Genomic prediction of dichotomous traits with Bayesian logistic models

Technow, Frank; Melchinger, Albrecht E.

doi:10.1007/s00122-013-2041-9

Cited by 18 publications

(11 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the introduction of genomic selection models from both the Bayesian (e.g. Technow and Melchinger 2013 ) and Frequentist methodology (Piepho 2009 ; Schulz-Streeck and Piepho 2010 ; Hofheinz and Frisch 2014 ) as well as machine learning methods (Ogutu et al 2011 , 2012 ) have been applied in plant breeding. Although great effort was put into developing these models no method showed clear superiority over the others across species or traits (Heslot et al 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Genomic selection across multiple breeding cycles in applied bread wheat breeding

Michel

Ametz²,

Güngör³

et al. 2016

Theor Appl Genet

104

View full text Add to dashboard Cite

five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of r GS = 0.51 for protein content, r GS = 0.38 for grain yield and r GS = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to r GS = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to r GS = 0.19 for this derived trait.

show abstract

Section: Discussionmentioning

confidence: 99%

Genomic selection across multiple breeding cycles in applied bread wheat breeding

Michel

Ametz²,

Güngör³

et al. 2016

Theor Appl Genet

104

View full text Add to dashboard Cite

show abstract

“…Genetic data is simulated using the CRAN package “hypred” (Technow, 2014). Mixed model software is publicly available via CRAN (Package EMMREML) (Akdemir and Godfrey, 2015).…”

Section: Supplementary Materialsmentioning

confidence: 99%

Efficient Breeding by Genomic Mating

2016

View full text Add to dashboard Cite

Selection in breeding programs can be done by using phenotypes (phenotypic selection), pedigree relationship (breeding value selection) or molecular markers (marker assisted selection or genomic selection). All these methods are based on truncation selection, focusing on the best performance of parents before mating. In this article we proposed an approach to breeding, named genomic mating, which focuses on mating instead of truncation selection. Genomic mating uses information in a similar fashion to genomic selection but includes information on complementation of parents to be mated. Following the efficiency frontier surface, genomic mating uses concepts of estimated breeding values, risk (usefulness) and coefficient of ancestry to optimize mating between parents. We used a genetic algorithm to find solutions to this optimization problem and the results from our simulations comparing genomic selection, phenotypic selection and the mating approach indicate that current approach for breeding complex traits is more favorable than phenotypic and genomic selection. Genomic mating is similar to genomic selection in terms of estimating marker effects, but in genomic mating the genetic information and the estimated marker effects are used to decide which genotypes should be crossed to obtain the next breeding population.

show abstract

“…Genomic selection [ 1 ], enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding [ 2 ]. Since its inception, attempts to improve prediction accuracy have focused on: developing improved and specialized statistical models [ 3 – 6 ], increasing the marker density used [ 7 – 9 ], increasing the size and defining optimal designs of estimation sets [ 10 – 13 ] and better understanding the genetic determinants driving prediction accuracy [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

et al. 2015

Self Cite

View full text Add to dashboard Cite

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.

show abstract

Genomic prediction of dichotomous traits with Bayesian logistic models

Cited by 18 publications

References 28 publications

Genomic selection across multiple breeding cycles in applied bread wheat breeding

Genomic selection across multiple breeding cycles in applied bread wheat breeding

Efficient Breeding by Genomic Mating

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

Contact Info

Product

Resources

About