Increased accuracy of artificial selection by using the realized relationship matrix.

By Ben J. Hayes, Peter M. Visscher, Michael E. Goddard. Genetics Research, 2009

627 citations recorded by the source corpus.

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 N(e)=100, approximately 12474 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 [corrected].

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/c63b8fc7cec9d148717b3897326a550bc9e8fe87

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Citations in the corpus

The Impact of Genetic Architecture on Genome-Wide Evaluation Methods (2010, methodology): “Both the number of haplotype blocks and Me increase with increasing Ne and in close relatives haplotype blocks will be long and Me will be small (Hayes et al. 2009c).”
Genome-wide association studies establish that human intelligence is highly heritable and polygenic (2011, methodology): “28 For the Norwegian sample, SNP effects were estimated from the entire CAGES sample by exploiting the mathematical equivalence of a model based upon genome-wide genetic effects and individual SNP effects”
Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor (2013, positive): “There are several ways of computing genomic relationships [7,21–24]; here, we have adopted one of the conventions which consist of centering (by subtracting the mean, 2hl ) and standardizing, by dividing by the variance 2hl 1{hl ð Þ, of each marker before computing genomic relationships.”
Common SNPs explain a large proportion of heritability for human height (2010, background): “An alternative approach designed to overcome this problem is to fit all the SNPs simultaneousl”
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP (2011, neutral): “[1]) (Hayes et al., 2009).”
Genomic selection in plant breeding: from theory to practice. (2010, background): “ship matrix calculated using marker data [17, 54].”
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. (2012, methodology): “In the second set of animals (2,797 Holstein and Jersey progeny-tested bulls), genotyped for the 50K panel, quality filters were imposed as described in Hayes et al. (2009a).”
Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies (2013, methodology): “We used the product of centered and scaled genotype matrix as an estimate of relatedness [58, 59, 16].”
Genomic Selection in Plant Breeding (2011, methodology): “As discussed in the methods section, RR-BLUP modeling marker effects as random are equivalent to a mixed model analysis wherein observations have random effects that covary according to a relationship matrix calculated using marker data (Habier et al., 2007; Hayes et al., 2009c).”