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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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dos Santos, Jhonathan P. R. [1, 2] ; Fernandes, Samuel B. [3] ; McCoy, Scott [4] ; Lozano, Roberto [2] ; Brown, Patrick J. [5] ; Leakey, Andrew D. B. [3, 4, 6] ; Buckler, Edward S. [2, 7, 8] ; Garcia, Antonio A. F. [1] ; Gore, Michael A. [2]
Total Authors: 9
[1] Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Genet, Piracicaba, SP - Brazil
[2] Cornell Univ, Sch Integrat Plant Sci, Plant Breeding & Genet Sect, 358 Plant Sci Bldg, Ithaca, NY 14853 - USA
[3] Univ Illinois, Dept Crop Sci, Urbana, IL 61801 - USA
[4] Univ Illinois, Inst Genom Biol, Urbana, IL 61801 - USA
[5] Univ Calif Davis, Dept Plant Sci, Sect Agr Plant Biol, Davis, CA 95616 - USA
[6] Univ Illinois, Dept Plant Biol, Urbana, IL 61801 - USA
[7] Cornell Univ, Inst Genom Divers, Ithaca, NY 14853 - USA
[8] ARS, USDA, RW Holley Ctr, Ithaca, NY 14853 - USA
Total Affiliations: 8
Document type: Journal article
Source: G3-GENES, GENOMES, GENETICS; v. 10, n. 2, p. 769-781, FEB 2020.
Web of Science Citations: 4

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 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. (AU)

FAPESP's process: 17/03625-2 - Development of temporal genomic selection models via Bayesian Networks applied to sorghum bicolor
Grantee:Jhonathan Pedroso Rigal dos Santos
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 17/25674-5 - Development of temporal Genomic Selection models via Dynamic Bayesian Networks, with application to Sorghum bicolor
Grantee:Jhonathan Pedroso Rigal dos Santos
Support type: Scholarships abroad - Research Internship - Doctorate