Genomic selection: Status in different species and challenges for breeding.
Abstract: Technical advances and development in the market for genomic tools have facilitated access to whole-genome data across species. Building-up on the acquired knowledge of the genome sequences, large-scale genotyping has been optimized for broad use, so genotype information can be routinely used to predict genetic merit. Genomic selection (GS) refers to the use of aggregates of estimated marker effects as predictors which allow improved individual differentiation at young age. Realizable benefits of GS are influenced by several factors and vary in quantity and quality between species. General characteristics and challenges of GS in implementation and routine application are described, followed by an overview over the current status of its use, prospects and challenges in important animal species. Genetic gain for a particular trait can be enhanced by shortening of the generation interval, increased selection accuracy and increased selection intensity, with species- and breed-specific relevance of the determinants. Reliable predictions based on genetic marker effects require assembly of a reference for linking of phenotype and genotype data to allow estimation and regular re-estimation. Experiences from dairy breeding have shown that international collaboration can set the course for fast and successful implementation of innovative selection tools, so genomics may significantly impact the structures of future breeding and breeding programmes. Traits of great and increasing importance, which were difficult to improve in the conventional systems, could be emphasized, if continuous availability of high-quality phenotype data can be assured. Equally elaborate strategies for genotyping and phenotyping will allow tailored approaches to balance efficient animal production, sustainability, animal health and welfare in future.
© 2013 Blackwell Verlag GmbH.
Publication Date: 2013-08-24 PubMed ID: 23962210DOI: 10.1111/rda.12201Google Scholar: Lookup
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The research article discusses the use and challenges of Genomic Selection (GS) across animal species, a method that uses information about genetic markers to predict an organism’s traits and genetic worth from a young age. It provides an overview of GS applications, its benefits and limitations as affected by the characteristics of individual species and breeds, as well as the need for a reliable prediction model that involves the linking of phenotype and genotype data.
Genomic Selection and its Applications
- Genomic Selection (GS) is described as a selection process based on aggregates of estimated marker effects. It uses genetic markers throughout the genome to predict an individual’s genetic merit by integrating genotype data and predictive analyses.
- This research paper delves into the developments in large-scale genotyping, propelled by technological advancements in genomic tools, and states how it is being used routinely in animal breeding programs across diverse species.
Factors Influencing Realizable Benefits
- The research highlights that the quantity and quality of the benefits obtained through GS are varied and dependent on several factors. Among the noted factors are the genetic gain for a particular trait, consistency in the availability of high-quality phenotype data, and the overall relevance of determinants in a particular breed or species.
- Genetic gain can be enhanced by shortening the generation interval, increasing selection accuracy and intensifying selection.
- It further notes that traits which were previously hard to improve under conventional breeding systems could potentially benefit from GS if premium phenotype data are consistently available.
Need for a Reliable Prediction Model
- The research underscores the need for a reliable prediction model based on genetic markers which necessitates the collection and assembly of a reference standard that links phenotype and genotype data.
- Routinely re-evaluating the reference standard based on these phenotype and genotype linkages is essential to ensure its accuracy.
Strategies for Implementation and Challenges
- The article also covers practical application challenges encountered in implementing GS, including strategies for genotyping and phenotyping that require high levels of coordination and data collection.
- It highlights the importance of international collaboration to fast-track implementation of innovative tools for selection, citing experiences from dairy breeding.
- Furthermore, the paper notes that genomics may significantly alter breeding structures and breeding programs in the future. This comprises balancing efficient animal production, sustainability, animal health, and welfare through bespoke approaches.
Cite This Article
APA
Stock KF, Reents R.
(2013).
Genomic selection: Status in different species and challenges for breeding.
Reprod Domest Anim, 48 Suppl 1, 2-10.
https://doi.org/10.1111/rda.12201 Publication
Researcher Affiliations
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Verden, Germany. friederike.katharina.stock@vit.de
MeSH Terms
- Animals
- Aquaculture
- Breeding / methods
- Cattle / genetics
- Dairying
- Female
- Genotyping Techniques / veterinary
- Goats / genetics
- Horses / genetics
- Male
- Poultry / genetics
- Quantitative Trait Loci / genetics
- Selection, Genetic
- Sequence Analysis, DNA
- Species Specificity
- Sus scrofa / genetics
Citations
This article has been cited 18 times.- Du A, Zhao F, Liu Y, Xu L, Chen K, Sun D, Han B. Genetic polymorphisms of PKLR gene and their associations with milk production traits in Chinese Holstein cows. Front Genet 2022;13:1002706.
- Kudinov AA, Mäntysaari EA, Pitkänen TJ, Saksa EI, Aamand GP, Uimari P, Strandén I. Single-step genomic evaluation of Russian dairy cattle using internal and external information. J Anim Breed Genet 2022 May;139(3):259-270.
- Zhao C, Teng J, Zhang X, Wang D, Zhang X, Li S, Jiang X, Li H, Ning C, Zhang Q. Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey. Front Genet 2021;12:728764.
- Rios EF, Andrade MHML, Resende MFR, Kirst M, de Resende MDV, de Almeida Filho JE, Gezan SA, Munoz P. Genomic prediction in family bulks using different traits and cross-validations in pine. G3 (Bethesda) 2021 Sep 6;11(9).
- Orbán L, Shen X, Phua N, Varga L. Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?. Front Genet 2021;12:506754.
- Eynard SE, Croiseau P, Laloë D, Fritz S, Calus MPL, Restoux G. Which Individuals To Choose To Update the Reference Population? Minimizing the Loss of Genetic Diversity in Animal Genomic Selection Programs. G3 (Bethesda) 2018 Jan 4;8(1):113-121.
- Brenig B, Schütz E. Recent development of allele frequencies and exclusion probabilities of microsatellites used for parentage control in the German Holstein Friesian cattle population. BMC Genet 2016 Jan 8;17:18.
- Gao N, Li J, He J, Xiao G, Luo Y, Zhang H, Chen Z, Zhang Z. Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model. BMC Genet 2015 Oct 14;16:120.
- Xie FY, Feng YL, Wang HH, Ma YF, Yang Y, Wang YC, Shen W, Pan QJ, Yin S, Sun YJ, Ma JY. De Novo Assembly of the Donkey White Blood Cell Transcriptome and a Comparative Analysis of Phenotype-Associated Genes between Donkeys and Horses. PLoS One 2015;10(7):e0133258.
- Eynard SE, Windig JJ, Leroy G, van Binsbergen R, Calus MP. The effect of rare alleles on estimated genomic relationships from whole genome sequence data. BMC Genet 2015 Mar 12;16:24.
- Li J, Guo P, Zhang Y, Ma H, Zhao Z, Wang Y, Wang Z, Chen Y, Xu L, Zhang L, Gao H, Gao X, Li J, Zhu B. ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction. Genet Sel Evol 2026 Jan 13;58(1):6.
- Chen Y, Wang K, Wang Q, Cao Y, Zhao R, Zhang Y, Li J. Genomic and Transcriptomic Profiling of Amino Acid Compositions in Common Carp Fillets. Animals (Basel) 2025 May 6;15(9).
- Jilo DD, Abebe BK, Wang J, Guo J, Li A, Zan L. Long non-coding RNA (LncRNA) and epigenetic factors: their role in regulating the adipocytes in bovine. Front Genet 2024;15:1405588.
- Zhou Y, Wang Q, Wang Q, Yan Y, Li G, Wu G, Yang N, Wen C. Pedigree reconstruction based on genotype data in chickens. Poult Sci 2024 Dec;103(12):104327.
- Tian R, Mahmoodi M, Tian J, Esmailizadeh Koshkoiyeh S, Zhao M, Saminzadeh M, Li H, Wang X, Li Y, Esmailizadeh A. Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality. Genes (Basel) 2024 Aug 22;15(8).
- Du A, Guo Z, Chen A, Xu L, Sun D, Han B. PC Gene Affects Milk Production Traits in Dairy Cattle. Genes (Basel) 2024 May 29;15(6).
- Wadood AA, Zhang X. The Omics Revolution in Understanding Chicken Reproduction: A Comprehensive Review. Curr Issues Mol Biol 2024 Jun 20;46(6):6248-6266.
- Li W, Li W, Song Z, Gao Z, Xie K, Wang Y, Wang B, Hu J, Zhang Q, Ning C, Wang D, Fan X. Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits. Genes (Basel) 2024 Apr 3;15(4).
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