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Henry AR, Miller ND, Spalding EP. QTL for the Kinematic Traits That Define the Arabidopsis Root Elongation Zone and Their Relationship to Gravitropism. PLANTS (BASEL, SWITZERLAND) 2024; 13:1189. [PMID: 38732404 PMCID: PMC11085590 DOI: 10.3390/plants13091189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024]
Abstract
Cell expansion in a discrete region called the elongation zone drives root elongation. Analyzing time lapse images can quantify the expansion in kinematic terms as if it were fluid flow. We used horizontal microscopes to collect images from which custom software extracted the length of the elongation zone, the peak relative elemental growth rate (REGR) within it, the axial position of the REGR peak, and the root elongation rate. Automation enabled these kinematic traits to be measured in 1575 Arabidopsis seedlings representing 162 recombinant inbred lines (RILs) derived from a cross of Cvi and Ler ecotypes. We mapped ten quantitative trait loci (QTL), affecting the four kinematic traits. Three QTL affected two or more traits in these vertically oriented seedlings. We compared this genetic architecture with that previously determined for gravitropism using the same RIL population. The major QTL peaks for the kinematic traits did not overlap with the gravitropism QTL. Furthermore, no single kinematic trait correlated with quantitative descriptors of the gravitropism response curve across this population. In addition to mapping QTL for growth zone traits, this study showed that the size and shape of the elongation zone may vary widely without affecting the differential growth induced by gravity.
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Affiliation(s)
| | | | - Edgar P. Spalding
- Department of Botany, University of Wisconsin, Madison, WI 53706, USA; (A.R.H.); (N.D.M.)
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2
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Swartz LG, Liu S, Dahlquist D, Kramer ST, Walter ES, McInturf SA, Bucksch A, Mendoza-Cózatl DG. OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1600-1616. [PMID: 37733751 DOI: 10.1111/tpj.16449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 08/16/2023] [Indexed: 09/23/2023]
Abstract
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
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Affiliation(s)
- Landon G Swartz
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Suxing Liu
- School of Plant Sciences, University of Arizona, 1140 E South Campus, Tucson, Arizona, 85721, USA
| | - Drew Dahlquist
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
| | - Skyler T Kramer
- MU Institute of Data Science and Informatics, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollinst St., Columbia, Missouri, 65211, USA
| | - Emily S Walter
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Samuel A McInturf
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Alexander Bucksch
- School of Plant Sciences, University of Arizona, 1140 E South Campus, Tucson, Arizona, 85721, USA
| | - David G Mendoza-Cózatl
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
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Meyer RC, Weigelt-Fischer K, Tschiersch H, Topali G, Altschmied L, Heuermann MC, Knoch D, Kuhlmann M, Zhao Y, Altmann T. Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5341-5362. [PMID: 37306093 DOI: 10.1093/jxb/erad222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/10/2023] [Indexed: 06/13/2023]
Abstract
Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Henning Tschiersch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Georgia Topali
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Lothar Altschmied
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Markus Kuhlmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
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4
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Leveraging orthology within maize and Arabidopsis QTL to identify genes affecting natural variation in gravitropism. Proc Natl Acad Sci U S A 2022; 119:e2212199119. [PMID: 36161933 PMCID: PMC9546580 DOI: 10.1073/pnas.2212199119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Plants typically orient their organs with respect to the Earth's gravity field by a dynamic process called gravitropism. To discover conserved genetic elements affecting seedling root gravitropism, we measured the process in a set of Zea mays (maize) recombinant inbred lines with machine vision and compared the results with those obtained in a similar study of Arabidopsis thaliana. Each of the several quantitative trait loci that we mapped in both species spanned many hundreds of genes, too many to test individually for causality. We reasoned that orthologous genes may be responsible for natural variation in monocot and dicot root gravitropism. If so, pairs of orthologous genes affecting gravitropism may be present within the maize and Arabidopsis QTL intervals. A reciprocal comparison of sequences within the QTL intervals identified seven pairs of such one-to-one orthologs. Analysis of knockout mutants demonstrated a role in gravitropism for four of the seven: CCT2 functions in phosphatidylcholine biosynthesis, ATG5 functions in membrane remodeling during autophagy, UGP2 produces the substrate for cellulose and callose polymer extension, and FAMA is a transcription factor. Automated phenotyping enabled this discovery of four naturally varying components of a conserved process (gravitropism) by making it feasible to conduct the same large-scale experiment in two species.
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5
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Wang P, Meng F, Donaldson P, Horan S, Panchy NL, Vischulis E, Winship E, Conner JK, Krysan PJ, Shiu S, Lehti‐Shiu MD. High-throughput measurement of plant fitness traits with an object detection method using Faster R-CNN. THE NEW PHYTOLOGIST 2022; 234:1521-1533. [PMID: 35218008 PMCID: PMC9310946 DOI: 10.1111/nph.18056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r2 = 0.9996) and highly accurate fruit counts (r2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.
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Affiliation(s)
- Peipei Wang
- Department of Plant BiologyMichigan State UniversityEast LansingMI48824USA
- DOE Great Lake Bioenergy Research CenterMichigan State UniversityEast LansingMI48824USA
| | - Fanrui Meng
- Department of Plant BiologyMichigan State UniversityEast LansingMI48824USA
- DOE Great Lake Bioenergy Research CenterMichigan State UniversityEast LansingMI48824USA
| | - Paityn Donaldson
- Department of Plant BiologyMichigan State UniversityEast LansingMI48824USA
| | - Sarah Horan
- Department of Plant BiologyMichigan State UniversityEast LansingMI48824USA
| | - Nicholas L. Panchy
- National Institute for Mathematical and Biological SynthesisUniversity of Tennessee1122 Volunteer Blvd, Suite 106KnoxvilleTN37996‐3410USA
| | - Elyse Vischulis
- Genetics and Genome Sciences Graduate ProgramMichigan State UniversityEast LansingMI48824USA
| | - Eamon Winship
- Department of Biochemistry and Molecular BiologyMichigan State UniversityEast LansingMI48824USA
| | - Jeffrey K. Conner
- Department of Plant BiologyMichigan State UniversityEast LansingMI48824USA
- W.K. Kellogg Biological StationMichigan State University3700 E. Gull Lake DriveHickory CornersMI49060USA
- Ecology, Evolution, and Behavior Graduate ProgramMichigan State UniversityEast LansingMI48824USA
| | - Patrick J. Krysan
- Department of HorticultureUniversity of Wisconsin‐MadisonMadisonWI53705USA
| | - Shin‐Han Shiu
- Department of Plant BiologyMichigan State UniversityEast LansingMI48824USA
- DOE Great Lake Bioenergy Research CenterMichigan State UniversityEast LansingMI48824USA
- Genetics and Genome Sciences Graduate ProgramMichigan State UniversityEast LansingMI48824USA
- Ecology, Evolution, and Behavior Graduate ProgramMichigan State UniversityEast LansingMI48824USA
- Department of Computational Mathematics, Science, and EngineeringMichigan State UniversityEast LansingMI48824USA
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6
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Mu Q, Guo T, Li X, Yu J. Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range. THE NEW PHYTOLOGIST 2022; 233:1768-1779. [PMID: 34870847 DOI: 10.1111/nph.17904] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
Phenotypic plasticity is observed widely in plants and often studied with reaction norms for adult plant or end-of-season traits. Uncovering genetic, environmental and developmental patterns behind the observed phenotypic variation under natural field conditions is needed. Using a sorghum (Sorghum bicolor) genetic population evaluated for plant height in seven natural field conditions, we investigated the major pattern that differentiated these environments. We then examined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments and conducting crop growth modelling. We found that diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index. Three genetic loci (Dw1, Dw3 and qHT7.1) were consistently detected for individual environments, reaction-norm parameters across environments and growth-curve parameters through the season. Their genetic effects changed dynamically along the environmental gradient and the developmental stage. A conceptual model with three-dimensional reaction norms was proposed to showcase the interconnecting components: genotype, environment and development. Beyond genomic and environmental analyses, further integration of development and physiology at the whole-plant and molecular levels into complex trait dissection would enhance our understanding of mechanisms underlying phenotypic variation.
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Affiliation(s)
- Qi Mu
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
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7
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Morota G, Jarquin D, Campbell MT, Iwata H. Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods Mol Biol 2022; 2539:269-296. [PMID: 35895210 DOI: 10.1007/978-1-0716-2537-8_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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8
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Deslauriers SD. High-resolution imaging as a tool for identifying quantitative trait loci that regulate photomorphogenesis in Arabidopsis thaliana. AOB PLANTS 2021; 13:plab063. [PMID: 34729159 PMCID: PMC8557632 DOI: 10.1093/aobpla/plab063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 09/20/2021] [Indexed: 06/13/2023]
Abstract
A primary component of seedling establishment is the photomorphogenic response as seedlings emerge from the soil. This process is characterized by a reduced growth rate in the hypocotyl, increased root growth, opening of the apical hook and expansion of the cotyledons as photosynthetic organs. While fundamental to plant success, the photomorphogenic response can be highly variable. Additionally, studies of Arabidopsis thaliana are made difficult by subtle differences in growth rate between individuals. High-resolution imaging and computational processing have emerged as useful tools for quantification of such phenotypes. This study sought to: (i) develop an imaging methodology which could capture changes in growth rate as seedlings transition from darkness to blue light in real time, and (ii) apply this methodology to single-quantitative trait locus (QTL) analysis using the Cvi × Ler recombinant inbred line (RIL) mapping population. Significant differences in the photomorphogenic response were observed between the parent lines and analysis of 158 RILs revealed a wide range of growth rate phenotypes. Quantitative trait locus analysis detected significant loci associated with dark growth rate on chromosome 5 and significant loci associated with light growth rate on chromosome 2. Candidate genes associated with these loci, such as the previously characterized ER locus, highlight the application of this approach for QTL analysis. Genetic analysis of Landsberg lines without the erecta mutation also supports a role for ER in modulating the photomorphogenic response, consistent with previous QTL analyses of this population. Strengths and limitations of this methodology are presented, as well as means of improvement.
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Affiliation(s)
- Stephen D Deslauriers
- Division of Science and Math, University of Minnesota, Morris, Morris, MN 56267, USA
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9
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Arjas A, Hauptmann A, Sillanpää MJ. Estimation of dynamic SNP-heritability with Bayesian Gaussian process models. Bioinformatics 2020; 36:3795-3802. [PMID: 32186692 PMCID: PMC7672693 DOI: 10.1093/bioinformatics/btaa199] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 03/10/2020] [Accepted: 03/17/2020] [Indexed: 11/23/2022] Open
Abstract
Motivation Improved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty. Results We introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which ‘borrows strength’ from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals. Availability and implementation The C++ implementation dynBGP and simulated data are available in GitHub: https://github.com/aarjas/dynBGP. The programmes can be run in R. Real datasets are available in QTL archive: https://phenome.jax.org/centers/QTLA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arttu Arjas
- Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland
| | - Andreas Hauptmann
- Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland.,Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Mikko J Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland.,Infotech Oulu, University of Oulu, Oulu FI-90014, Finland
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10
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Bazzer SK, Purcell LC. Identification of quantitative trait loci associated with canopy temperature in soybean. Sci Rep 2020; 10:17604. [PMID: 33077811 PMCID: PMC7572360 DOI: 10.1038/s41598-020-74614-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/05/2020] [Indexed: 02/07/2023] Open
Abstract
A consistent risk for soybean (Glycine max L.) production is the impact of drought on growth and yield. Canopy temperature (CT) is an indirect measure of transpiration rate and stomatal conductance and may be valuable in distinguishing differences among genotypes in response to drought. The objective of this study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging in a population of recombinant inbred lines developed from a cross between KS4895 and Jackson. Heritability of CT was 35% when estimated across environments. QTL analysis identified 11 loci for CT distributed on eight chromosomes that individually explained between 4.6 and 12.3% of the phenotypic variation. The locus on Gm11 was identified in two individual environments and across environments and explained the highest proportion of phenotypic variation (9.3% to 11.5%) in CT. Several of these CT loci coincided with the genomic regions from previous studies associated with canopy wilting, canopy temperature, water use efficiency, and other morpho-physiological traits related with drought tolerance. Candidate genes with biological function related to transpiration, root development, and signal transduction underlie these putative CT loci. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought.
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Affiliation(s)
- Sumandeep K Bazzer
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, 72704, USA
| | - Larry C Purcell
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, 72704, USA.
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11
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Miao C, Xu Y, Liu S, Schnable PS, Schnable JC. Increased Power and Accuracy of Causal Locus Identification in Time Series Genome-wide Association in Sorghum. PLANT PHYSIOLOGY 2020; 183:1898-1909. [PMID: 32461303 PMCID: PMC7401099 DOI: 10.1104/pp.20.00277] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 05/20/2020] [Indexed: 05/18/2023]
Abstract
The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum (Sorghum bicolor) diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses.
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Affiliation(s)
- Chenyong Miao
- Quantitative Life Science Initiative, Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Yuhang Xu
- Department of Applied Statistics and Operations Research, Bowling Green State University, Bowling Green, Ohio 43403
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, Kansas 66506
| | | | - James C Schnable
- Quantitative Life Science Initiative, Center for Plant Science Innovation, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
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12
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Bartholomé J, Mabiala A, Burlett R, Bert D, Leplé JC, Plomion C, Gion JM. The pulse of the tree is under genetic control: eucalyptus as a case study. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 103:338-356. [PMID: 32142191 DOI: 10.1111/tpj.14734] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 01/16/2020] [Accepted: 02/17/2020] [Indexed: 06/10/2023]
Abstract
The pulse of the tree (diurnal cycle of stem radius fluctuations) has been widely studied as a way of analyzing tree responses to the environment, including the phenotypic plasticity of tree-water relationships in particular. However, the genetic basis of this daily phenotype and its interplay with the environment remain largely unexplored. We characterized the genetic and environmental determinants of this response, by monitoring daily stem radius fluctuation (dSRF) on 210 trees from a Eucalyptus urophylla × E. grandis full-sib family over 2 years. The dSRF signal was broken down into hydraulic capacitance, assessed as the daily amplitude of shrinkage (DA), and net growth, estimated as the change in maximum radius between two consecutive days (ΔR). The environmental determinants of these two traits were clearly different: DA was positively correlated with atmospheric variables relating to water demand, while ΔR was associated with soil water content. The heritability for these two traits ranged from low to moderate over time, revealing a time-dependent or environment-dependent complex genetic determinism. We identified 686 and 384 daily quantitative trait loci (QTL) representing 32 and 31 QTL regions for DA and ΔR, respectively. The identification of gene networks underlying the 27 major genomics regions for both traits generated additional hypotheses concerning the biological mechanisms involved in response to water demand and supply. This study highlights that environmentally induced changes in daily stem radius fluctuation are genetically controlled in trees and suggests that these daily responses integrated over time shape the genetic architecture of mature traits.
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Affiliation(s)
- Jérôme Bartholomé
- BIOGECO, INRAE, University of Bordeaux, 33610, Cestas, France
- CIRAD, UMR AGAP, F-34398, Montpellier, France
- AGAP, University of Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | | | - Régis Burlett
- BIOGECO, INRAE, University of Bordeaux, 33610, Cestas, France
| | - Didier Bert
- BIOGECO, INRAE, University of Bordeaux, 33610, Cestas, France
| | | | | | - Jean-Marc Gion
- BIOGECO, INRAE, University of Bordeaux, 33610, Cestas, France
- CIRAD, UMR AGAP, F-34398, Montpellier, France
- AGAP, University of Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
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Vanhatalo J, Li Z, Sillanpää MJ. A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data. Bioinformatics 2020; 35:3684-3692. [PMID: 30850830 PMCID: PMC6761969 DOI: 10.1093/bioinformatics/btz164] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 12/05/2018] [Accepted: 03/06/2019] [Indexed: 12/22/2022] Open
Abstract
Motivation Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection. Results We propose a Bayesian Gaussian process (GP) approach for functional QTL mapping. We use GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over time. We use an efficient gradient based algorithm to estimate the tuning parameters of GPs. Notably, the GP approach is directly applicable to the incomplete datasets having even larger than 50% missing data rate (among phenotypes). We further develop a stepwise algorithm to search through the model space in terms of genetic variants, and use a minimal increase of Bayesian posterior probability as a stopping rule to focus on only a small set of putative QTL. We also discuss the connection between GP and penalized B-splines and wavelets. On two simulated and three real datasets, our GP approach demonstrates great flexibility for modeling different types of phenotypic trajectories with low computational cost. The proposed model selection approach finds the most likely QTL reliably in tested datasets. Availability and implementation Software and simulated data are available as a MATLAB package ‘GPQTLmapping’, and they can be downloaded from GitHub (https://github.com/jpvanhat/GPQTLmapping). Real datasets used in case studies are publicly available at QTL Archive. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jarno Vanhatalo
- Department of Mathematics and Statistics and Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Zitong Li
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT 2601, Australia
| | - Mikko J Sillanpää
- Department of Mathematical Sciences, Biocenter Oulu and Infotech Oulu University of Oulu, Oulu FI-90014, Finland
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14
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1885-1898. [PMID: 32097472 PMCID: PMC7094083 DOI: 10.1093/jxb/erz545] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 02/19/2020] [Indexed: 05/08/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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15
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020. [PMID: 32097472 DOI: 10.17632/pkxpkw6j43.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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16
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Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
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17
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Jiang N, Floro E, Bray AL, Laws B, Duncan KE, Topp CN. Three-Dimensional Time-Lapse Analysis Reveals Multiscale Relationships in Maize Root Systems with Contrasting Architectures. THE PLANT CELL 2019; 31:1708-1722. [PMID: 31123089 PMCID: PMC6713302 DOI: 10.1105/tpc.19.00015] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 05/08/2019] [Accepted: 07/01/2019] [Indexed: 05/22/2023]
Abstract
Understanding how an organism's phenotypic traits are conditioned by genetic and environmental variation is a central goal of biology. Root systems are one of the most important but poorly understood aspects of plants, largely due to the three-dimensional (3D), dynamic, and multiscale phenotyping challenge they pose. A critical gap in our knowledge is how root systems build in complexity from a single primary root to a network of thousands of roots that collectively compete for ephemeral, heterogeneous soil resources. We used time-lapse 3D imaging and mathematical modeling to assess root system architectures (RSAs) of two maize (Zea mays) inbred genotypes and their hybrid as they grew in complexity from a few to many roots. Genetically driven differences in root branching zone size and lateral branching densities along a single root, combined with differences in peak growth rate and the relative allocation of carbon resources to new versus existing roots, manifest as sharply distinct global RSAs over time. The 3D imaging of mature field-grown root crowns showed that several genetic differences in seedling architectures could persist throughout development and across environments. This approach connects individual and system-wide scales of root growth dynamics, which could eventually be used to predict genetic variation for complex RSAs and their functions.
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Affiliation(s)
- Ni Jiang
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Eric Floro
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Adam L Bray
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211
| | - Benjamin Laws
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Keith E Duncan
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
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18
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Jiang N, Floro E, Bray AL, Laws B, Duncan KE, Topp CN. Three-Dimensional Time-Lapse Analysis Reveals Multiscale Relationships in Maize Root Systems with Contrasting Architectures. THE PLANT CELL 2019; 31:1708-1722. [PMID: 31123089 DOI: 10.1101/381046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 05/08/2019] [Accepted: 07/01/2019] [Indexed: 05/28/2023]
Abstract
Understanding how an organism's phenotypic traits are conditioned by genetic and environmental variation is a central goal of biology. Root systems are one of the most important but poorly understood aspects of plants, largely due to the three-dimensional (3D), dynamic, and multiscale phenotyping challenge they pose. A critical gap in our knowledge is how root systems build in complexity from a single primary root to a network of thousands of roots that collectively compete for ephemeral, heterogeneous soil resources. We used time-lapse 3D imaging and mathematical modeling to assess root system architectures (RSAs) of two maize (Zea mays) inbred genotypes and their hybrid as they grew in complexity from a few to many roots. Genetically driven differences in root branching zone size and lateral branching densities along a single root, combined with differences in peak growth rate and the relative allocation of carbon resources to new versus existing roots, manifest as sharply distinct global RSAs over time. The 3D imaging of mature field-grown root crowns showed that several genetic differences in seedling architectures could persist throughout development and across environments. This approach connects individual and system-wide scales of root growth dynamics, which could eventually be used to predict genetic variation for complex RSAs and their functions.
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Affiliation(s)
- Ni Jiang
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Eric Floro
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Adam L Bray
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211
| | - Benjamin Laws
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
| | - Keith E Duncan
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
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Campbell M, Momen M, Walia H, Morota G. Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits. THE PLANT GENOME 2019; 12:180075. [PMID: 31290928 DOI: 10.3835/plantgenome2018.10.0075] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Understanding the genetic basis of dynamic plant phenotypes has largely been limited because of a lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to nondestructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g., genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits and provide a robust framework for modeling trait trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice ( L.) from 33,674 single nucleotide polymorphisms. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent as well as time-specific transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.
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20
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Marchadier E, Hanemian M, Tisné S, Bach L, Bazakos C, Gilbault E, Haddadi P, Virlouvet L, Loudet O. The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana. PLoS Genet 2019; 15:e1007954. [PMID: 31009456 PMCID: PMC6476473 DOI: 10.1371/journal.pgen.1007954] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 01/11/2019] [Indexed: 12/16/2022] Open
Abstract
One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.
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Affiliation(s)
- Elodie Marchadier
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Mathieu Hanemian
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Sébastien Tisné
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Liên Bach
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Christos Bazakos
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Elodie Gilbault
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Parham Haddadi
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Laetitia Virlouvet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Olivier Loudet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
- * E-mail:
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Pham AT, Maurer A, Pillen K, Brien C, Dowling K, Berger B, Eglinton JK, March TJ. Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC PLANT BIOLOGY 2019; 19:134. [PMID: 30971212 PMCID: PMC6458831 DOI: 10.1186/s12870-019-1723-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 03/17/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Barley (Hordeum vulgare L.) is the fourth most important cereal crop worldwide. Barley production is compromised by many abiotic stresses including drought. Wild barley is a valuable source of alleles that can improve adaptation of cultivated barley to drought stress. RESULTS In the present study, a nested association mapping population named HEB-25, consisting of 1420 BC1S3 lines that were developed by crossing 25 different wild barley accessions to the elite barley cultivar 'Barke', was evaluated under both control and drought-stressed conditions in the Australian Plant Phenomics Facility, University of Adelaide. Overall, 14 traits reflecting the performance of individual plants in each treatment were calculated from non-destructive imaging over time and destructive end-of-experiment measurements. For each trait, best linear unbiased estimators (BLUEs) were calculated and used for genome-wide association study (GWAS) analysis. Among the quantitative trait loci (QTL) identified for the 14 traits, many co-localise with known inflorescence and developmental genes. We identified a QTL on chromosome 4H where, under drought and control conditions, wild barley alleles increased biomass by 10 and 17% respectively compared to the Barke allele. CONCLUSIONS Across all traits, QTL which increased phenotypic values were identified, providing a wider range of genetic diversity for the improvement of drought tolerance in barley.
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Affiliation(s)
- Anh-Tung Pham
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Andreas Maurer
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Klaus Pillen
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany
| | - Chris Brien
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
- Phenomics and Bioinformatics Research Centre, University of South Australia, North Terrace, Adelaide, SA 5000 Australia
- Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Kate Dowling
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
- Phenomics and Bioinformatics Research Centre, University of South Australia, North Terrace, Adelaide, SA 5000 Australia
- Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Bettina Berger
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
- Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
| | - Jason K. Eglinton
- Sugar Research Australia, 71378 Bruce Highway, Gordonvale, QLD 4865 Australia
| | - Timothy J. March
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064 Australia
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Du Q, Yang X, Xie J, Quan M, Xiao L, Lu W, Tian J, Gong C, Chen J, Li B, Zhang D. Time-specific and pleiotropic quantitative trait loci coordinately modulate stem growth in Populus. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:608-624. [PMID: 30133117 PMCID: PMC6381792 DOI: 10.1111/pbi.13002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 08/05/2018] [Accepted: 08/18/2018] [Indexed: 05/15/2023]
Abstract
In perennial woody plants, the coordinated increase of stem height and diameter during juvenile growth improves competitiveness (i.e. access to light); however, the factors underlying variation in stem growth remain unknown in trees. Here, we used linkage-linkage disequilibrium (linkage-LD) mapping to decipher the genetic architecture underlying three growth traits during juvenile stem growth. We used two Populus populations: a linkage mapping population comprising a full-sib family of 1,200 progeny and an association mapping panel comprising 435 unrelated individuals from nearly the entire natural range of Populus tomentosa. We mapped 311 quantitative trait loci (QTL) for three growth traits at 12 timepoints to 42 regions in 17 linkage groups. Of these, 28 regions encompassing 233 QTL were annotated as 27 segmental homology regions (SHRs). Using SNPs identified by whole-genome re-sequencing of the 435-member association mapping panel, we identified significant SNPs (P ≤ 9.4 × 10-7 ) within 27 SHRs that affect stem growth at nine timepoints with diverse additive and dominance patterns, and these SNPs exhibited complex allelic epistasis over the juvenile growth period. Nineteen genes linked to potential causative alleles that have time-specific or pleiotropic effects, and mostly overlapped with significant signatures of selection within SHRs between climatic regions represented by the association mapping panel. Five genes with potential time-specific effects showed species-specific temporal expression profiles during the juvenile stages of stem growth in five representative Populus species. Our observations revealed the importance of considering temporal genetic basis of complex traits, which will facilitate the molecular design of tree ideotypes.
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Affiliation(s)
- Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Xiaohui Yang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Jianbo Xie
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Mingyang Quan
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Liang Xiao
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Wenjie Lu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Jiaxing Tian
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Chenrui Gong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Jinhui Chen
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Bailian Li
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Department of ForestryNorth Carolina State UniversityRaleighNCUSA
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
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Du Q, Lu W, Quan M, Xiao L, Song F, Li P, Zhou D, Xie J, Wang L, Zhang D. Genome-Wide Association Studies to Improve Wood Properties: Challenges and Prospects. FRONTIERS IN PLANT SCIENCE 2018; 9:1912. [PMID: 30622554 PMCID: PMC6309013 DOI: 10.3389/fpls.2018.01912] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 12/10/2018] [Indexed: 05/02/2023]
Abstract
Wood formation is an excellent model system for quantitative trait analysis due to the strong associations between the transcriptional and metabolic traits that contribute to this complex process. Investigating the genetic architecture and regulatory mechanisms underlying wood formation will enhance our understanding of the quantitative genetics and genomics of complex phenotypic variation. Genome-wide association studies (GWASs) represent an ideal statistical strategy for dissecting the genetic basis of complex quantitative traits. However, elucidating the molecular mechanisms underlying many favorable loci that contribute to wood formation and optimizing GWAS design remain challenging in this omics era. In this review, we summarize the recent progress in GWAS-based functional genomics of wood property traits in major timber species such as Eucalyptus, Populus, and various coniferous species. We discuss several appropriate experimental designs for extensive GWAS in a given undomesticated tree population, such as omics-wide association studies and high-throughput phenotyping technologies. We also explain why more attention should be paid to rare allelic and major structural variation. Finally, we explore the potential use of GWAS for the molecular breeding of trees. Such studies will help provide an integrated understanding of complex quantitative traits and should enable the molecular design of new cultivars.
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Affiliation(s)
- Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Wenjie Lu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Mingyang Quan
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Liang Xiao
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Fangyuan Song
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Peng Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Daling Zhou
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Jianbo Xie
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Longxin Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
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Miart F, Fontaine JX, Pineau C, Demailly H, Thomasset B, Van Wuytswinkel O, Pageau K, Mesnard F. MuSeeQ, a novel supervised image analysis tool for the simultaneous phenotyping of the soluble mucilage and seed morphometric parameters. PLANT METHODS 2018; 14:112. [PMID: 30568724 PMCID: PMC6297999 DOI: 10.1186/s13007-018-0377-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/30/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. RESULTS Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. CONCLUSIONS MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr.
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Affiliation(s)
- Fabien Miart
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
- Present Address: Institut Jean-Pierre Bourgin, UMR1318, INRA/AgroParisTech, Saclay Plant Sciences, INRA Centre de Versailles, 78026 Versailles Cedex, France
| | - Jean-Xavier Fontaine
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - Christophe Pineau
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - Hervé Demailly
- Centre de ressources régionales en biologie moléculaire, Bâtiment Serrres-Transfert, rue Dallery, 80039 Amiens Cedex 1, France
| | - Brigitte Thomasset
- Sorbonne Universités, Génie Enzymatique et Cellulaire, UMR CNRS 7025, Université de Technologie de Compiègne, CS 60319, 60203 Compiègne Cedex, France
| | - Olivier Van Wuytswinkel
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - Karine Pageau
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
| | - François Mesnard
- Laboratoire de Biologie des Plantes et Innovation, EA-3900, UPJV, UFR des Sciences, 33 rue St Leu, 80039 Amiens, France
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25
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Abstract
Regulation of plant root angle is critical for obtaining nutrients and water and is an important trait for plant breeding. A plant’s final, long-term root angle is the net result of a complex series of decisions made by a root tip in response to changes in nutrient availability, impediments, the gravity vector and other stimuli. When a root tip is displaced from the gravity vector, the short-term process of gravitropism results in rapid reorientation of the root toward the vertical. Here, we explore both short- and long-term regulation of root growth angle, using natural variation in tomato to identify shared and separate genetic features of the two responses. Mapping of expression quantitative trait loci mapping and leveraging natural variation between and within species including Arabidopsis suggest a role for PURPLE ACID PHOSPHATASE 27 and CELL DIVISION CYCLE 73 in determining root angle.
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26
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Hussain W, Campbell M, Walia H, Morota G. ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies. PLANT DIRECT 2018; 2:e00091. [PMID: 31245691 PMCID: PMC6508828 DOI: 10.1002/pld3.91] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 10/06/2018] [Accepted: 10/08/2018] [Indexed: 06/09/2023]
Abstract
Owning to advancements in sensor-based, non-destructive phenotyping platforms, researchers are increasingly collecting data with higher temporal resolution. These phenotypes collected over several time points are cataloged as longitudinal traits and used for genome-wide association studies (GWAS). Longitudinal GWAS typically yield a large number of output files, posing a significant challenge to data interpretation and visualization. Efficient, dynamic, and integrative data visualization tools are essential for the interpretation of longitudinal GWAS results for biologists; however, these tools are not widely available to the community. We have developed a flexible and user-friendly Shiny-based online application, ShinyAIM, to dynamically view and interpret temporal GWAS results. The main features of the application include (a) interactive Manhattan plots for single time points, (b) a grid plot to view Manhattan plots for all time points simultaneously, (c) dynamic scatter plots for p-value-filtered selected markers to investigate co-localized genomic regions across time points, (d) and interactive phenotypic data visualization to capture variation and trends in phenotypes. The application is written entirely in the R language and can be used with limited programming experience. ShinyAIM is deployed online as a Shiny web server application at https://chikudaisei.shinyapps.io/shinyaim/, enabling easy access for users without installation. The application can also be launched on a local machine in RStudio.
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Affiliation(s)
- Waseem Hussain
- Department of Animal ScienceUniversity of Nebraska‐LincolnLincolnNebraska
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska
| | - Malachy Campbell
- Department of Animal ScienceUniversity of Nebraska‐LincolnLincolnNebraska
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska
| | - Harkamal Walia
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska
| | - Gota Morota
- Department of Animal ScienceUniversity of Nebraska‐LincolnLincolnNebraska
- Department of Animal and Poultry SciencesVirginia Polytechnic Institute and State UniversityBlacksburgVirginia
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27
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Bray AL, Topp CN. The Quantitative Genetic Control of Root Architecture in Maize. PLANT & CELL PHYSIOLOGY 2018; 59:1919-1930. [PMID: 30020530 PMCID: PMC6178961 DOI: 10.1093/pcp/pcy141] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/04/2018] [Indexed: 05/07/2023]
Abstract
Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits.
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Affiliation(s)
- Adam L Bray
- Division of Plant Sciences, University of Missouri-Columbia, Columbia, MO, USA
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Christopher N Topp
- Donald Danforth Plant Science Center, St. Louis, MO, USA
- Corresponding author: E-mail, ; Fax, 314 587 1501
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28
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Jiang L, Shi C, Ye M, Xi F, Cao Y, Wang L, Zhang M, Sang M, Wu R. A computational‐experimental framework for mapping plant coexistence. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12981] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Libo Jiang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Chaozhong Shi
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Meixia Ye
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Feifei Xi
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Yige Cao
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Lina Wang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Miaomiao Zhang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Mengmeng Sang
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
| | - Rongling Wu
- Center for Computational BiologyCollege of Biological Sciences and TechnologyBeijing Forestry University Beijing China
- Center for Statistical GeneticsPennsylvania State University Hershey PA USA
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29
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van Rooijen R, Kruijer W, Boesten R, van Eeuwijk FA, Harbinson J, Aarts MGM. Natural variation of YELLOW SEEDLING1 affects photosynthetic acclimation of Arabidopsis thaliana. Nat Commun 2017; 8:1421. [PMID: 29123092 PMCID: PMC5680337 DOI: 10.1038/s41467-017-01576-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 09/29/2017] [Indexed: 12/28/2022] Open
Abstract
Exploiting genetic variation for more efficient photosynthesis is an underexplored route towards new crop varieties. This study demonstrates the genetic dissection of higher plant photosynthesis efficiency down to the genomic DNA level, by confirming that allelic sequence variation at the Arabidopsis thaliana YELLOW SEEDLING1 (YS1) gene explains natural diversity in photosynthesis acclimation to high irradiance. We use a genome-wide association study to identify quantitative trait loci (QTLs) involved in the Arabidopsis photosynthetic acclimation response. Candidate genes underlying the QTLs are prioritized according to functional clues regarding gene ontology, expression and function. Reverse genetics and quantitative complementation confirm the candidacy of YS1, which encodes a pentatrico-peptide-repeat (PPR) protein involved in RNA editing of plastid-encoded genes (anterograde signalling). Gene expression analysis and allele sequence comparisons reveal polymorphisms in a light-responsive element in the YS1 promoter that affect its expression, and that of its downstream targets, resulting in the variation in photosynthetic acclimation. Natural genetic variation of photosynthesis is an underexplored resource for plant genetic improvement. Here, the authors find allelic variations of YS1 affect Arabidopsis photosynthesis acclimation using genome-wide association study, reverse genetics, and quantitative complementation approaches.
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Affiliation(s)
- Roxanne van Rooijen
- Laboratory of Genetics, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.,Horticulture and Product Physiology Group, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.,Cluster of Excellence on Plant Science, Heinrich Heine University, Düsseldorf, Germany
| | - Willem Kruijer
- Mathematical and Statistical Methods Group - Biometris, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - René Boesten
- Laboratory of Genetics, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Fred A van Eeuwijk
- Mathematical and Statistical Methods Group - Biometris, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Jeremy Harbinson
- Horticulture and Product Physiology Group, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Mark G M Aarts
- Laboratory of Genetics, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.
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30
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Rahaman MM, Ahsan MA, Gillani Z, Chen M. Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis. J Integr Bioinform 2017; 14:/j/jib.2017.14.issue-3/jib-2017-0028/jib-2017-0028.xml. [PMID: 28862986 PMCID: PMC6042821 DOI: 10.1515/jib-2017-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 05/29/2017] [Accepted: 07/12/2017] [Indexed: 12/20/2022] Open
Abstract
Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.
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Affiliation(s)
- Md. Matiur Rahaman
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Md. Asif Ahsan
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zeeshan Gillani
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- COMSATS Institute of Information Technology – MA Jinnah Campus, Computer Science 1km Defense Road, Lahore 54000, Pakistan
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
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31
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Campbell MT, Du Q, Liu K, Brien CJ, Berger B, Zhang C, Walia H. A Comprehensive Image-based Phenomic Analysis Reveals the Complex Genetic Architecture of Shoot Growth Dynamics in Rice ( Oryza sativa). THE PLANT GENOME 2017; 10. [PMID: 28724075 DOI: 10.3835/plantgenome2016.07.0064] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Early vigor is an important trait for many rice ( L.)-growing environments. However, genetic characterization and improvement for early vigor is hindered by the temporal nature of the trait and strong genotype × environment effects. We explored the genetic architecture of shoot growth dynamics during the early and active tillering stages by applying a functional modeling and genomewide association (GWAS) mapping approach on a diversity panel of ∼360 rice accessions. Multiple loci with small effects on shoot growth trajectory were identified, indicating a complex polygenic architecture. Natural variation for shoot growth dynamics was assessed in a subset of 31 accessions using RNA sequencing and hormone quantification. These analyses yielded a gibberellic acid (GA) catabolic gene, , which could influence GA levels to regulate vigor in the early tillering stage. Given the complex genetic architecture of shoot growth dynamics, the potential of genomic selection (GS) for improving early vigor was explored using all 36,901 single-nucleotide polymorphisms (SNPs) as well as several subsets of the most significant SNPs from GWAS. Shoot growth trajectories could be predicted with reasonable accuracy using the 50 most significant SNPs from GWAS (0.37-0.53); however, the accuracy of prediction was improved by including more markers, which indicates that GS may be an effective strategy for improving shoot growth dynamics during the vegetative growth stage. This study provides insights into the complex genetic architecture and molecular mechanisms underlying early shoot growth dynamics and provides a foundation for improving this complex trait in rice.
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32
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Feldman MJ, Paul RE, Banan D, Barrett JF, Sebastian J, Yee MC, Jiang H, Lipka AE, Brutnell TP, Dinneny JR, Leakey ADB, Baxter I. Time dependent genetic analysis links field and controlled environment phenotypes in the model C4 grass Setaria. PLoS Genet 2017. [PMID: 28644860 PMCID: PMC5507400 DOI: 10.1371/journal.pgen.1006841] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Vertical growth of plants is a dynamic process that is influenced by genetic and environmental factors and has a pronounced effect on overall plant architecture and biomass composition. We have performed six controlled growth trials of an interspecific Setaria italica x Setaria viridis recombinant inbred line population to assess how the genetic architecture of plant height is influenced by developmental queues, water availability and planting density. The non-destructive nature of plant height measurements has enabled us to monitor height throughout the plant life cycle in both field and controlled environments. We find that plant height is reduced under water limitation and high density planting and affected by growth environment (field vs. growth chamber). The results support a model where plant height is a heritable, polygenic trait and that the major genetic loci that influence plant height function independent of growth environment. The identity and contribution of loci that influence height changes dynamically throughout development and the reduction of growth observed in water limited environments is a consequence of delayed progression through the genetic program which establishes plant height in Setaria. In this population, alleles inherited from the weedy S. viridis parent act to increase plant height early, whereas a larger number of small effect alleles inherited from the domesticated S. italica parent collectively act to increase plant height later in development. Growth is a dynamic process that responds to a changing environment. Most of the methods that we have for measuring are static and collecting information throughout an organisms lifecycle is labor and cost prohibitive. Advances in imaging and robotics technology have enabled novel approaches to understanding how plants adapt to the environment. Using the model grass Setaria and new methods for measuring parameters from images, we investigate the genetic architecture of plant height in response to water availability and planting density. Height is one of the most influential components of plant architecture, determining tradeoffs between competition and resource allocation and is an important trait for boosting yields. The non-destructive nature of plant height measurements has enabled us to monitor growth throughout the plant life cycle in both field and controlled environments. We identified several loci controlling height in a population derived from a wild strain of Setaria viridis and its domesticated relative Setaria italica, as well as the developmental time in which these loci act. In this population, alleles inherited from the wild parent act to increase plant height early, whereas a larger number of small effect alleles inherited from the domesticated parent collectively act to increase plant height later in development.
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Affiliation(s)
- Max J. Feldman
- Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America
| | - Rachel E. Paul
- Department of Plant Biology and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Darshi Banan
- Department of Plant Biology and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jennifer F. Barrett
- Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America
| | - Jose Sebastian
- Carnegie Institution for Science, Department of Plant Biology, Stanford, California, United States of America
| | - Muh-Ching Yee
- Carnegie Institution for Science, Department of Plant Biology, Stanford, California, United States of America
| | - Hui Jiang
- Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America
| | - Alexander E. Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Thomas P. Brutnell
- Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America
| | - José R. Dinneny
- Carnegie Institution for Science, Department of Plant Biology, Stanford, California, United States of America
| | - Andrew D. B. Leakey
- Department of Plant Biology and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America
- USDA-ARS, Plant Genetics Research Unit, St. Louis, Missouri, United States of America
- * E-mail:
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33
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Sytar O, Brestic M, Zivcak M, Olsovska K, Kovar M, Shao H, He X. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 578:90-99. [PMID: 27524726 DOI: 10.1016/j.scitotenv.2016.08.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 08/03/2016] [Accepted: 08/03/2016] [Indexed: 05/26/2023]
Abstract
Salinity represents an abiotic stress constraint affecting growth and productivity of plants in many regions of the world. One of the possible solutions is to improve the level of salt resistance using natural genetic variability within crop species. In the context of recent knowledge on salt stress effects and mechanisms of salt tolerance, this review present useful phenomic approach employing different non-invasive imaging systems for detection of quantitative and qualitative changes caused by salt stress at the plant and canopy level. The focus is put on hyperspectral imaging technique, which provides unique opportunities for fast and reliable estimate of numerous characteristics associated both with various structural, biochemical and physiological traits. The method also provides possibilities to combine plant and canopy analyses with a direct determination of salinity in soil. The future perspectives in salt stress applications as well as some limits of the method are also identified.
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Affiliation(s)
- Oksana Sytar
- Research Centre AgroBioTech, Slovak University of Agriculture in Nitra, A. Hlinku 2, Nitra, Slovak Republic
| | - Marian Brestic
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Provincial Key Laboratory of Agrobiology, Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China; Department of Plant Physiology, Slovak University of Agriculture in Nitra, A. Hlinku 2, Nitra, Slovak Republic.
| | - Marek Zivcak
- Department of Plant Physiology, Slovak University of Agriculture in Nitra, A. Hlinku 2, Nitra, Slovak Republic
| | - Katarina Olsovska
- Department of Plant Physiology, Slovak University of Agriculture in Nitra, A. Hlinku 2, Nitra, Slovak Republic
| | - Marek Kovar
- Department of Plant Physiology, Slovak University of Agriculture in Nitra, A. Hlinku 2, Nitra, Slovak Republic
| | - Hongbo Shao
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Provincial Key Laboratory of Agrobiology, Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China; Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
| | - Xiaolan He
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Provincial Key Laboratory of Agrobiology, Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
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Muraya MM, Chu J, Zhao Y, Junker A, Klukas C, Reif JC, Altmann T. Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 89:366-380. [PMID: 27714888 DOI: 10.1111/tpj.13390] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 09/14/2016] [Accepted: 09/19/2016] [Indexed: 05/02/2023]
Abstract
Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase-specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non-invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome-wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker-trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non-parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage-specific growth affecting genes to elucidate important processes operating at different developmental phases.
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Affiliation(s)
- Moses M Muraya
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
- Department of Plant Sciences, Chuka University, P.O. Box 109 - 60400, Chuka, Kenya
| | - Jianting Chu
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
| | - Yusheng Zhao
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
| | - Christian Klukas
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, D-06466, Seeland, Germany
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35
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Topp CN, Bray AL, Ellis NA, Liu Z. How can we harness quantitative genetic variation in crop root systems for agricultural improvement? JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2016; 58:213-25. [PMID: 26911925 DOI: 10.1111/jipb.12470] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 02/21/2016] [Indexed: 05/20/2023]
Abstract
Root systems are a black box obscuring a comprehensive understanding of plant function, from the ecosystem scale down to the individual. In particular, a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them, how we can bridge knowledge gaps and accelerate the derivation of structure-function relationships for roots, and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.
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Affiliation(s)
| | - Adam L Bray
- Donald Danforth Plant Science Center, Saint Louis, Missouri 63132, USA
| | - Nathanael A Ellis
- Donald Danforth Plant Science Center, Saint Louis, Missouri 63132, USA
| | - Zhengbin Liu
- Donald Danforth Plant Science Center, Saint Louis, Missouri 63132, USA
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Flood PJ, Kruijer W, Schnabel SK, van der Schoor R, Jalink H, Snel JFH, Harbinson J, Aarts MGM. Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. PLANT METHODS 2016; 12:14. [PMID: 26884806 PMCID: PMC4754911 DOI: 10.1186/s13007-016-0113-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 01/25/2016] [Indexed: 05/18/2023]
Abstract
BACKGROUND Recent advances in genome sequencing technologies have shifted the research bottleneck in plant sciences from genotyping to phenotyping. This shift has driven the development of phenomics, high-throughput non-invasive phenotyping technologies. RESULTS We describe an automated high-throughput phenotyping platform, the Phenovator, capable of screening 1440 Arabidopsis plants multiple times per day for photosynthesis, growth and spectral reflectance at eight wavelengths. Using this unprecedented phenotyping capacity, we have been able to detect significant genetic differences between Arabidopsis accessions for all traits measured, across both temporal and environmental scales. The high frequency of measurement allowed us to observe that heritability was not only trait specific, but for some traits was also time specific. CONCLUSIONS Such continuous real-time non-destructive phenotyping will allow detailed genetic and physiological investigations of the kinetics of plant homeostasis and development. The success and ultimate outcome of a breeding program will depend greatly on the genetic variance which is sampled. Our observation of temporal fluctuations in trait heritability shows that the moment of measurement can have lasting consequences. Ultimately such phenomic level technologies will provide more dynamic insights into plant physiology, and the necessary data for the omics revolution to reach its full potential.
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Affiliation(s)
- Pádraic J. Flood
- />Laboratory of Genetics, Wageningen University, Wageningen, The Netherlands
- />Horticulture and Production Physiology, Wageningen University, Wageningen, The Netherlands
- />Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Willem Kruijer
- />Biometris, Wageningen University and Research Centre, Wageningen, The Netherlands
| | - Sabine K. Schnabel
- />Biometris, Wageningen University and Research Centre, Wageningen, The Netherlands
| | - Rob van der Schoor
- />Greenhouse Horticulture, Wageningen University and Research Centre, Wageningen, The Netherlands
- />PhenoVation BV, Wageningen, The Netherlands
| | - Henk Jalink
- />Greenhouse Horticulture, Wageningen University and Research Centre, Wageningen, The Netherlands
- />PhenoVation BV, Wageningen, The Netherlands
| | - Jan F. H. Snel
- />Greenhouse Horticulture, Wageningen University and Research Centre, Wageningen, The Netherlands
- />Adviesbureau JFH Snel, Wageningen, The Netherlands
| | - Jeremy Harbinson
- />Horticulture and Production Physiology, Wageningen University, Wageningen, The Netherlands
| | - Mark G. M. Aarts
- />Laboratory of Genetics, Wageningen University, Wageningen, The Netherlands
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37
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Li Z, Sillanpää MJ. Dynamic Quantitative Trait Locus Analysis of Plant Phenomic Data. TRENDS IN PLANT SCIENCE 2015; 20:822-833. [PMID: 26482958 DOI: 10.1016/j.tplants.2015.08.012] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Revised: 08/12/2015] [Accepted: 08/26/2015] [Indexed: 05/27/2023]
Abstract
Advanced platforms have recently become available for automatic and systematic quantification of plant growth and development. These new techniques can efficiently produce multiple measurements of phenotypes over time, and introduce time as an extra dimension to quantitative trait locus (QTL) studies. Functional mapping utilizes a class of statistical models for identifying QTLs associated with the growth characteristics of interest. A major benefit of functional mapping is that it integrates information over multiple timepoints, and therefore could increase the statistical power for QTL detection. We review the current development of computationally efficient functional mapping methods which provide invaluable tools for analyzing large-scale timecourse data that are readily available in our post-genome era.
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Affiliation(s)
- Zitong Li
- Biocenter Oulu, Oulu, Finland; Department of Mathematical Sciences and Department of Biology, University of Oulu, 90014 Oulu, Finland
| | - Mikko J Sillanpää
- Biocenter Oulu, Oulu, Finland; Department of Mathematical Sciences and Department of Biology, University of Oulu, 90014 Oulu, Finland.
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38
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Mapping Quantitative Trait Loci Underlying Function-Valued Traits Using Functional Principal Component Analysis and Multi-Trait Mapping. G3-GENES GENOMES GENETICS 2015; 6:79-86. [PMID: 26530421 PMCID: PMC4704727 DOI: 10.1534/g3.115.024133] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
We previously proposed a simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. In order to better handle the case of noisy phenotype measurements and accommodate the correlation structure among time points, we propose an alternative approach that maintains much of the simplicity and speed of the regression-based method. We overcome noisy measurements by replacing the observed data with a smooth approximation. We then apply functional principal component analysis, replacing the smoothed phenotype data with a small number of principal components. Quantitative trait locus mapping is applied to these dimension-reduced data, either with a multi-trait method or by considering the traits individually and then taking the average or maximum LOD score across traits. We apply these approaches to root gravitropism data on Arabidopsis recombinant inbred lines and further investigate their performance in computer simulations. Our methods have been implemented in the R package, funqtl.
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Bac-Molenaar JA, Vreugdenhil D, Granier C, Keurentjes JJB. Genome-wide association mapping of growth dynamics detects time-specific and general quantitative trait loci. JOURNAL OF EXPERIMENTAL BOTANY 2015; 66:5567-80. [PMID: 25922493 PMCID: PMC4585414 DOI: 10.1093/jxb/erv176] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Growth is a complex trait determined by the interplay between many genes, some of which play a role at a specific moment during development whereas others play a more general role. To identify the genetic basis of growth, natural variation in Arabidopsis rosette growth was followed in 324 accessions by a combination of top-view imaging, high-throughput image analysis, modelling of growth dynamics, and end-point fresh weight determination. Genome-wide association (GWA) mapping of the temporal growth data resulted in the detection of time-specific quantitative trait loci (QTLs), whereas mapping of model parameters resulted in another set of QTLs related to the whole growth curve. The positive correlation between projected leaf area (PLA) at different time points during the course of the experiment suggested the existence of general growth factors with a function in multiple developmental stages or with prolonged downstream effects. Many QTLs could not be identified when growth was evaluated only at a single time point. Eleven candidate genes were identified, which were annotated to be involved in the determination of cell number and size, seed germination, embryo development, developmental phase transition, or senescence. For eight of these, a mutant or overexpression phenotype related to growth has been reported, supporting the identification of true positives. In addition, the detection of QTLs without obvious candidate genes implies the annotation of novel functions for underlying genes.
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Affiliation(s)
- Johanna A Bac-Molenaar
- Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Dick Vreugdenhil
- Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Christine Granier
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, UMR 759, Institut National de la Recherche Agronomique/Ecole Nationale Supérieure d'Agronomie, Place Viala, F-34060 Montpellier, Cedex 1, France
| | - Joost J B Keurentjes
- Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
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40
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Campbell MT, Knecht AC, Berger B, Brien CJ, Wang D, Walia H. Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice. PLANT PHYSIOLOGY 2015; 168:1476-89. [PMID: 26111541 PMCID: PMC4528749 DOI: 10.1104/pp.15.00450] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 06/25/2015] [Indexed: 05/18/2023]
Abstract
Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.
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Affiliation(s)
- Malachy T Campbell
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Avi C Knecht
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Bettina Berger
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Chris J Brien
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Dong Wang
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Harkamal Walia
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
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41
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Fraas S, Lüthen H. Novel imaging-based phenotyping strategies for dissecting crosstalk in plant development. JOURNAL OF EXPERIMENTAL BOTANY 2015; 66:4947-4955. [PMID: 26041318 DOI: 10.1093/jxb/erv265] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In an era of genomics, proteomics, and metabolomics a large number of mutants are available. The discovery of their phenotypes is fast becoming the bottleneck of molecular plant physiology. This crisis can be overcome by imaging-based phenotyping, an emerging, rapidly developing and innovative approach integrating plant and computer science. A tremendous amount of digital image data are automatically analysed using techniques of 'machine vision'. This minireview will shed light on the available imaging strategies and discuss standard methods for the automated analysis of images to give the non-bioinformatic reader an idea how the new technology works. A number of successful platforms will be described and the prospects that image-based phenomics may offer for elucidating hormonal cross-talk and molecular growth physiology will be discussed.
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Affiliation(s)
- Simon Fraas
- Biozentrum Hamburg der Universität, Physiology, Ohnhorststr. 18, D-22609 Hamburg, Germany
| | - Hartwig Lüthen
- Biozentrum Hamburg der Universität, Physiology, Ohnhorststr. 18, D-22609 Hamburg, Germany
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42
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Campbell MT, Knecht AC, Berger B, Brien CJ, Wang D, Walia H. Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice. PLANT PHYSIOLOGY 2015; 168:1476-1489. [PMID: 26111541 DOI: 10.1104/pp15.00450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 06/25/2015] [Indexed: 05/26/2023]
Abstract
Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.
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Affiliation(s)
- Malachy T Campbell
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Avi C Knecht
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Bettina Berger
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Chris J Brien
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Dong Wang
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
| | - Harkamal Walia
- Department of Agronomy and Horticulture (M.T.C., H.W.), Holland Computing Center (A.C.K.), and Department of Statistics (D.W.), University of Nebraska, Lincoln, Nebraska 68583;The Plant Accelerator, Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, South Australia 5064, Australia (B.B.); andPhenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001, Australia (C.J.B.)
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43
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Poland J. Breeding-assisted genomics. CURRENT OPINION IN PLANT BIOLOGY 2015; 24:119-24. [PMID: 25795171 DOI: 10.1016/j.pbi.2015.02.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 02/23/2015] [Accepted: 02/23/2015] [Indexed: 05/18/2023]
Abstract
The revolution of inexpensive sequencing has ushered in an unprecedented age of genomics. The promise of using this technology to accelerate plant breeding is being realized with a vision of genomics-assisted breeding that will lead to rapid genetic gain for expensive and difficult traits. The reality is now that robust phenotypic data is an increasing limiting resource to complement the current wealth of genomic information. While genomics has been hailed as the discipline to fundamentally change the scope of plant breeding, a more symbiotic relationship is likely to emerge. In the context of developing and evaluating large populations needed for functional genomics, none excel in this area more than plant breeders. While genetic studies have long relied on dedicated, well-structured populations, the resources dedicated to these populations in the context of readily available, inexpensive genotyping is making this philosophy less tractable relative to directly focusing functional genomics on material in breeding programs. Through shifting effort for basic genomic studies from dedicated structured populations, to capturing the entire scope of genetic determinants in breeding lines, we can move towards not only furthering our understanding of functional genomics in plants, but also rapidly improving crops for increased food security, availability and nutrition.
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Affiliation(s)
- Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, United States; Department of Agronomy, Kansas State University, Manhattan, KS 66506, United States.
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44
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Chitwood DH, Topp CN. Revealing plant cryptotypes: defining meaningful phenotypes among infinite traits. CURRENT OPINION IN PLANT BIOLOGY 2015; 24:54-60. [PMID: 25658908 DOI: 10.1016/j.pbi.2015.01.009] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 01/20/2015] [Accepted: 01/20/2015] [Indexed: 05/22/2023]
Abstract
The plant phenotype is infinite. Plants vary morphologically and molecularly over developmental time, in response to the environment, and genetically. Exhaustive phenotyping remains not only out of reach, but is also the limiting factor to interpreting the wealth of genetic information currently available. Although phenotyping methods are always improving, an impasse remains: even if we could measure the entirety of phenotype, how would we interpret it? We propose the concept of cryptotype to describe latent, multivariate phenotypes that maximize the separation of a priori classes. Whether the infinite points comprising a leaf outline or shape descriptors defining root architecture, statistical methods to discern the quantitative essence of an organism will be required as we approach measuring the totality of phenotype.
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45
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Lipka AE, Kandianis CB, Hudson ME, Yu J, Drnevich J, Bradbury PJ, Gore MA. From association to prediction: statistical methods for the dissection and selection of complex traits in plants. CURRENT OPINION IN PLANT BIOLOGY 2015; 24:110-8. [PMID: 25795170 DOI: 10.1016/j.pbi.2015.02.010] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Revised: 02/24/2015] [Accepted: 02/27/2015] [Indexed: 05/02/2023]
Abstract
Quantification of genotype-to-phenotype associations is central to many scientific investigations, yet the ability to obtain consistent results may be thwarted without appropriate statistical analyses. Models for association can consider confounding effects in the materials and complex genetic interactions. Selecting optimal models enables accurate evaluation of associations between marker loci and numerous phenotypes including gene expression. Significant improvements in QTL discovery via association mapping and acceleration of breeding cycles through genomic selection are two successful applications of models using genome-wide markers. Given recent advances in genotyping and phenotyping technologies, further refinement of these approaches is needed to model genetic architecture more accurately and run analyses in a computationally efficient manner, all while accounting for false positives and maximizing statistical power.
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Affiliation(s)
- Alexander E Lipka
- University of Illinois, Department of Crop Sciences, Urbana, IL 61801, USA.
| | - Catherine B Kandianis
- Michigan State University, Department of Biochemistry and Molecular Biology, East Lansing, MI 48824, USA; Cornell University, Plant Breeding and Genetics Section, School of Integrative Plant Science, Ithaca, NY 14853, USA
| | - Matthew E Hudson
- University of Illinois, Department of Crop Sciences, Urbana, IL 61801, USA
| | - Jianming Yu
- Iowa State University, Department of Agronomy, Ames, IA 50011, USA
| | - Jenny Drnevich
- University of Illinois, High Performance Biological Computing Group and the Carver Biotechnology Center, Urbana, IL 61801, USA
| | - Peter J Bradbury
- United States Department of Agriculture (USDA) - Agricultural Research Service (ARS), Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Michael A Gore
- Cornell University, Plant Breeding and Genetics Section, School of Integrative Plant Science, Ithaca, NY 14853, USA
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46
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Fahlgren N, Gehan MA, Baxter I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. CURRENT OPINION IN PLANT BIOLOGY 2015; 24:93-9. [PMID: 25733069 DOI: 10.1016/j.pbi.2015.02.006] [Citation(s) in RCA: 310] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Revised: 02/13/2015] [Accepted: 02/13/2015] [Indexed: 05/18/2023]
Abstract
Anticipated population growth, shifting demographics, and environmental variability over the next century are expected to threaten global food security. In the face of these challenges, crop yield for food and fuel must be maintained and improved using fewer input resources. In recent years, genetic tools for profiling crop germplasm has benefited from rapid advances in DNA sequencing, and now similar advances are needed to improve the throughput of plant phenotyping. We highlight recent developments in high-throughput plant phenotyping using robotic-assisted imaging platforms and computer vision-assisted analysis tools.
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Affiliation(s)
- Noah Fahlgren
- Donald Danforth Plant Science Center, United States.
| | - Malia A Gehan
- Donald Danforth Plant Science Center, United States.
| | - Ivan Baxter
- USDA-ARS, Donald Danforth Plant Science Center, United States
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Abstract
Every data visualization can be improved with some level of interactivity. Interactive graphics hold particular promise for the exploration of high-dimensional data. R/qtlcharts is an R package to create interactive graphics for experiments to map quantitative trait loci (QTL) (genetic loci that influence quantitative traits). R/qtlcharts serves as a companion to the R/qtl package, providing interactive versions of R/qtl’s static graphs, as well as additional interactive graphs for the exploration of high-dimensional genotype and phenotype data.
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48
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Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. THE PLANT CELL 2014; 26:4636-55. [PMID: 25501589 PMCID: PMC4311194 DOI: 10.1105/tpc.114.129601] [Citation(s) in RCA: 178] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 11/20/2014] [Accepted: 11/21/2014] [Indexed: 05/18/2023]
Abstract
Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.
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Affiliation(s)
- Dijun Chen
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, P.R. China
| | - Kerstin Neumann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
| | - Swetlana Friedel
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
| | - Benjamin Kilian
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, P.R. China
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
| | - Christian Klukas
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
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49
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Bucksch A, Burridge J, York LM, Das A, Nord E, Weitz JS, Lynch JP. Image-based high-throughput field phenotyping of crop roots. PLANT PHYSIOLOGY 2014. [PMID: 25187526 DOI: 10.1104/pp.114.24351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.
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Affiliation(s)
- Alexander Bucksch
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - James Burridge
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Larry M York
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Abhiram Das
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Eric Nord
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Joshua S Weitz
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Jonathan P Lynch
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
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50
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Bucksch A, Burridge J, York LM, Das A, Nord E, Weitz JS, Lynch JP. Image-based high-throughput field phenotyping of crop roots. PLANT PHYSIOLOGY 2014; 166:470-86. [PMID: 25187526 PMCID: PMC4213080 DOI: 10.1104/pp.114.243519] [Citation(s) in RCA: 128] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Accepted: 08/31/2014] [Indexed: 05/18/2023]
Abstract
Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.
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Affiliation(s)
- Alexander Bucksch
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - James Burridge
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Larry M York
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Abhiram Das
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Eric Nord
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Joshua S Weitz
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
| | - Jonathan P Lynch
- Schools of Biology (A.B., A.D. J.S.W.), Interactive Computing (A.B.), and Physics (J.S.W.), Georgia Institute of Technology, Atlanta, Georgia 30332; andDepartment of Plant Science (J.B., L.M.Y., E.N., J.P.L.) and Intercollege Graduate Degree Program in Ecology (L.M.Y.), Pennsylvania State University, University Park, Pennsylvania 16801
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