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

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

doi:10.1534/g3.119.400759

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

Journal Article · Wed Dec 18 00:00:00 EST 2019 · G3

DOI:https://doi.org/10.1534/g3.119.400759· OSTI ID:1597030

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Cornell Univ., Ithaca, NY (United States); University of São Paulo (Brazil)
Univ. of Illinois at Urbana-Champaign, IL (United States)
Cornell Univ., Ithaca, NY (United States)
Univ. of California, Davis, CA (United States)
Cornell Univ., Ithaca, NY (United States); United States Department of Agriculture, Agricultural Research Service, R. W. Holley Center, Ithaca, NY (United States)
University of São Paulo (Brazil)

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 0.46 (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.

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Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E); FAPESP

Grant/Contract Number:: AR0000598; AR0000661; 2017/03625-2; 2017/25674-5

OSTI ID:: 1597030

Journal Information:: G3, Vol. 10, Issue 2; ISSN 2160-1836

Publisher:: Genetics Society of AmericaCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 16 works

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING
bayesian networks
biomass sorghum
genomic prediction
indirect selection
probabilistic programming
genpred
shared data resources

Supplemental Material for dos Santos et al., 2019 Santos, Jhonathan Pedroso Rigal Dos; Fernandes, Samuel B.; Lozano, Roberto https://doi.org/10.25387/g3.9891119 The current record is supplemented by this dataset	dataset	January 2019
Supplemental Material for dos Santos et al., 2019 Santos, Jhonathan Pedroso Rigal Dos; Fernandes, Samuel B.; Lozano, Roberto https://doi.org/10.25387/g3.9891119.v1 The current record is supplemented by this dataset	dataset	January 2019

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

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