The Impact of Genetic Architecture on Genome-Wide Evaluation Methods

By Hans D. Daetwyler, R. Pong-Wong, B. Villanueva, J. A. Woolliams. Genetics, 2010

746 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d770bac28f555394b5405e31d7fca9fffcbf1ed3

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Citations in the corpus

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, background): “Daetwyler et al. (2010b) showed in a simulation study that the accuracy of G-BLUP is not affected by the number of QTL; however, the predictive performance of BayesC was greatly affected by that factor and its accuracy was higher when the number of QTL was low and decreased with increasing number…”
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program (2011, background): “: GENOMIC SELECTION ACCURACY 71 population size (Ne) and (ii) trait architecture (Daetwyler et al., 2010).”
Optimal Design of Preliminary Yield Trials with Genome-Wide Markers (2014, background): “Although no biological interpretation is needed to perform the economic optimization, theory suggests the b parameter is approximately the ratio between the number of independent chromosome segments (Me) and the heritability (Daetwyler et al., 2010).”
Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population (2010, positive): “2009) and it has been shown by simulation that this equivalency occurs when many QTL affect a trait (Daetwyler et al. 2010).”
Accuracy of Genomewide Selection for Different Traits with Constant Population Size, Heritability, and Number of Markers (2013, neutral): “[1]) derived by Daetwyler et al. (2008, 2010) assumes that the genome is sui ciently saturated with markers, and we surmise that a lack of increase in r MP at er a certain N M is reached indicated marker saturation in the populations we studied.”
Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats (2011, methodology): “Th e empirical study of Daetwyler et al. (2010a) in sheep demonstrated that the breed of the selection candidates that was most represented in mul-tibreed training populations achieved higher accuracies.”
Extension of the bayesian alphabet for genomic selection (2011, methodology): “This can be explained partly by the fact that [23] considered the GEBV accuracy of the offspring of training individuals, meaning that genetic-relationships were important; these were captured better by the SNPs, when more SNPs were fitted as in G-BLUP [7].”
Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection (2020, background): “[84] reported an improvement in prediction accuracy of 50% by increasing the TP size from 500 to 2000.”
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. (2017, background): “When a trait is affected by a large number of loci, GP accuracy depends on several genetic factors: (i) the size and genetic diversity of the TRN population and its relationship with the TST population [4]; that is, whether the cultivars in the TRN are relatives (close and/or distant) of cultivars i”
Genome-wide selection in cassava (2012, background): “…and research with simulated data have demonstrated excellent results using this technology for various crops (Bernardo and Yu 2007; Resende et al. 2008; Wong and Bernardo 2008; Heffner et al. 2009; Zhong et al. 2009; Daetwyler et al. 2010; Meuwissen and Goddard 2010; Villanueva et al. 2011).”
Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor (2013, neutral): “In summary, our approach is complementary to that of Goddard [28] and Daetwyler [30]; we focused on a much narrower problem and by virtue of that were able to arrive at closed-form formulas that account for a sizable proportion of the shrinkage in R(2) due to imperfect LD without making strong assum”