Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

G3 : Genes - Genomes - Genetics
Réka HowardWilliam D Beavis

Abstract

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased p...Continue Reading

References

Sep 2, 2008·Talanta·Marcos Almeida BezerraLuciane Amélia Escaleira
Nov 15, 2011·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·Yusheng ZhaoJochen C Reif
Sep 6, 2012·Proceedings of the National Academy of Sciences of the United States of America·Wen HuangTrudy F C Mackay

Citations

Jan 17, 2019·PloS One·Atefeh Ghorbani AghdamShayan Maleknia

Related Concepts

Machine Learning
In Silico
Deviation, Epistatic
Haploid Cell
Reproducibility of Results
Computational Molecular Biology
Genomics
Quantitative Trait Loci
Epistasis, Genetic
Genetic Vectors

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