In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 7 ( 2021-7-9), p. e0240948-
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 to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0240948
DOI:
10.1371/journal.pone.0240948.g001
DOI:
10.1371/journal.pone.0240948.g002
DOI:
10.1371/journal.pone.0240948.g003
DOI:
10.1371/journal.pone.0240948.g004
DOI:
10.1371/journal.pone.0240948.g005
DOI:
10.1371/journal.pone.0240948.g006
DOI:
10.1371/journal.pone.0240948.t001
DOI:
10.1371/journal.pone.0240948.t002
DOI:
10.1371/journal.pone.0240948.s001
DOI:
10.1371/journal.pone.0240948.s002
DOI:
10.1371/journal.pone.0240948.s003
DOI:
10.1371/journal.pone.0240948.s004
DOI:
10.1371/journal.pone.0240948.s005
DOI:
10.1371/journal.pone.0240948.s006
DOI:
10.1371/journal.pone.0240948.s007
DOI:
10.1371/journal.pone.0240948.s008
DOI:
10.1371/journal.pone.0240948.s009
DOI:
10.1371/journal.pone.0240948.s010
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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