The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Increased accuracy of artificial selection by using the realized relationship matrix
Open accessAbstract
Summary: Dense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0·5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0·82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0·3, and Ne=1000, approximately 5750 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0·7.
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Cites
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000008 · Cited by 1,039
Self-citationRecent human effective population size estimated from linkage disequilibrium
Tenesa, A., Navarro, P., Hayes, B. J., Duffy, D. L., Clarke, G. M., Goddard, M. E., & Visscher, P. M. (2007) · Genome Research · doi:10.1101/gr.6023607 · Cited by 438
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Cited by
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Self-citationCommon SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446
Self-citationRidge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
Endelman, J. B. (2011) · The Plant Genome · doi:10.3835/plantgenome2011.08.0024 · Cited by 2,094
FaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209
Efficient multivariate linear mixed model algorithms for genome-wide association studies
Zhou, X., & Stephens, M. (2014) · Nature Methods · doi:10.1038/nmeth.2848 · Cited by 841
Metadata
- Authors
- HAYES, B. J., VISSCHER, P. M., & GODDARD, M. E.
- Venue
- Genetics Research
- Publisher
- Hindawi Limited
- Published
- 2009-02
- Volume
- 91
- Issue
- 1
- Pages
- 47-60
- ISSN
- 0016-6723 (ISSN-L)
- 1469-5073
- OpenAlex
- W2127601863
- Cited by
- 662
- References
- 3
- FWCI
- 18.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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