Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Sapkota, Sirjan; Boatwright, J. Lucas; Jordan, Kathleen; Boyles, Richard; Kresovich, Stephen

doi:10.3390/agronomy10091221

Title: Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Journal Article · Wed Aug 19 00:00:00 EDT 2020 · Agronomy (Online)

DOI:https://doi.org/10.3390/agronomy10091221· OSTI ID:1650690

;

; Jordan, Kathleen;

; Kresovich, Stephen

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. 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. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. 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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Research Organization:: Clemson Univ., SC (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE); USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0001134

OSTI ID:: 1650690

Alternate ID(s):: OSTI ID: 1799142

Journal Information:: Agronomy (Online), Journal Name: Agronomy (Online) Vol. 10 Journal Issue: 9; ISSN 2073-4395

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Antigua and Barbuda

Language:: English

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