AUTHOR=Freitas Moreira Fabiana , Rojas de Oliveira Hinayah , Lopez Miguel Angel , Abughali Bilal Jamal , Gomes Guilherme , Cherkauer Keith Aric , Brito Luiz Fernando , Rainey Katy Martin 

TITLE=High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.715983

DOI=10.3389/fpls.2021.715983

ISSN=1664-462X

ABSTRACT=Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n=383) was grown
in multi-environment trials and destructive AGB measurements were collected along with
multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine learning
methods for phenotypic prediction of AGB and genomic prediction and genome-wide association
studies (GWAS) based on random regression models (RRM). AGB phenotypic predictions were high
(R2=0.92-0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from
0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations
compared to those DAP further apart. We observed high accuracies and low biases of prediction
indicating that genomic breeding values for AGB can be predicted over specific time intervals.
Genomic regions associated with AGB varied with time, and no genetic markers were significant in
all time points. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of
soybean AGB and can provide useful information for crop improvement. This study offers a basis for
future studies to combine phenotyping and genomic analysis to understand the genetic architecture of complex longitudinal traits.