检索结果-南通市图书馆

Genomic selection in plant breeding:Key factors shaping two decades of progress

Molecular Plant 2024年第4期17卷 552-578页

作者： Admas Alemu Johanna Astrand osval a.montesinos-l.pez Julio Isidro y Sanchez Javier Fernandez-Gónzalez Wuletaw Tadesse Ramesh R.Vetukuri Anders S.Carlsson Alf Ceplitis JoséCrossa Rodomiro Ortiz Aakash Chawade Department of Plant Breeding Swedish University of Agricultural SciencesAlnarpSweden lantmannen lantbruk SvalövSweden Facultad de Telematica University de ColimaColimaColima 28040Mexico Centro de Biotecnología y Genómica de Plantas(CBGP UPM-INIA)Universidad Politécnica de Madrid(UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria(INIA)Campus de Montegancedo-UPM28223 MadridSpain International Center for Agricultural Research in the Dry Areas(ICARDA) RabatMorocco International Maize and Wheat Improvement Center(CIMMYT) Km 45Carretera México-VeracruzTexcocoMéxico 52640Mexico

Genomic selection,the application of genomic prediction(GP)models to select candidate individuals,has significantly advanced in the past two decades,effectively accelerating genetic gains in plant *** article provides a holistic overview of key factors that have influenced GP in plant breeding during this *** delved into the pivotal roles of training population size and genetic diversity,and their relationship with the breeding population,in determining GP *** emphasis was placed on optimizing training population *** explored its benefits and the associated diminishing returns beyond an optimum *** was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization *** density and distribution of single-nucleotide polymorphisms,level of linkage disequilibrium,genetic complexity,trait heritability,statistical machine-learning methods,and non-additive effects are the other vital *** wheat,maize,and potato as examples,we summarize the effect of these factors on the accuracy of GP for various *** search for high accuracy in GP—theoretically reaching one when using the Pearson’s correlation as a metric—is an active research area as yet far from optimal for various *** hypothesize that with ultra-high sizes of genotypic and phenotypic datasets,effective training population optimization methods and support from other omics approaches(transcriptomics,metabolomics and proteomics)coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy,making genomic selection an effective tool in plant breeding.

关键词： genomic selection genetic gain genomic prediction optimization deep learning training population optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Bayesian regularized quantile regression:A robust alternative for genome-based prediction of skewed data

引用

The Crop Journal 2020年第5期8卷 713-722页

作者： Paulino Pérez-Rodríguez osval a.montesinos-l.pez Abelardo montesinos-l.pez JoséCross Colegio de Postgraduados MontecillosEdo.de México 56230Mexico Facultad de Telemática Universidad de ColimaColima 28040Mexico Departamento de Matemáticas Centro Universitario de Ciencias Exactas e Ingenierías(CUCEI)Universidad de GuadalajaraGuadalajaraJalisco 44430Mexico International Maize and Wheat Improvement Center(CIMMYT) Km 45 Carretera México-VeracruzTexcocoEdo.de México 56237Mexico

Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable *** this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research *** evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBlUP).In addition,BlUP can be used with pedigree information obtained from the coefficient of coancestry(ABlUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.

关键词： laplace distribution Robust regression Bayesian quantile regression Genomic-enabled prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：