Literature DB >> 24970707

Genomic selection: genome-wide prediction in plant improvement.

Zeratsion Abera Desta1, Rodomiro Ortiz2.   

Abstract

Association analysis is used to measure relations between markers and quantitative trait loci (QTL). Their estimation ignores genes with small effects that trigger underpinning quantitative traits. By contrast, genome-wide selection estimates marker effects across the whole genome on the target population based on a prediction model developed in the training population (TP). Whole-genome prediction models estimate all marker effects in all loci and capture small QTL effects. Here, we review several genomic selection (GS) models with respect to both the prediction accuracy and genetic gain from selection. Phenotypic selection or marker-assisted breeding protocols can be replaced by selection, based on whole-genome predictions in which phenotyping updates the model to build up the prediction accuracy.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  accuracy; breeding cycle; genetic gain; genomic selection; prediction models

Mesh:

Year:  2014        PMID: 24970707     DOI: 10.1016/j.tplants.2014.05.006

Source DB:  PubMed          Journal:  Trends Plant Sci        ISSN: 1360-1385            Impact factor:   18.313


  151 in total

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2.  Prediction and association mapping of agronomic traits in maize using multiple omic data.

Authors:  Y Xu; C Xu; S Xu
Journal:  Heredity (Edinb)       Date:  2017-06-07       Impact factor: 3.821

3.  Unlocking historical phenotypic data from an ex situ collection to enhance the informed utilization of genetic resources of barley (Hordeum sp.).

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Journal:  Theor Appl Genet       Date:  2018-06-29       Impact factor: 5.699

Review 4.  Genotypic Context and Epistasis in Individuals and Populations.

Authors:  Timothy B Sackton; Daniel L Hartl
Journal:  Cell       Date:  2016-07-14       Impact factor: 41.582

5.  Genomic selection for wheat traits and trait stability.

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Journal:  Theor Appl Genet       Date:  2016-06-04       Impact factor: 5.699

6.  Efficiency of genomic selection for breeding population design and phenotype prediction in tomato.

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Journal:  Heredity (Edinb)       Date:  2016-09-14       Impact factor: 3.821

7.  Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population.

Authors:  Katie M O'Connor; Ben J Hayes; Craig M Hardner; Mobashwer Alam; Robert J Henry; Bruce L Topp
Journal:  BMC Genomics       Date:  2021-05-20       Impact factor: 3.969

8.  Genome-wide association analysis of lead accumulation in maize.

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Journal:  Mol Genet Genomics       Date:  2017-12-22       Impact factor: 3.291

9.  Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.

Authors:  Yang Xu; Yue Zhao; Xin Wang; Ying Ma; Pengcheng Li; Zefeng Yang; Xuecai Zhang; Chenwu Xu; Shizhong Xu
Journal:  Plant Biotechnol J       Date:  2020-09-02       Impact factor: 9.803

10.  Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model.

Authors:  R T Resende; M D V Resende; F F Silva; C F Azevedo; E K Takahashi; O B Silva-Junior; D Grattapaglia
Journal:  Heredity (Edinb)       Date:  2017-07-05       Impact factor: 3.821

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