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Activity Number: 644
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #309648
Title: Implementation of Parametric and Nonparametric Models for Genomic-Assisted Selection in Plant Breeding
Author(s): Reka Howard*+ and Alicia Carriquiry and William Beavis
Companies: Iowa State University and Iowa State University and Iowa State University
Keywords: parametric, nonparametric, genomic selection, prediction
Abstract:

Genomic selection (GS) has become an important tool in breeding. Accordingly, statistical approaches for genomic assisted prediction have also evolved. Meuwissen et al. (2001) proposed parametric methods for GS. Nonparametric methods for GS, which require fewer assumptions than parametric methods, can handle the multiplicity of potential interactions across the genome. In this presentation, we implement some of the parametric and nonparametric methods for GS using simulated data containing phenotypic and marker information on F2 and BC populations. We consider marker data with and without interactions, and we compare the performance of the methods for predicting the genetic value for individuals in the plant breeding populations. The performance of the different methods is illustrated by comparing the accuracy of prediction. We show the advantage of using nonparametric models when interaction presents.


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