模型。缺失基因型数据的插补对预测准确性有边际影响，而在大多数情况下，GWAS衍生的标记可提高预测准确性。这些结果表明，QC缺失率和MAF对GS模型的可预测性具有积极影响。对于所有特征和GS模型，我们无法确定一种质量控制方案的组合可能胜过其他组合。但是，标记数量和标记质量之间的平衡对于小麦育种中GS的部署很重要。 GWAS能够选择与性状最相关的标记，因此可用于提高GS的预测准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号