Novel bayesian networks for genomic prediction of developmental traits in biomass sorghum

Jhonathan P.R. Dos Santos, Samuel B. Fernandes, Scott McCoy, Roberto Lozano, Patrick J. Brown, Andrew D.B. Leakey, Edward S. Buckler, Antonio A.F. Garcia, Michael A. Gore

Research output: Contribution to journalArticle

Abstract

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining crossvalidation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of highthroughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

Original languageEnglish (US)
Pages (from-to)769-781
Number of pages13
JournalG3: Genes, Genomes, Genetics
Volume10
Issue number2
DOIs
StatePublished - Feb 1 2020

Keywords

  • Bayesian
  • Biomass sorghum
  • GenPred
  • Genomic
  • Genomic
  • Indirect selection
  • Networks
  • Prediction
  • Prediction
  • Probabilistic
  • Programming
  • Resources
  • Shared Data

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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  • Cite this

    Dos Santos, J. P. R., Fernandes, S. B., McCoy, S., Lozano, R., Brown, P. J., Leakey, A. D. B., Buckler, E. S., Garcia, A. A. F., & Gore, M. A. (2020). Novel bayesian networks for genomic prediction of developmental traits in biomass sorghum. G3: Genes, Genomes, Genetics, 10(2), 769-781. https://doi.org/10.1534/g3.119.400759