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van Eeuwijk FA, Bustos-Korts D, Millet EJ, Boer MP, Kruijer W, Thompson A, Malosetti M, Iwata H, Quiroz R, Kuppe C, Muller O, Blazakis KN, Yu K, Tardieu F, Chapman SC. Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:23-39. [PMID: 31003609 DOI: 10.1016/j.plantsci.2018.06.018] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 06/05/2018] [Accepted: 06/19/2018] [Indexed: 05/18/2023]
Abstract
New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.
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Affiliation(s)
- Fred A van Eeuwijk
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands.
| | - Daniela Bustos-Korts
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Emilie J Millet
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Martin P Boer
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Willem Kruijer
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Addie Thompson
- Institute for Plant Sciences, Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Marcos Malosetti
- Biometris, Wageningen University & Research Centre, P.O. Box 16, 6700 AC Wageningen, The Netherlands
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Roberto Quiroz
- International Potato Center (CIP), P.O. Box 1558, Lima 12, Peru
| | - Christian Kuppe
- Institute for Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Onno Muller
- Institute for Bio-and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Konstantinos N Blazakis
- Department of Horticultural Genetics and Biotechnology, Mediterranean Agronomic Institute of Chania (MAICh), Alsylio Agrokipiou, P.O. Box 85, 73100 Chania-Crete, Greece
| | - Kang Yu
- Crop Science, Institute of Agricultural Sciences, ETH Zurich, Switzerland; Remote Sensing & Terrestrial Ecology, Department of Earth and Environmental Sciences, KU Leuven, Belgium
| | - Francois Tardieu
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, UMR759, INRA, 34060 Montpellier, France
| | - Scott C Chapman
- CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, QLD 4067, Australia; School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD 4343, Australia
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Schulthess AW, Reif JC, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Ganal MW, Röder MS, Jiang Y. The roles of pleiotropy and close linkage as revealed by association mapping of yield and correlated traits of wheat (Triticum aestivum L.). JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:4089-4101. [PMID: 28922760 PMCID: PMC5853857 DOI: 10.1093/jxb/erx214] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 06/01/2017] [Indexed: 05/22/2023]
Abstract
Grain yield (GY) of bread wheat (Triticum aestivum L.) is quantitatively inherited. Correlated GY-syndrome traits such as plant height (PH), heading date (HD), thousand grain weight (TGW), test weight (TW), grains per ear (GPE), and ear weight (EW) influence GY. Most quantitative genetics studies assessed the multiple-trait (MT) complex of GY-syndrome using single-trait approaches, and little is known about its underlying pleiotropic architecture. We investigated the pleiotropic architecture of wheat GY-syndrome through MT association mapping (MT-GWAS) using 372 varieties phenotyped in up to eight environments and genotyped with 18 832 single nucleotide polymorphisms plus 24 polymorphic functional markers. MT-GWAS revealed a total of 345 significant markers spread genome wide, representing 8, 40, 11, 40, 34, and 35 effective GY-PH, GY-HD, GY-TGW, GY-TW, GY-GPE, and GY-EW associations, respectively. Among them, pleiotropic roles of Rht-B1 and TaGW2-6B loci were corroborated. Only one marker presented simultaneous associations for three traits (i.e. GY-TGW-TW). Close linkage was difficult to differentiate from pleiotropy; thus, the pleiotropic architecture of GY-syndrome was dissected more as a cause of pleiotropy rather than close linkage. Simulations showed that minor allele frequencies, along with sizes and distances between quantitative trait loci for two traits, influenced the ability to distinguish close linkage from pleiotropy.
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Affiliation(s)
- Albert W Schulthess
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Jie Ling
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | | | | | | | | | | | | | | | - Marion S Röder
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
- Correspondence:
| | - Yong Jiang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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Cheng R, Doerge RW, Borevitz J. Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping. G3 (BETHESDA, MD.) 2017; 7:813-822. [PMID: 28064191 PMCID: PMC5345711 DOI: 10.1534/g3.116.037531] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 12/29/2016] [Indexed: 01/13/2023]
Abstract
Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.
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Affiliation(s)
- Riyan Cheng
- Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia
| | - R W Doerge
- Department of Statistics, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Justin Borevitz
- Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia
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Rungrat T, Awlia M, Brown T, Cheng R, Sirault X, Fajkus J, Trtilek M, Furbank B, Badger M, Tester M, Pogson BJ, Borevitz JO, Wilson P. Using Phenomic Analysis of Photosynthetic Function for Abiotic Stress Response Gene Discovery. THE ARABIDOPSIS BOOK 2016; 14:e0185. [PMID: 27695390 PMCID: PMC5042155 DOI: 10.1199/tab.0185] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Monitoring the photosynthetic performance of plants is a major key to understanding how plants adapt to their growth conditions. Stress tolerance traits have a high genetic complexity as plants are constantly, and unavoidably, exposed to numerous stress factors, which limits their growth rates in the natural environment. Arabidopsis thaliana, with its broad genetic diversity and wide climatic range, has been shown to successfully adapt to stressful conditions to ensure the completion of its life cycle. As a result, A. thaliana has become a robust and renowned plant model system for studying natural variation and conducting gene discovery studies. Genome wide association studies (GWAS) in restructured populations combining natural and recombinant lines is a particularly effective way to identify the genetic basis of complex traits. As most abiotic stresses affect photosynthetic activity, chlorophyll fluorescence measurements are a potential phenotyping technique for monitoring plant performance under stress conditions. This review focuses on the use of chlorophyll fluorescence as a tool to study genetic variation underlying the stress tolerance responses to abiotic stress in A. thaliana.
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Affiliation(s)
- Tepsuda Rungrat
- ARC Centre of Excellence for Plant Energy Biology, Australian National University, Australia
| | - Mariam Awlia
- ARC Centre of Excellence for Translational Photosynthesis, Australian National University, Australia
| | - Tim Brown
- ARC Centre of Excellence for Plant Energy Biology, Australian National University, Australia
| | - Riyan Cheng
- ARC Centre of Excellence for Plant Energy Biology, Australian National University, Australia
| | - Xavier Sirault
- High Resolution Plant Phenomics Centre, CSIRO Agriculture, Australia
| | - Jiri Fajkus
- Photon Systems Instruments (PSI), Czech Republic
| | | | - Bob Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Australian National University, Australia
- High Resolution Plant Phenomics Centre, CSIRO Agriculture, Australia
| | - Murray Badger
- ARC Centre of Excellence for Translational Photosynthesis, Australian National University, Australia
| | - Mark Tester
- King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Barry J Pogson
- ARC Centre of Excellence for Plant Energy Biology, Australian National University, Australia
| | - Justin O Borevitz
- ARC Centre of Excellence for Plant Energy Biology, Australian National University, Australia
| | - Pip Wilson
- ARC Centre of Excellence for Plant Energy Biology, Australian National University, Australia
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Brown TB, Cheng R, Sirault XRR, Rungrat T, Murray KD, Trtilek M, Furbank RT, Badger M, Pogson BJ, Borevitz JO. TraitCapture: genomic and environment modelling of plant phenomic data. CURRENT OPINION IN PLANT BIOLOGY 2014; 18:73-9. [PMID: 24646691 DOI: 10.1016/j.pbi.2014.02.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Revised: 02/04/2014] [Accepted: 02/09/2014] [Indexed: 05/18/2023]
Abstract
Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience traits from large collections of germplasm if software can integrate among the technologies. Traits could be Captured throughout development and across environments from multi-dimensional phenotypes, by applying Genome Wide Association Studies (GWAS) to identify causal genes and background variation and functional structural plant models (FSPMs) to predict plant growth and reproduction in target environments. TraitCapture should be applicable to both controlled and field environments and would allow breeders to simulate regional variety trials to pre-select for increased productivity under challenging environments.
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Affiliation(s)
- Tim B Brown
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia
| | - Riyan Cheng
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia
| | - Xavier R R Sirault
- High Resolution Plant Phenomics Centre, Plant Industry, CSIRO, Australia
| | - Tepsuda Rungrat
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia
| | - Kevin D Murray
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia
| | - Martin Trtilek
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia; High Resolution Plant Phenomics Centre, Plant Industry, CSIRO, Australia; Photon Systems Instruments, Czech Republic; ARC Centre of Excellence in Plant Energy Biology, Australia
| | - Robert T Furbank
- High Resolution Plant Phenomics Centre, Plant Industry, CSIRO, Australia
| | - Murray Badger
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia; ARC Centre of Excellence in Plant Energy Biology, Australia
| | - Barry J Pogson
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia; ARC Centre of Excellence in Plant Energy Biology, Australia
| | - Justin O Borevitz
- Division of Plant Sciences, Research School of Biology, Australian National University, Australia.
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