Multi-trait regressor stacking increased genomic prediction accuracy of sorghum grain composition
2020
Cereal grains, primarily composed of starch, protein, and fat, are major source of staple
for human and animal nutrition. Sorghum, a cereal crop, serves as a dietary staple for
over half a billion people in the semi-arid tropics of Africa and South Asia. Genomic
prediction has enabled plant breeders to estimate breeding values of unobserved
genotypes and environments. Therefore, the use of genomic prediction will be extremely
valuable for compositional traits for which phenotyping is labor-intensive and
destructive for most accurate results. We studied the potential of Bayesian multi-output
regressor stacking (BMORS) model in improving prediction performance over single
trait single environment (STSE) models using a grain sorghum diversity panel (GSDP)
and a biparental recombinant inbred lines (RILs) population. A total of five highly
correlated grain composition traits: amylose, fat, gross energy, protein and starch, with
genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs
were studied. Average prediction accuracies from the STSE model were within a range
of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP.
Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in
the GSDP and RILs, respectively. Predicting whole environments by training with
remaining environments in BMORS yielded higher average prediction accuracy than
from STSE model. Our results show regression stacking methods such as BMORS have
potential to accurately predict unobserved individuals and environments, and
implementation of such models can accelerate genetic gain.
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