Genomic selection: prediction of accuracy and maximisation of long term response

By Michael E. Goddard. Genetica, 2009

1,061 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/197c241b54bce47e1803ca889fa8f93194dfa42c

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In Genetics

In Plant Science

Citations in the corpus

Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program (2011, methodology): “Ridge regression best linear unbiased prediction is equivalent to estimating markers eff ects with a realized-relationship matrix determined from markers (Habier et al., 2007; Goddard, 2009; Piepho, 2009).”
Perspectives for Genomic Selection Applications and Research in Plants (2015, positive): “A selection index can be used including the estimated marker effect and allele frequency to prevent the loss of low-frequency alleles to preserve long-term gains (Goddard, 2009).”
Genomic selection for durable stem rust resistance in wheat (2011, background): “Accurate estimation of QTL effects in SCs is achieved by increasing the number of training records (Hayes et al. 2009b; Zhong et al. 2009) and by reestimating QTL effects in the population where the allele frequencies (and therefore QTL effects) and LD structure change due to selection ( Goddard 200”
REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Will genomic selection be a practical method for plant breeding? (2012, background): “Goddard (2009) and Jannink et al. (2010) surveyed the dynamics of long-term selection responses by performing simulation studies, and concluded that GS leads to a more rapid decline in the selection response than PS unless new markers are continually added to the prediction of breeding value.”
Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017, methodology): “, such as when it arises from distinct subpopulations, two popular approaches for addressing this are principal component analysis [13] and linear mixed models [2], [14].”
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores (2015, background): “However, calculating the posterior mean under this model is non-trivial but can be solved numerically.(61) Alternatively, the posterior mode has a simple analytical form.”
Genomewide Prediction Accuracy within 969 Maize Biparental Populations (2014, methodology): “The Me pertains to the idealized concept of having independent chromosome segments, with each segment containing a QTL–marker pair and with all the QTL having additive, equal effects (Daetwyler et al., 2008; Goddard, 2009).”
Using Genetic Distance to Infer the Accuracy of Genomic Prediction (2015, background): “The accuracy of genomic predictions will depend on a number of factors: relatedness among genotyped individuals [7, 8]; the density of the markers [7, 9, 10]; and the genetic architecture of the trait, in particular the allele frequencies of causal variants [11, 12] and the distribution of their eff”
Marker-Assisted Plant Breeding: Principles and Practices (2015, background): “allele frequency at each marker locus (Goddard 2009).”
Genomic selection in plant breeding: from theory to practice. (2010, background): “For example, Goddard [71] proposed varying the weight given to marker information as a function of the allele frequencies at each marker [19].”
Genomic selection in forest tree breeding (2011, methodology): “2001), a number of simulation studies have evaluated some of the parameters that impact the expected performance of genomic selection and compared the selection efficiency with conventional phenotype-based selection methods (Dekkers 2007; Goddard 2009; Goddard and Hayes 2009; Heffner et al. 2009; Ni”
Genomic Selection Accuracy for Grain Quality Traits in Biparental Wheat Populations (2011, background): “Goddard (2009) and Piepho (2009) showed that RR is equivalent to a model where a realized-relationship matrix is determined from marker information to estimate marker eff ects (Habier et al.”
Development and Application of High-density SNP Arrays in Genomic Studies of Domestic Animals (2010, neutral): “Based on most of the studies using simulated data (Goddard, 2009; Hayes et al., 2009b), several major factors which influence the accuracy of genomic selection were recognized: i) the LD extent between SNPs and the QTL; ii) the size of the training population (the individuals both phenotyped and gen”
Genomic evaluation of cattle in a multi-breed context ☆ (2014, background): “As a consequence, the formula describing the effectiveness of genomic prediction (Goddard, 2009) cannot be used directly in the multibreed context.”
Mapping genes for complex traits in domestic animals and their use in breeding programmes (2009, methodology): “Some of the statistical methods for genomic selection have been reviewed elsewher”