Genomic breeding value estimation using nonparametric additive regression models

25Citations
Citations of this article
70Readers
Mendeley users who have this article in their library.

Abstract

Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors) separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped) was predicted using data from the next last generation (genotyped and phenotyped). The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy. © 2009 Bennewitz et al.

Cite

CITATION STYLE

APA

Bennewitz, J., Solberg, T., & Meuwissen, T. (2009). Genomic breeding value estimation using nonparametric additive regression models. Genetics Selection Evolution, 41(1). https://doi.org/10.1186/1297-9686-41-20

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free