Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

Gore, M. A.; dos Santos, J. P. R.; Fernandes, S. B.; Lozano, R.; Brown, P. J.; Buckler, E. S.; Garcia, A. A. F.

doi:10.1101/677179

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bioRxiv · 10.1101/677179

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

Gore, M. A.·dos Santos, J. P. R.·Fernandes, S. B.·Lozano, R.·Brown, P. J.·Buckler, E. S.·Garcia, A. A. F.

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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 5-fold cross-validation, prediction accuracies ranged from 0.48 (PBN) to 0.51 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.74 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation 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 high-throughput 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.

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Gore, M. A., dos Santos, J. P. R., Fernandes, S. B., Lozano, R., Brown, P. J., Buckler, E. S., Garcia, A. A. F.. 2019-06-21. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. https://doi.org/10.1101/677179

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