Literature DB >> 35451776

Overview of Major Computer Packages for Genomic Prediction of Complex Traits.

Giovanny Covarrubias-Pazaran1,2.   

Abstract

Genomic prediction models are showing their power to increase the rate of genetic gain by boosting all the elements of the breeder's equation. Insight into the factors associated with the successful implementation of this prediction model is increasing with time but the technology has reached a stage of acceptance. Most genomic prediction models require specialized computer packages based mainly on linear models and related methods. The number of computer packages has exploded in recent years given the interest in this technology. In this chapter, we explore the main computer packages available to fit these models; we also review the special features, strengths, and weaknesses of the methods behind the most popular computer packages.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Computer packages; Linear models; Prediction model; REML

Mesh:

Year:  2022        PMID: 35451776     DOI: 10.1007/978-1-0716-2205-6_6

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


  21 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.  GCTA: a tool for genome-wide complex trait analysis.

Authors:  Jian Yang; S Hong Lee; Michael E Goddard; Peter M Visscher
Journal:  Am J Hum Genet       Date:  2010-12-17       Impact factor: 11.025

3.  WOMBAT: a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML).

Authors:  Karin Meyer
Journal:  J Zhejiang Univ Sci B       Date:  2007-11       Impact factor: 3.066

4.  C. R. Henderson: contributions to predicting genetic merit.

Authors:  L R Schaeffer
Journal:  J Dairy Sci       Date:  1991-11       Impact factor: 4.034

5.  The impact of genetic relationship information on genome-assisted breeding values.

Authors:  D Habier; R L Fernando; J C M Dekkers
Journal:  Genetics       Date:  2007-12       Impact factor: 4.562

6.  Efficient methods to compute genomic predictions.

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

Review 7.  Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction.

Authors:  William G Hill
Journal:  Genetics       Date:  2014-01       Impact factor: 4.562

8.  An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree.

Authors:  Sang Hong Lee; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2006 Jan-Feb       Impact factor: 4.297

9.  MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.

Authors:  S H Lee; J H J van der Werf
Journal:  Bioinformatics       Date:  2016-01-10       Impact factor: 6.937

10.  Genomic Selection in Multi-environment Crop Trials.

Authors:  Helena Oakey; Brian Cullis; Robin Thompson; Jordi Comadran; Claire Halpin; Robbie Waugh
Journal:  G3 (Bethesda)       Date:  2016-05-03       Impact factor: 3.154

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