» Articles » PMID: 29559995

Non-additive Effects in Genomic Selection

Overview
Journal Front Genet
Date 2018 Mar 22
PMID 29559995
Citations 82
Authors
Affiliations
Soon will be listed here.
Abstract

In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.

Citing Articles

Whole-genome resequencing of Japanese large-sized tomato cultivars provides insights into the history of modern breeding.

Yamamoto E, Matsunaga H, Ohyama A, Nunome T, Yamaguchi H, Miyatake K Breed Sci. 2025; 74(4):344-353.

PMID: 39872322 PMC: 11769584. DOI: 10.1270/jsbbs.24004.


Genomic prediction accounting for dominance and epistatic genetic effects on litter size traits in Large White pigs.

Chen J, Dou T, Wu Z, Bai L, Xu M, Zhang Y J Anim Sci. 2025; 103.

PMID: 39774780 PMC: 11776020. DOI: 10.1093/jas/skaf004.


Optimizing drought tolerance in cassava through genomic selection.

da Costa W, Bandeira E Souza M, Azevedo C, Nascimento M, Morgante C, Borel J Front Plant Sci. 2024; 15:1483340.

PMID: 39737377 PMC: 11683140. DOI: 10.3389/fpls.2024.1483340.


SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection.

Peixoto M, Amadeu R, Bhering L, Ferrao L, Munoz P, Resende Jr M Plant Genome. 2024; 18(1):e20533.

PMID: 39604031 PMC: 11726409. DOI: 10.1002/tpg2.20533.


Modeling gene interactions in polygenic prediction via geometric deep learning.

Li H, Zeng J, Snyder M, Zhang S Genome Res. 2024; 35(1):178-187.

PMID: 39562137 PMC: 11789630. DOI: 10.1101/gr.279694.124.


References
1.
Wellmann R, Bennewitz J . Bayesian models with dominance effects for genomic evaluation of quantitative traits. Genet Res (Camb). 2012; 94(1):21-37. DOI: 10.1017/S0016672312000018. View

2.
Akdemir D, Jannink J . Locally epistatic genomic relationship matrices for genomic association and prediction. Genetics. 2015; 199(3):857-71. PMC: 4349077. DOI: 10.1534/genetics.114.173658. View

3.
Ramayo-Caldas Y, Mach N, Lepage P, Levenez F, Denis C, Lemonnier G . Phylogenetic network analysis applied to pig gut microbiota identifies an ecosystem structure linked with growth traits. ISME J. 2016; 10(12):2973-2977. PMC: 5148198. DOI: 10.1038/ismej.2016.77. View

4.
Tusell L, Perez-Rodriguez P, Forni S, Gianola D . Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield. J Anim Breed Genet. 2014; 131(2):105-15. DOI: 10.1111/jbg.12070. View

5.
Sevillano C, Vandenplas J, Bastiaansen J, Calus M . Empirical determination of breed-of-origin of alleles in three-breed cross pigs. Genet Sel Evol. 2016; 48(1):55. PMC: 4973529. DOI: 10.1186/s12711-016-0234-9. View