Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

Authors

Toshimi Baba, Virginia Polytechnic Institute and State University
Mehdi Momen, Virginia Polytechnic Institute and State University
Malachy T. Campbell, Virginia Polytechnic Institute and State UniversityFollow
Harkamal Walia, University of Nebraska-LincolnFollow
Gota Morota, Virginia Polytechnic Institute and State UniversityFollow

ORCID IDs

Malachy T. Campbell

Gota Morota

Document Type

Article

Date of this Version

2020

Citation

PLoS ONE 15(2): e0228118.

Comments

© 2020 Baba et al.

Open access

https://doi.org/10.1371/journal.pone.0228118

Abstract

Random regression models (RRM) are used extensively for genomic inference and predic- tion of time-valued traits in animal breeding, but only recently have been used in plant sys- tems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. How- ever, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenar- ios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Weak to strong genomic correla- tions between WU and PSA were observed across the days of imaging (0.29-0.870.38- 0.80). In both CV scenarios, MT-RRMs showed better predictive abilities compared to sin- gle-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.