Genomic selection
Abstract
Summary: Genomic selection is a form of marker‐assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.
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Animal breeders have used genomic selection successfully for a decade, selecting sires and dams on predicted breeding values from full genomic marker sets.
“Animal breeders have been using ‘genomic selection’ methods with great success for the past decade72, basing their selection of sires and dams on the overall predicted breeding value determined from the full set of genomic markers that capture variation distributed throughout the genome.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [72]
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917 · see citing works
Self-citationParkes, M., Barrett, J. C., Prescott, N. J., Tremelling, M., Anderson, C. A., Fisher, S. A., Roberts, R. G., Nimmo, E. R., Cummings, F. J. R., Soars, D., et al. (2007) · Nature Genetics · doi:10.1038/ng2061 · Cited by 1,144 · see citing works
Strategy for applying genome‐wide selection in dairy cattle
Schaeffer, L. R. (2006) · Journal of Animal Breeding and Genetics · doi:10.1111/j.1439-0388.2006.00595.x · Cited by 863 · see citing works
Prediction of individual genetic risk to disease from genome-wide association studiesPreprint
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007) · Genome Research · doi:10.1101/gr.6665407 · Cited by 714 · see citing works
Self-citationCommercial application of marker- and gene-assisted selection in livestock: strategies and lessons.
Dekkers, J. C. M. (2004) · PubMed · doi:10.2527/2004.8213_supple313x · Cited by 692 · see citing works
Molecular Dissection of Complex Traits
Lee, M. J. (1998) · Crop Science · doi:10.2135/cropsci1998.0011183x003800040039x · Cited by 576 · see citing works
Accuracy of Genomic Selection Using Different Methods to Define Haplotypes
Calus, M. P. L., Meuwissen, T. H. E., de Roos, A. P. W., & Veerkamp, R. F. (2008) · Genetics · doi:10.1534/genetics.107.080838 · Cited by 413 · see citing works
Estimating Polygenic Effects Using Markers of the Entire Genome
Xu, S. (2003) · Genetics · doi:10.1093/genetics/163.2.789 · Cited by 403 · see citing works
The 9th World Congress on Genetics Applied to Livestock Production
Smaragdov, M. G. (2011) · Russian Journal of Genetics · doi:10.1134/s1022795411050140 · Cited by 360 · see citing works
Meuwissen, T. H. E., Karlsen, A., Lien, S., Olsaker, I., & Goddard, M. E. (2002) · Genetics · doi:10.1093/genetics/161.1.373 · Cited by 283 · see citing works
Self-citationArmstrong, D. T., Kotaras, P. J., & Earl, C. R. (1997) · Reproduction Fertility and Development · doi:10.1071/r96080 · Cited by 126 · see citing works
Genomewide Analysis of Epistatic Effects for Quantitative Traits in Barley
Xu, S., & Jia, Z. (2007) · Genetics · doi:10.1534/genetics.106.066571 · Cited by 122 · see citing works
Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle
Hayes, B. J., Chamberlain, A. J., McPartlan, H. C., et al. (2007) · Genetics Research · doi:10.1017/s0016672307008865 · Cited by 109 · see citing works
Self-citationComparing Linkage Disequilibrium-Based Methods for Fine Mapping Quantitative Trait Loci
Grapes, L., Dekkers, J. C. M., Rothschild, M. F., & Fernando, R. L. (2004) · Genetics · doi:10.1534/genetics.166.3.1561 · Cited by 100 · see citing works
Benefits from marker-assisted selection under an additive polygenic genetic model1
Villanueva, B., Pong‐Wong, R., Fernández, J., & Toro, M. Á. (2005) · Journal of Animal Science · doi:10.2527/2005.8381747x · Cited by 86 · see citing works
Zhao, H. H., Fernando, R. L., & Dekkers, J. C. M. (2007) · Genetics · doi:10.1534/genetics.106.066480 · Cited by 81 · see citing works
Meuwissen, T. H. E., & Goddard, M. E. (2007) · Genetics · doi:10.1534/genetics.107.070953 · Cited by 59 · see citing works
Self-citationOptimal Effective Population Size for the Global Population of Black and White Dairy Cattle
Goddard, M. E. (1992) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(92)78052-7 · Cited by 58 · see citing works
Self-citationOptimal weighting of information in marker-assisted selection
Whittaker, J. C., Haley, C. S., & Thompson, R. (1997) · Genetics Research · doi:10.1017/s0016672397002711 · Cited by 44 · see citing works
The Number of Loci That Affect Milk Production Traits in Dairy Cattle
Chamberlain, A. J., McPartlan, H. C., & Goddard, M. E. (2007) · Genetics · doi:10.1534/genetics.107.077784 · Cited by 34 · see citing works
Self-citationCorrecting for bias in estimation of quantitative trait loci effects
Weller, J. I., Shlezinger, M., & Ron, M. (2005) · Genetics Selection Evolution · doi:10.1186/1297-9686-37-6-501 · Cited by 33 · see citing works
Mapping genes for quantitative traits using linkage disequilibrium
Goddard, M. E. (1991) · Genetics Selection Evolution · doi:10.1186/1297-9686-23-s1-s131 · Cited by 22 · see citing works
Self-citationUse of markers in linkage disequilibrium with QTL in breeding programs.
Hayes, B. J., Chamberlain, A. J., & Goddard, M. E. (2006) · Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006 · Cited by 22 · see citing works
Self-citationOptimisation of response using molecular data.
Goddard, M. E., & Hayes, B. J. (2002) · Cited by 20 · see citing works
Self-citationMarker assisted estimation of breeding values when marker information is missing on many animals
Meuwissen, T. H. E., & Goddard, M. E. (1999) · Genetics Selection Evolution · doi:10.1186/1297-9686-31-4-375 · Cited by 14 · see citing works
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Speed, D., & Balding, D. J. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3821 · Cited by 316 · see citing works
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- Authors
- Goddard, M. E., & Hayes, B. J.
- Affiliations
- The University of Melbourne (AU)
- Department of Primary Industries (AU)
- Venue
- Journal of Animal Breeding and Genetics
- Publisher
- Wiley
- Published
- 2007-12
- Volume
- 124
- Issue
- 6
- Pages
- 323-330
- ISSN
- 0044-3581 (ISSN-L)
- 0931-2668
- 1439-0388
- OpenAlex
- W1982957378
- Cited by
- 721
- References
- 26
- Citations by year
- 2026: 31 · 2025: 45 · 2024: 60 · 2023: 42 · 2022: 66 · 2021: 49 · 2020: 68 · 2019: 44 · 2018: 39 · 2017: 45 · 2016: 36 · 2015: 34 · 2014: 27 · 2013: 28 · 2012: 34
- FWCI
- 8.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Animal Breeding and Genetics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- genomic selection, marker-assisted selection, single nucleotide polymorphisms, quantitative trait loci, linkage disequilibrium, breeding value estimation, genetic evaluation, genetic improvement, marker genotypes
- MeSH terms
- Animals, Breeding, Genetic Markers, Phenotype, Selection, Genetic, Genome, Quantitative Trait Loci
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