Literature DB >> 35451772

Genomic Prediction of Complex Traits, Principles, Overview of Factors Affecting the Reliability of Genomic Prediction, and Algebra of the Reliability.

Jean-Michel Elsen1.   

Abstract

The quality of the predictions of genetic values based on the genotyping of neutral markers (GEBVs) is a key information to decide whether or not to implement genomic selection. This quality depends on the part of the genetic variability captured by the markers and on the precision of the estimate of their effects. Selection index theory provided the framework for evaluating the accuracy of GEBVs once the information had been gathered, with the genomic relationship matrix (GRM) playing a central role. When this accuracy must be known a priori, the theory of quantitative genetics gives clues to calculate the expectation of this GRM. This chapter makes a critical inventory of the methods developed to calculate these accuracies a posteriori and a priori. The most significant factors affecting this accuracy are described (size of the reference population, number of markers, linkage disequilibrium, heritability).
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Accuracy of predictions; Effective number of loci; Genomic prediction; Genomic relationships matrix

Mesh:

Year:  2022        PMID: 35451772     DOI: 10.1007/978-1-0716-2205-6_2

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


  55 in total

1.  Prediction of total genetic value using genome-wide dense marker maps.

Authors:  T H Meuwissen; B J Hayes; M E Goddard
Journal:  Genetics       Date:  2001-04       Impact factor: 4.562

2.  Effect of total allelic relationship on accuracy of evaluation and response to selection.

Authors:  A Nejati-Javaremi; C Smith; J P Gibson
Journal:  J Anim Sci       Date:  1997-07       Impact factor: 3.159

3.  Using the genomic relationship matrix to predict the accuracy of genomic selection.

Authors:  M E Goddard; B J Hayes; T H E Meuwissen
Journal:  J Anim Breed Genet       Date:  2011-12       Impact factor: 2.380

4.  Different methods to calculate genomic predictions--comparisons of BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP).

Authors:  M Koivula; I Strandén; G Su; E A Mäntysaari
Journal:  J Dairy Sci       Date:  2012-07       Impact factor: 4.034

5.  Efficient methods to compute genomic predictions.

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

Review 6.  Additive genetic variability and the Bayesian alphabet.

Authors:  Daniel Gianola; Gustavo de los Campos; William G Hill; Eduardo Manfredi; Rohan Fernando
Journal:  Genetics       Date:  2009-07-20       Impact factor: 4.562

7.  Efficiency of marker-assisted selection in the improvement of quantitative traits.

Authors:  R Lande; R Thompson
Journal:  Genetics       Date:  1990-03       Impact factor: 4.562

8.  Genomic BLUP decoded: a look into the black box of genomic prediction.

Authors:  David Habier; Rohan L Fernando; Dorian J Garrick
Journal:  Genetics       Date:  2013-05-02       Impact factor: 4.562

9.  Common SNPs explain a large proportion of the heritability for human height.

Authors:  Jian Yang; Beben Benyamin; Brian P McEvoy; Scott Gordon; Anjali K Henders; Dale R Nyholt; Pamela A Madden; Andrew C Heath; Nicholas G Martin; Grant W Montgomery; Michael E Goddard; Peter M Visscher
Journal:  Nat Genet       Date:  2010-06-20       Impact factor: 38.330

10.  Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle.

Authors:  François Guillaume; Sébastien Fritz; Didier Boichard; Tom Druet
Journal:  Genet Sel Evol       Date:  2007-12-21       Impact factor: 4.297

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