Literature DB >> 35451782

Incorporating Omics Data in Genomic Prediction.

Johannes W R Martini1, Ning Gao2, José Crossa3.   

Abstract

In this chapter, we discuss the motivation for integrating other types of omics data into genomic prediction methods. We give an overview of literature investigating the performance of omics-enhanced predictions, and highlight potential pitfalls when applying these methods in breeding. We emphasize that the statistical methods available for genomic data can be transferred to the general omics case. However, when using a framework of omic relationship matrices, the standardization of the variables may be more relevant than it is for a genomic relationship matrix based on single-nucleotide polymorphisms.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Metabolomic relationship; Omics-based prediction; Omics-enhanced prediction; Transcriptomic relationship

Mesh:

Year:  2022        PMID: 35451782     DOI: 10.1007/978-1-0716-2205-6_12

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  53 in total

1.  Best linear unbiased estimation and prediction under a selection model.

Authors:  C R Henderson
Journal:  Biometrics       Date:  1975-06       Impact factor: 2.571

2.  Modeling Epistasis in Genomic Selection.

Authors:  Yong Jiang; Jochen C Reif
Journal:  Genetics       Date:  2015-07-27       Impact factor: 4.562

3.  Epistasis and covariance: how gene interaction translates into genomic relationship.

Authors:  Johannes W R Martini; Valentin Wimmer; Malena Erbe; Henner Simianer
Journal:  Theor Appl Genet       Date:  2016-02-16       Impact factor: 5.699

4.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

5.  Efficient Algorithms for Calculating Epistatic Genomic Relationship Matrices.

Authors:  Yong Jiang; Jochen C Reif
Journal:  Genetics       Date:  2020-09-24       Impact factor: 4.562

6.  Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

Authors:  John M Hickey; Tinashe Chiurugwi; Ian Mackay; Wayne Powell
Journal:  Nat Genet       Date:  2017-08-30       Impact factor: 38.330

Review 7.  Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Authors:  José Crossa; Paulino Pérez-Rodríguez; Jaime Cuevas; Osval Montesinos-López; Diego Jarquín; Gustavo de Los Campos; Juan Burgueño; Juan M González-Camacho; Sergio Pérez-Elizalde; Yoseph Beyene; Susanne Dreisigacker; Ravi Singh; Xuecai Zhang; Manje Gowda; Manish Roorkiwal; Jessica Rutkoski; Rajeev K Varshney
Journal:  Trends Plant Sci       Date:  2017-09-28       Impact factor: 18.313

8.  Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Authors:  José Crossa; Gustavo de Los Campos; Paulino Pérez; Daniel Gianola; Juan Burgueño; José Luis Araus; Dan Makumbi; Ravi P Singh; Susanne Dreisigacker; Jianbing Yan; Vivi Arief; Marianne Banziger; Hans-Joachim Braun
Journal:  Genetics       Date:  2010-09-02       Impact factor: 4.562

9.  On the approximation of interaction effect models by Hadamard powers of the additive genomic relationship.

Authors:  Johannes W R Martini; Fernando H Toledo; José Crossa
Journal:  Theor Popul Biol       Date:  2020-01-25       Impact factor: 1.570

Review 10.  Invited review: Genomic selection in dairy cattle: progress and challenges.

Authors:  B J Hayes; P J Bowman; A J Chamberlain; M E Goddard
Journal:  J Dairy Sci       Date:  2009-02       Impact factor: 4.034

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