"11648","7","archive","3170",,,"disk0/00/01/16/48","2020-11-13 15:10:37","2020-11-13 15:10:37","2020-11-13 15:10:37","article",,,"show",,,,"","","","","","","","","","",,,,"Kar","S","","","","","",,"Kholová","J","","","","",,,,,"","",,,,,"","","Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India.","","Automated discretization of ‘transpiration restriction to increasing VPD’ features from outdoors high-throughput phenotyping data","pub","S2","CRPS4","","public",,,"High throughput phenotyping, Transpiration rate, Vapor pressure deficit, Time series, Machine learning,
Feature selection, Unsupervised random-forest, Gini index, Neural network, Sensitivity analysis",,"The senior author was partially supported by the grant from the Bill and
Melinda Gates Foundation to develop this framework (“Sorghum Genomics
Toolbox” project). The write-up of the paper took place in the scope of the
Make Our Planet Great Again (MOPGA) ICARUS project (Improve Crops in Arid
Regions and future climates) funded by the Agence Nationale de la Recherche
(ANR, grant ANR-17-MPGA-0011). The authors would like to thank ICRISAT,
Patancheru, India, with special thanks to Ms. Rekha Baddam, for providing
chickpea reference dataset from the LeasyScan, HTP platform, Dr. James D.
Burridge, Institute de Recherche pour le Dèveloppment (IRD) for his constructive
suggestions in editing, and the DST-JST joint lab research initiative, DSFS
for extending support in carrying out this work.","Abstract
Background: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for
drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable
for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum
transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD
breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min
frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract
informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints,
and compare genotypes.

Results: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load
cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected
using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE
and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest
genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a
multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest
water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa),
depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared
to have the most striking influence on the transpiration response independently of other environment variable,
whereas light, temperature, and relative humidity alone had little/no effect.

Conclusion: Through this study, we present a novel approach to identifying genotypes with drought-tolerance
potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy
phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the
water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications,
applied to other traits, and help expedite maximized information extraction from HTP data.","2020-10","published",,"Plant Methods (TSI)","16","1","BMC",,"1-20",,,,,,"doi:10.1186/s13007-020-00680-8",,,,,"TRUE",,"1746-4811",,,,,,"","https://doi.org/10.1186/s13007-020-00680-8","https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=10.1186%2Fs13007-020-00680-8&btnG=","pub",,"","",,,,,,"",,,,,,,"",,,,,"",,,,,"","",,,,,"","",,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Tanaka","R","","",,,,,,,,,,,,,,,,,,,,,,,"Laboratory of Biometrics and Bioinformatics, University of Tokyo, Tokyo, Japan.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Korbu","L B","","",,,,,,,,,,,,,,,,,,,,,,,"Debre Zeit Research Center, Ethiopian Institute of Agricultural Research (EIAR), Debre Zeit, Ethiopia.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Kholová","J","","",,,,"Female",,,,,,,,,,,,,,,,,,,"ICRISAT (Patancheru)",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Iwata","H","","",,,,,,,,,,,,,,,,,,,,,,,"Institut de Recherche Pour Le Développement (IRD), Université de Montpellier—UMR DIADE,Avenue Agropolis, Montpellier, France.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Durbha","S S","","",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Adinarayana","J","","",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
"11648",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Vadez","V","","",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,