Genomic Prediction Enhanced Sparse Testing for Multi-Environment Trials

• Activity Number: 209 - Statistical methods for genomic and epigenetic data analysis
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Reka Howard* and Diego Jarquin and Jose Crossa
• Companies: University of Nebraska - Lincoln and University of Nebraska - Lincoln and CIMMYT
• Keywords: genomic prediction accuracy; sparse testing methods; random cross-validations; genotype-by-environment interaction

Abstract

Sparse testing in plant breeding refers to a situation where not all genotypes of interest are grown in each environment. Using genomic prediction and genotype × environment interaction, the non-observed genotype- in-environment combinations can be predicted. Hence, the overall costs can be reduced, and the testing capacities increased. The accuracy of predicting the unobserved data depends on different factors: (1) how many genotypes overlap between environments, (2) in how many environments each genotype is grown, and (3) which prediction method is used. Here, we studied the predictive ability obtained when using a fixed number of plots and different sparse testing designs. The empirical study is built upon two different maize hybrid data sets and each data set was analyzed separately. For each set, phenotypic records on yield from three different environments are available. Three different prediction models were implemented, two main effects models, and a model including the genotype-by-environment interaction (GE) term. The GE model provided higher prediction accuracy than the other two models for the different allocation scenarios.