Extension of the bayesian alphabet for genomic selection

By David Habier, Rohan L. Fernando, K. Kızılkaya, Dorian Garrick. BMC Bioinformatics, 2011

1,179 citations recorded by the source corpus.

BackgroundTwo Bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.ResultsEstimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesCπ than for BayesDπ, and longest for our implementation of BayesA.ConclusionsCollectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesCπ has merit for routine applications.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/b75afdb5255412c3d749d72908f4220d49be97a0

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In Genetics

Citations in the corpus

GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction (2020, methodology): “, Bayes A, B, and CPi) can be used not only to predict EBVs and phenotypes of individuals by summing the effects of all markers, but also to map genetic markers associated with phenotypes of interest [28].”
Genomic Relationship Matrix for Correcting Pedigree Errors in Breeding Populations: Impact on Genetic Parameters and Genomic Selection Accuracy (2014, background): “Recently, a number of analytical approaches (Gianola et al., 2006; de los Campos et al., 2009; Habier et al., 2011) have been developed to study factors that contribute to GS accuracy (Habier et al.”
Genome-Wide Regression and Prediction with the BGLR Statistical Package (2014, background): “The scale parameter is treated as unknown, and BGLR assigns to this parameter a gamma density with rate and shape parameters r and s , respectively.”
Genomic Selection for Quantitative Adult Plant Stem Rust Resistance in Wheat (2014, methodology): “Genomic best linear unbiased prediction (Bernardo, 1994; Piepho, 2009) treats markers homogenously, whereas Bayesian methods such as BL (Park and Casella, 2008) and BCp (Habier et al., 2011) treat markers of different effect sizes heterogeneously.”
Polygenic scores via penalized regression on summary statistics (2016, methodology): “In principle, various machine learning methods or Bayesian methods can be applied in the construction of PGS, as they have been applied in the estimation of breeding values in animal studies (Meuwissen et al., 2001; Abraham et al., 2013; Szymczak et al., 2009; Habier et al., 2011; Pirinen et al., 20”
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. (2012, background): “However, one potential criticism of both BayesB and BayesSSVS is that the proportion of SNP in each distribution was not sampled appropriately, such that the means of the posterior distributions of the proportion of SNP with a zero or nonzero effect closely reflected the prior values of these propor”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “Given that a majority of the existing Bayesian genomic prediction models, including Bayes alphabetic methods [Habier et al., 2011; Hayes et al., 2010; Meuwissen and Goddard, 2004; Meuwissen et al., 2001; Verbyla et al., 2009, 2010; Yi et al., 2003], BayesR [Erbe et al., 2012; Moser et al., 2015],…”
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction (2014, background): “Many scientific publications have been written in recent years on both the statistical aspects of the methodology used to implement genomic selection (Gianola et al. 2006; Piepho 2009; Habier et al. 2011) and the utility and potential practical implications of using this novel genetic evaluation tec”
Genomic Selection for Crop Improvement (2017, positive): “Ultimately, it is the genetic relationships at causal loci that influence the effectiveness of training populations to predict trait values in prediction sets and not genetic relationships calculated according to markers (Habier et al. 2013; Campos de los et al. 2013). The genomic relationship matri”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, methodology): “In Bayes B the slab is a scaled-t density, while in BayesC the slab is a normal density.”
Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, methodology): “Here we are echo-ing and reinforcing similar themes emphasized by [34] and [19].”
Genomic Prediction of Hybrid Wheat Performance (2013, background): “(2009), BayesA and BayesB are centered on the prior for SNP effects, which conflicts with the concept of Bayesian learning (Habier et al., 2011).”