Literature DB >> 25387018

Accelerating improvement of livestock with genomic selection.

Theo Meuwissen1, Ben Hayes, Mike Goddard.   

Abstract

Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data.

Entities:  

Keywords:  complex traits; genetic improvement; marker-assisted selection; use of genome sequence data

Mesh:

Substances:

Year:  2013        PMID: 25387018     DOI: 10.1146/annurev-animal-031412-103705

Source DB:  PubMed          Journal:  Annu Rev Anim Biosci        ISSN: 2165-8102            Impact factor:   8.923


  96 in total

1.  The Valdostana goat: a genome-wide investigation of the distinctiveness of its selective sweep regions.

Authors:  Andrea Talenti; Francesca Bertolini; Giulio Pagnacco; Fabio Pilla; Paolo Ajmone-Marsan; Max F Rothschild; Paola Crepaldi
Journal:  Mamm Genome       Date:  2017-03-02       Impact factor: 2.957

2.  Efficiency of genomic prediction of non-assessed single crosses.

Authors:  José Marcelo Soriano Viana; Helcio Duarte Pereira; Gabriel Borges Mundim; Hans-Peter Piepho; Fabyano Fonseca E Silva
Journal:  Heredity (Edinb)       Date:  2017-11-28       Impact factor: 3.821

3.  Relevance of genetic relationship in GWAS and genomic prediction.

Authors:  Helcio Duarte Pereira; José Marcelo Soriano Viana; Andréa Carla Bastos Andrade; Fabyano Fonseca E Silva; Geísa Pinheiro Paes
Journal:  J Appl Genet       Date:  2017-11-30       Impact factor: 3.240

4.  Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

Authors:  Anderson Antonio Carvalho Alves; Rebeka Magalhães da Costa; Tiago Bresolin; Gerardo Alves Fernandes Júnior; Rafael Espigolan; André Mauric Frossard Ribeiro; Roberto Carvalheiro; Lucia Galvão de Albuquerque
Journal:  J Anim Sci       Date:  2020-06-01       Impact factor: 3.159

5.  Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

Authors:  Pau Bellot; Gustavo de Los Campos; Miguel Pérez-Enciso
Journal:  Genetics       Date:  2018-08-31       Impact factor: 4.562

6.  Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects.

Authors:  Thinh Tuan Chu; Mark Henryon; Just Jensen; Birgitte Ask; Ole Fredslund Christensen
Journal:  Genet Sel Evol       Date:  2021-01-04       Impact factor: 4.297

7.  The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

Authors:  Anna R Rogers; Jeffrey C Dunne; Cinta Romay; Martin Bohn; Edward S Buckler; Ignacio A Ciampitti; Jode Edwards; David Ertl; Sherry Flint-Garcia; Michael A Gore; Christopher Graham; Candice N Hirsch; Elizabeth Hood; David C Hooker; Joseph Knoll; Elizabeth C Lee; Aaron Lorenz; Jonathan P Lynch; John McKay; Stephen P Moose; Seth C Murray; Rebecca Nelson; Torbert Rocheford; James C Schnable; Patrick S Schnable; Rajandeep Sekhon; Maninder Singh; Margaret Smith; Nathan Springer; Kurt Thelen; Peter Thomison; Addie Thompson; Mitch Tuinstra; Jason Wallace; Randall J Wisser; Wenwei Xu; A R Gilmour; Shawn M Kaeppler; Natalia De Leon; James B Holland
Journal:  G3 (Bethesda)       Date:  2021-02-09       Impact factor: 3.154

Review 8.  Classical, Molecular, and Genomic Cytogenetics of the Pig, a Clinical Perspective.

Authors:  Brendan Donaldson; Daniel A F Villagomez; W Allan King
Journal:  Animals (Basel)       Date:  2021-04-27       Impact factor: 2.752

9.  Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris.

Authors:  Ainhoa Calleja-Rodriguez; ZhiQiang Chen; Mari Suontama; Jin Pan; Harry X Wu
Journal:  Front Plant Sci       Date:  2021-07-07       Impact factor: 5.753

10.  Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata.

Authors:  Sang V Vu; Wayne Knibb; Cedric Gondro; Sankar Subramanian; Ngoc T H Nguyen; Mobashwer Alam; Michael Dove; Arthur R Gilmour; In Van Vu; Salma Bhyan; Rick Tearle; Le Duy Khuong; Tuan Son Le; Wayne O'Connor
Journal:  Front Genet       Date:  2021-07-08       Impact factor: 4.599

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