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

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 0.46 (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 targetmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [2]; ORCiD logo [3]; ORCiD logo [4];  [2]; ORCiD logo [5]; ORCiD logo [6]; ORCiD logo [3]
  1. Cornell Univ., Ithaca, NY (United States); University of São Paulo (Brazil)
  2. Univ. of Illinois at Urbana-Champaign, IL (United States)
  3. Cornell Univ., Ithaca, NY (United States)
  4. Univ. of California, Davis, CA (United States)
  5. Cornell Univ., Ithaca, NY (United States); United States Department of Agriculture, Agricultural Research Service, R. W. Holley Center, Ithaca, NY (United States)
  6. University of São Paulo (Brazil)
Publication Date:
Research Org.:
Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); FAPESP
OSTI Identifier:
1597030
Grant/Contract Number:  
AR0000598; AR0000661; 2017/03625-2; 2017/25674-5
Resource Type:
Accepted Manuscript
Journal Name:
G3
Additional Journal Information:
Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 2160-1836
Publisher:
Genetics Society of America
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; bayesian networks; biomass sorghum; genomic prediction; indirect selection; probabilistic programming; genpred; shared data resources

Citation Formats

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., and Gore, Michael A. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. United States: N. p., 2019. Web. doi:10.1534/g3.119.400759.
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. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. United States. doi:https://doi.org/10.1534/g3.119.400759
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., and Gore, Michael A. Wed . "Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum". United States. doi:https://doi.org/10.1534/g3.119.400759. https://www.osti.gov/servlets/purl/1597030.
@article{osti_1597030,
title = {Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum},
author = {dos Santos, Jhonathan P. R. and Fernandes, Samuel B. and McCoy, Scott and Lozano, Roberto and Brown, Patrick J. and Leakey, Andrew D. B. and Buckler, Edward S. and Garcia, Antonio A. F. and Gore, Michael A.},
abstractNote = {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.},
doi = {10.1534/g3.119.400759},
journal = {G3},
number = 2,
volume = 10,
place = {United States},
year = {2019},
month = {12}
}

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