Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.

ABSTRACT: In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the 'wrong' genotypes retained, and how often are the 'correct' genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied t

SUBMITTER: Xu Z 

PROVIDER: S-EPMC8270216 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature