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Morales N, Anche MT, Kaczmar NS, Lepak N, Ni P, Romay MC, Santantonio N, Buckler ES, Gore MA, Mueller LA, Robbins KR. Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize. Genetics 2024; 227:iyae037. [PMID: 38469622 DOI: 10.1093/genetics/iyae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 12/02/2023] [Accepted: 02/18/2024] [Indexed: 03/13/2024] Open
Abstract
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
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Affiliation(s)
- Nicolas Morales
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Mahlet T Anche
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas S Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas Lepak
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Pengzun Ni
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenhe District, Shenyang, Liaoning Province, PR China
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas Santantonio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Lukas A Mueller
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- Boyce Thompson Institute, Ithaca, NY 14853, USA
| | - Kelly R Robbins
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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2
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Brzozowski LJ, Campbell MT, Hu H, Yao L, Caffe M, Gutiérrez LA, Smith KP, Sorrells ME, Gore MA, Jannink JL. Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa). Plant Genome 2023; 16:e20370. [PMID: 37539632 DOI: 10.1002/tpg2.20370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 08/05/2023]
Abstract
Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, New York, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Linxing Yao
- Analytical Resources Core-Bioanalysis and Omics, Colorado State University, Fort Collins, Colorado, USA
| | - Melanie Caffe
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, Saint Paul, Minnesota, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, New York, USA
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3
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Tibbs-Cortes LE, Guo T, Li X, Tanaka R, Vanous AE, Peters D, Gardner C, Magallanes-Lundback M, Deason NT, DellaPenna D, Gore MA, Yu J. Genomic prediction of tocochromanols in exotic-derived maize. Plant Genome 2023; 16:e20286. [PMID: 36575809 DOI: 10.1002/tpg2.20286] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/16/2022] [Indexed: 06/17/2023]
Abstract
Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.
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Affiliation(s)
| | - Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, China
- Huazhong Agricultural Univ., Wuhan, China
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, USA
| | - Adam E Vanous
- USDA ARS, North Central Regional Plant Introduction Station, Ames, IA, USA
| | - David Peters
- USDA ARS, North Central Regional Plant Introduction Station, Ames, IA, USA
| | - Candice Gardner
- USDA ARS, North Central Regional Plant Introduction Station, Ames, IA, USA
| | | | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, USA
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4
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Tanaka R, Wu D, Li X, Tibbs-Cortes LE, Wood JC, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Li X, Deason NT, Schoenbaum GR, Buell CR, DellaPenna D, Yu J, Gore MA. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. Plant Genome 2023; 16:e20276. [PMID: 36321716 DOI: 10.1002/tpg2.20276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.
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Affiliation(s)
- Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | | | - Joshua C Wood
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | | | - Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - John P Hamilton
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Brieanne Vaillancourt
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, 99164, USA
| | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | | | - C Robin Buell
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
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5
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Ferrão LFV, Dhakal R, Dias R, Tieman D, Whitaker V, Gore MA, Messina C, Resende MFR. Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics. Curr Opin Biotechnol 2023; 83:102968. [PMID: 37515935 DOI: 10.1016/j.copbio.2023.102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/31/2023]
Abstract
Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.
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Affiliation(s)
- Luís Felipe V Ferrão
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Rakshya Dhakal
- Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Raquel Dias
- Microbiology and Cell Science Department, University of Florida, Gainesville, FL, United States
| | - Denise Tieman
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Vance Whitaker
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Carlos Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Márcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States.
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6
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Lima DC, Aviles AC, Alpers RT, Perkins A, Schoemaker DL, Costa M, Michel KJ, Kaeppler S, Ertl D, Romay MC, Gage JL, Holland J, Beissinger T, Bohn M, Buckler E, Edwards J, Flint-Garcia S, Gore MA, Hirsch CN, Knoll JE, McKay J, Minyo R, Murray SC, Schnable J, Sekhon RS, Singh MP, Sparks EE, Thomison P, Thompson A, Tuinstra M, Wallace J, Washburn JD, Weldekidan T, Xu W, de Leon N. 2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative. BMC Res Notes 2023; 16:219. [PMID: 37710302 PMCID: PMC10502993 DOI: 10.1186/s13104-023-06430-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/17/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.
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Affiliation(s)
- Dayane Cristina Lima
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA.
| | | | - Ryan Timothy Alpers
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Alden Perkins
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Dylan L Schoemaker
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Martin Costa
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Kathryn J Michel
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Shawn Kaeppler
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27606, USA
| | - Timothy Beissinger
- Department of Crop Science, Center for Integrated Breeding Research, University of Göttingen, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany
| | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Jode Edwards
- USDA ARS CICGRU, 716 Farmhouse Ln, Ames, IA, 50011-1051, USA
| | - Sherry Flint-Garcia
- USDA-ARS, Plant Genetics Research Unit, University of Missouri, 205 Curtis Hall, Columbia, MO, 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Joseph E Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA, 31793, USA
| | - John McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Richard Minyo
- Department of Horticulture and Crop Science, College of Food, Agricultural, and Environmental Sciences, Ohio State University, Wooster, OH, 44691, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - James Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Rajandeep S Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29634, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA
| | | | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Mitchell Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, 49707, USA
| | - Jason Wallace
- Department of Crop & Soil Sciences, University of Georgia, Athens, GA, 30602, USA
| | - Jacob D Washburn
- USDA-ARS, Plant Genetics Research Unit, University of Missouri, 205 Curtis Hall, Columbia, MO, 65211, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
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7
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Lima DC, Washburn JD, Varela JI, Chen Q, Gage JL, Romay MC, Holland J, Ertl D, Lopez-Cruz M, Aguate FM, de Los Campos G, Kaeppler S, Beissinger T, Bohn M, Buckler E, Edwards J, Flint-Garcia S, Gore MA, Hirsch CN, Knoll JE, McKay J, Minyo R, Murray SC, Ortez OA, Schnable JC, Sekhon RS, Singh MP, Sparks EE, Thompson A, Tuinstra M, Wallace J, Weldekidan T, Xu W, de Leon N. Genomes to Fields 2022 Maize genotype by Environment Prediction Competition. BMC Res Notes 2023; 16:148. [PMID: 37461058 DOI: 10.1186/s13104-023-06421-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/28/2023] [Indexed: 07/20/2023] Open
Abstract
OBJECTIVES The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.
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Affiliation(s)
- Dayane Cristina Lima
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA.
| | - Jacob D Washburn
- USDA-ARS Plant Genetics Research Unit, 205 Curtis Hall, Columbia, MO, 65211, USA
| | - José Ignacio Varela
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Qiuyue Chen
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, Raleigh, NC, 27606, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, 50131, USA
| | - Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Fernando M Aguate
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Gustavo de Los Campos
- Department of Plant, Soil and Microbial Sciences, Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Shawn Kaeppler
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Timothy Beissinger
- Department of Crop Science, Center for Integrated Breeding Research, University of Göttingen, Carl-Sprengel-Weg 1, 37075, Göttingen, Germany
| | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Jode Edwards
- USDA ARS CICGRU, 716 Farmhouse Ln, Ames, IA, 50011-1051, USA
| | - Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit, 205 Curtis Hall, Columbia, MO, 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, 55108, USA
| | - Joseph E Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA, 31793, USA
| | - John McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Richard Minyo
- Department of Horticulture and Crop Science, College of Food, Agricultural, and Environmental Sciences, Ohio State University, Wooster, OH, 44691, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Osler A Ortez
- Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, 43210, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Rajandeep S Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29634, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, 19716, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Mitchell Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, 49707, USA
| | - Jason Wallace
- Department of Crop & Soil Sciences, University of Georgia, Athens, GA, 30602, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA
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8
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Vogel G, Giles G, Robbins KR, Gore MA, Smart CD. Quantitative Genetic Analysis of Interactions in the Pepper- Phytophthora capsici Pathosystem. Mol Plant Microbe Interact 2022; 35:1018-1033. [PMID: 35914305 DOI: 10.1094/mpmi-12-21-0307-r] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The development of pepper cultivars with durable resistance to the oomycete Phytophthora capsici has been challenging due to differential interactions between the species that allow certain pathogen isolates to cause disease on otherwise resistant host genotypes. Currently, little is known about the pathogen genes involved in these interactions. To investigate the genetic basis of P. capsici virulence on individual pepper genotypes, we inoculated sixteen pepper accessions, representing commercial varieties, sources of resistance, and host differentials, with 117 isolates of P. capsici, for a total of 1,864 host-pathogen combinations. Analysis of disease outcomes revealed a significant effect of inter-species genotype-by-genotype interactions, although these interactions were quantitative rather than qualitative in scale. Isolates were classified into five pathogen subpopulations, as determined by their genotypes at over 60,000 single-nucleotide polymorphisms (SNPs). While absolute virulence levels on certain pepper accessions significantly differed between subpopulations, a multivariate phenotype reflecting relative virulence levels on certain pepper genotypes compared with others showed the strongest association with pathogen subpopulation. A genome-wide association study (GWAS) identified four pathogen loci significantly associated with virulence, two of which colocalized with putative RXLR effector genes and another with a polygalacturonase gene cluster. All four loci appeared to represent broad-spectrum virulence genes, as significant SNPs demonstrated consistent effects regardless of the host genotype tested. Host genotype-specific virulence variants in P. capsici may be difficult to map via GWAS with all but excessively large sample sizes, perhaps controlled by genes of small effect or by multiple allelic variants that have arisen independently. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
- Gregory Vogel
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, U.S.A
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456, U.S.A
| | - Garrett Giles
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456, U.S.A
| | - Kelly R Robbins
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, U.S.A
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, U.S.A
| | - Christine D Smart
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456, U.S.A
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9
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Lin M, Qiao P, Matschi S, Vasquez M, Ramstein GP, Bourgault R, Mohammadi M, Scanlon MJ, Molina I, Smith LG, Gore MA. Integrating GWAS and TWAS to elucidate the genetic architecture of maize leaf cuticular conductance. Plant Physiol 2022; 189:2144-2158. [PMID: 35512195 PMCID: PMC9342973 DOI: 10.1093/plphys/kiac198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/28/2022] [Indexed: 05/11/2023]
Abstract
The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed. Dissecting the genetic architecture of natural variation for maize (Zea mays L.) leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we performed an integrated genome- and transcriptome-wide association studies (GWAS and TWAS) to identify candidate genes putatively regulating variation in leaf gc. Of the 22 plausible candidate genes identified, 4 were predicted to be involved in cuticle precursor biosynthesis and export, 2 in cell wall modification, 9 in intracellular membrane trafficking, and 7 in the regulation of cuticle development. A gene encoding an INCREASED SALT TOLERANCE1-LIKE1 (ISTL1) protein putatively involved in intracellular protein and membrane trafficking was identified in GWAS and TWAS as the strongest candidate causal gene. A set of maize nested near-isogenic lines that harbor the ISTL1 genomic region from eight donor parents were evaluated for gc, confirming the association between gc and ISTL1 in a haplotype-based association analysis. The findings of this study provide insights into the role of regulatory variation in the development of the maize leaf cuticle and will ultimately assist breeders to develop drought-tolerant maize for target environments.
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Affiliation(s)
- Meng Lin
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Pengfei Qiao
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | | | - Miguel Vasquez
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California 92093, USA
| | | | - Richard Bourgault
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Marc Mohammadi
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Michael J Scanlon
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Isabel Molina
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Laurie G Smith
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California 92093, USA
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10
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Morales N, Ogbonna AC, Ellerbrock BJ, Bauchet GJ, Tantikanjana T, Tecle IY, Powell AF, Lyon D, Menda N, Simoes CC, Saha S, Hosmani P, Flores M, Panitz N, Preble RS, Agbona A, Rabbi I, Kulakow P, Peteti P, Kawuki R, Esuma W, Kanaabi M, Chelangat DM, Uba E, Olojede A, Onyeka J, Shah T, Karanja M, Egesi C, Tufan H, Paterne A, Asfaw A, Jannink JL, Wolfe M, Birkett CL, Waring DJ, Hershberger JM, Gore MA, Robbins KR, Rife T, Courtney C, Poland J, Arnaud E, Laporte MA, Kulembeka H, Salum K, Mrema E, Brown A, Bayo S, Uwimana B, Akech V, Yencho C, de Boeck B, Campos H, Swennen R, Edwards JD, Mueller LA. Breedbase: a digital ecosystem for modern plant breeding. G3 Genes|Genomes|Genetics 2022; 12:6564228. [PMID: 35385099 PMCID: PMC9258556 DOI: 10.1093/g3journal/jkac078] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/14/2022] [Indexed: 01/17/2023]
Abstract
Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.
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Affiliation(s)
- Nicolas Morales
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | - Alex C Ogbonna
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | | | | | | | | | | | - David Lyon
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | - Naama Menda
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | - Surya Saha
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Ezenwanyi Uba
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Adeyemi Olojede
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Joseph Onyeka
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | | | | | - Chiedozie Egesi
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- IITA Ibadan , 200001 Ibadan, Nigeria
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Hale Tufan
- Cornell University , Ithaca, NY 14853, USA
| | | | | | - Jean-Luc Jannink
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | - Clay L Birkett
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | - David J Waring
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | | | | | - Trevor Rife
- Kansas State University , Manhattan, KS 66506, USA
| | | | - Jesse Poland
- Kansas State University , Manhattan, KS 66506, USA
| | | | | | | | | | | | | | | | | | | | - Craig Yencho
- North Carolina State University (NCSU) , Raleigh, NC 27695, USA
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11
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Feng H, Acosta-Gamboa L, Kruse LH, Tracy JD, Chung SH, Nava Fereira AR, Shakir S, Xu H, Sunter G, Gore MA, Casteel CL, Moghe GD, Jander G. Acylsugars protect Nicotiana benthamiana against insect herbivory and desiccation. Plant Mol Biol 2022; 109:505-522. [PMID: 34586580 DOI: 10.1007/s11103-021-01191-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 09/12/2021] [Indexed: 06/13/2023]
Abstract
KEY MESSAGE Nicotiana benthamiana acylsugar acyltransferase (ASAT) is required for protection against desiccation and insect herbivory. Knockout mutations provide a new resource for investigation of plant-aphid and plant-whitefly interactions. Nicotiana benthamiana is used extensively as a transient expression platform for functional analysis of genes from other species. Acylsugars, which are produced in the trichomes, are a hypothesized cause of the relatively high insect resistance that is observed in N. benthamiana. We characterized the N. benthamiana acylsugar profile, bioinformatically identified two acylsugar acyltransferase genes, ASAT1 and ASAT2, and used CRISPR/Cas9 mutagenesis to produce acylsugar-deficient plants for investigation of insect resistance and foliar water loss. Whereas asat1 mutations reduced accumulation, asat2 mutations caused almost complete depletion of foliar acylsucroses. Three hemipteran and three lepidopteran herbivores survived, gained weight, and/or reproduced significantly better on asat2 mutants than on wildtype N. benthamiana. Both asat1 and asat2 mutations reduced the water content and increased leaf temperature. Our results demonstrate the specific function of two ASAT proteins in N. benthamiana acylsugar biosynthesis, insect resistance, and desiccation tolerance. The improved growth of aphids and whiteflies on asat2 mutants will facilitate the use of N. benthamiana as a transient expression platform for the functional analysis of insect effectors and resistance genes from other plant species. Similarly, the absence of acylsugars in asat2 mutants will enable analysis of acylsugar biosynthesis genes from other Solanaceae by transient expression.
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Affiliation(s)
- Honglin Feng
- Boyce Thompson Institute, Ithaca, NY, 14853, USA
| | - Lucia Acosta-Gamboa
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Lars H Kruse
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Jake D Tracy
- Plant-Microbe Biology and Plant Pathology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, 59717, USA
| | | | - Alba Ruth Nava Fereira
- Department of Biology, University of Texas San Antonio, San Antonio, TX, 78249, USA
- Department of Biological Sciences, Northern Illinois University, Dekalb, IL, 60115, USA
| | - Sara Shakir
- Boyce Thompson Institute, Ithaca, NY, 14853, USA
- Gembloux Agro-Bio Tech Institute, The University of Liege, Gembloux, Belgium
| | - Hongxing Xu
- Boyce Thompson Institute, Ithaca, NY, 14853, USA
- College of Life Science, The Shaanxi Normal University, Xi'an, China
| | - Garry Sunter
- Department of Biology, University of Texas San Antonio, San Antonio, TX, 78249, USA
- Department of Biological Sciences, Northern Illinois University, Dekalb, IL, 60115, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Clare L Casteel
- Plant-Microbe Biology and Plant Pathology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Gaurav D Moghe
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Georg Jander
- Boyce Thompson Institute, Ithaca, NY, 14853, USA.
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12
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Wu D, Li X, Tanaka R, Wood JC, Tibbs-Cortes LE, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Diepenbrock CH, Li X, Deason NT, Schoenbaum GR, Yu J, Buell CR, DellaPenna D, Gore MA. Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics 2022; 221:6603118. [PMID: 35666198 PMCID: PMC9339294 DOI: 10.1093/genetics/iyac091] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/01/2022] [Indexed: 11/20/2022] Open
Abstract
Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.
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Affiliation(s)
| | | | | | - Joshua C Wood
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Nolan Bornowski
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - John P Hamilton
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Brieanne Vaillancourt
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Xianran Li
- United States Department of Agriculture, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Nicholas T Deason
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - C Robin Buell
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Dean DellaPenna
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Michael A Gore
- Corresponding author: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
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13
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Hershberger J, Tanaka R, Wood JC, Kaczmar N, Wu D, Hamilton JP, DellaPenna D, Buell CR, Gore MA. Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels. Plant Genome 2022; 15:e20197. [PMID: 35262278 DOI: 10.1002/tpg2.20197] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
Sweet corn (Zea mays L.) is consistently one of the most highly consumed vegetables in the United States, providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step toward understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.
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Affiliation(s)
- Jenna Hershberger
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Joshua C Wood
- Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Nicholas Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - John P Hamilton
- Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - C Robin Buell
- Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
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14
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Brzozowski LJ, Campbell MT, Hu H, Caffe M, Gutiérrez LA, Smith KP, Sorrells ME, Gore MA, Jannink JL. Generalizable approaches for genomic prediction of metabolites in plants. Plant Genome 2022; 15:e20205. [PMID: 35470586 DOI: 10.1002/tpg2.20205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Melanie Caffe
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57006, USA
| | - Lucı A Gutiérrez
- Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Dep. of Agronomy & Plant Genetics, Univ. of Minnesota, St. Paul, MN, 55108, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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15
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Brzozowski LJ, Hu H, Campbell MT, Broeckling CD, Caffe M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.). G3 (Bethesda) 2022; 12:6459173. [PMID: 34893823 PMCID: PMC9210299 DOI: 10.1093/g3journal/jkab419] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022]
Abstract
Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites—avenanthramides, avenacins, and avenacosides—we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Corey D Broeckling
- Bioanalysis and Omics Center of the Analytical Resources Core, Colorado State University, Fort Collins, CO 80523 USA
| | - Melanie Caffe
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57006, USA
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kevin P Smith
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853 USA
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16
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Fumia N, Pironon S, Rubinoff D, Khoury CK, Gore MA, Kantar MB. Wild relatives of potato may bolster its adaptation to new niches under future climate scenarios. Food Energy Secur 2022. [DOI: 10.1002/fes3.360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Nathan Fumia
- Department of Tropical Plant and Soil Science University of Hawaii at Manoa Honolulu Hawaii USA
| | | | - Daniel Rubinoff
- Department of Plant and Environmental Protection Sciences University of Hawaii at Manoa Honolulu Hawaii USA
| | - Colin K. Khoury
- International Center for Tropical Agriculture (CIAT) Cali Colombia
- San Diego Botanic Garden Encinitas California USA
| | - Michael A. Gore
- Plant Breeding and Genetics Section School of Integrative Plant Science Cornell University Ithaca New York USA
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Science University of Hawaii at Manoa Honolulu Hawaii USA
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17
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Nkouaya Mbanjo EG, Hershberger J, Peteti P, Agbona A, Ikpan A, Ogunpaimo K, Kayondo SI, Abioye RS, Nafiu K, Alamu EO, Adesokan M, Maziya-Dixon B, Parkes E, Kulakow P, Gore MA, Egesi C, Rabbi IY. Predicting starch content in cassava fresh roots using near-infrared spectroscopy. Front Plant Sci 2022; 13:990250. [PMID: 36426140 PMCID: PMC9679500 DOI: 10.3389/fpls.2022.990250] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/14/2022] [Indexed: 05/20/2023]
Abstract
The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350-2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400-2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84-0.90; ratio of performance deviation (RPD): 2.49-3.11, ratio of performance to interquartile distance (RPIQ): 3.24-4.08, concordance correlation coefficient (CCC): 0.91-0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871-1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.
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Affiliation(s)
- Edwige Gaby Nkouaya Mbanjo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- *Correspondence: Edwige Gaby Nkouaya Mbanjo,
| | - Jenna Hershberger
- Department of Plant and Environmental Sciences, Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Prasad Peteti
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX, United States
| | - Andrew Ikpan
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Kayode Ogunpaimo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Siraj Ismail Kayondo
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Racheal Smart Abioye
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Kehinde Nafiu
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | | | - Michael Adesokan
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Busie Maziya-Dixon
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Elizabeth Parkes
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Chiedozie Egesi
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- National Root Crops Research Institute (NRCRI), Umuahia, Nigeria
| | - Ismail Yusuf Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
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18
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Fumia N, Rubinoff D, Zenil-Ferguson R, Khoury CK, Pironon S, Gore MA, Kantar MB. Interactions between breeding system and ploidy affect niche breadth in Solanum. R Soc Open Sci 2022; 9:211862. [PMID: 35116168 PMCID: PMC8767206 DOI: 10.1098/rsos.211862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Understanding the factors driving ecological and evolutionary interactions of economically important plant species is important for agricultural sustainability. The geography of crop wild relatives, including wild potatoes (Solanum section Petota), have received attention; however, such information has not been analysed in combination with phylogenetic histories, genomic composition and reproductive systems to identify potential species for use in breeding for abiotic stress tolerance. We used a combination of ordinary least-squares (OLS) and phylogenetic generalized least-squares (PGLM) analyses to identify the discrete climate classes that make up the climate niche that wild potato species inhabit in the context of breeding system and ploidy. Self-incompatible diploid or self-compatible polyploid species significantly increase the number of discrete climate classes within a climate niche inhabited. This result was sustained when correcting for phylogenetic non-independence in the linear model. Our results support the idea that specific breeding system and ploidy combinations increase niche breadth through the decoupling of geographical range and niche diversity, and therefore, these species may be of particular interest for crop adaptation to a changing climate.
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Affiliation(s)
- Nathan Fumia
- Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Daniel Rubinoff
- Department of Plant and Environmental Sciences, University of Hawaii at Manoa, Honolulu, HI, USA
| | | | - Colin K. Khoury
- International Center for Tropical Agriculture (CIAT), Cali, Colombia
- San Diego Botanic Garden, Encinitas, CA, USA
| | | | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Michael B. Kantar
- Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, Honolulu, HI, USA
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19
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Pignon CP, Fernandes SB, Valluru R, Bandillo N, Lozano R, Buckler E, Gore MA, Long SP, Brown PJ, Leakey ADB. Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. Plant Physiol 2021; 187:2544-2562. [PMID: 34618072 PMCID: PMC8644692 DOI: 10.1093/plphys/kiab395] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/26/2021] [Indexed: 05/07/2023]
Abstract
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.
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Affiliation(s)
- Charles P Pignon
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Samuel B Fernandes
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN1 3QE, UK
| | - Nonoy Bandillo
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota 58105, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Edward Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Stephen P Long
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster LA1 1YX, UK
| | - Patrick J Brown
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Andrew D B Leakey
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Author for communication:
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20
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. Theor Appl Genet 2021. [PMID: 34643760 DOI: 10.25739/8p1e-0931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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21
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. Theor Appl Genet 2021; 134:4043-4054. [PMID: 34643760 PMCID: PMC8580906 DOI: 10.1007/s00122-021-03946-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/05/2021] [Indexed: 05/26/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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22
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Ferguson JN, Fernandes SB, Monier B, Miller ND, Allen D, Dmitrieva A, Schmuker P, Lozano R, Valluru R, Buckler ES, Gore MA, Brown PJ, Spalding EP, Leakey ADB. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. Plant Physiol 2021; 187:1481-1500. [PMID: 34618065 PMCID: PMC9040483 DOI: 10.1093/plphys/kiab346] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/29/2021] [Indexed: 05/04/2023]
Abstract
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
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Affiliation(s)
- John N Ferguson
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin
53706, USA
| | - Dylan Allen
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Anna Dmitrieva
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Peter Schmuker
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
- Present address: Lincoln Institute for Agri-Food Technology,
University of Lincoln, Lincoln LN2 2LG, UK
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Patrick J Brown
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Present address: Section of Agricultural Plant Biology,
Department of Plant Sciences, University of California Davis, California 95616,
USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin
53706, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Crop Sciences, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Plant Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Author for communication: ,
Present address: Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA,
UK
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23
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Ferguson JN, Fernandes SB, Monier B, Miller ND, Allen D, Dmitrieva A, Schmuker P, Lozano R, Valluru R, Buckler ES, Gore MA, Brown PJ, Spalding EP, Leakey ADB. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. Plant Physiol 2021; 187:1481-1500. [PMID: 34618065 DOI: 10.1093/plphys/kiab34] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/29/2021] [Indexed: 05/27/2023]
Abstract
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
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Affiliation(s)
- John N Ferguson
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Dylan Allen
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Anna Dmitrieva
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Peter Schmuker
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Patrick J Brown
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
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24
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Esuma W, Ozimati A, Kulakow P, Gore MA, Wolfe MD, Nuwamanya E, Egesi C, Kawuki RS. Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava. G3 (Bethesda) 2021; 11:jkab160. [PMID: 33963852 PMCID: PMC8496257 DOI: 10.1093/g3journal/jkab160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/26/2021] [Indexed: 11/14/2022]
Abstract
Global efforts are underway to develop cassava with enhanced levels of provitamin A carotenoids to sustainably meet increasing demands for food and nutrition where the crop is a major staple. Herein, we tested the effectiveness of genomic selection (GS) for rapid improvement of cassava for total carotenoids content and associated traits. We evaluated 632 clones from Uganda's provitamin A cassava breeding pipeline and 648 West African introductions. At harvest, each clone was assessed for level of total carotenoids, dry matter content, and resistance to cassava brown streak disease (CBSD). All clones were genotyped with diversity array technology and imputed to a set of 23,431 single nucleotide polymorphic markers. We assessed predictive ability of four genomic prediction methods in scenarios of cross-validation, across population prediction, and inclusion of quantitative trait loci markers. Cross-validations produced the highest mean prediction ability for total carotenoids content (0.52) and the lowest for CBSD resistance (0.20), with G-BLUP outperforming other models tested. Across population, predictions showed low ability of Ugandan population to predict the performance of West African clones, with the highest predictive ability recorded for total carotenoids content (0.34) and the lowest for CBSD resistance (0.12) using G-BLUP. By incorporating chromosome 1 markers associated with carotenoids content as independent kernel in the G-BLUP model of a cross-validation scenario, prediction ability slightly improved from 0.52 to 0.58. These results reinforce ongoing efforts aimed at integrating GS into cassava breeding and demonstrate the utility of this tool for rapid genetic improvement.
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Affiliation(s)
- Williams Esuma
- National Crops Resources Research Institute, Kampala, Uganda
| | - Alfred Ozimati
- National Crops Resources Research Institute, Kampala, Uganda
| | - Peter Kulakow
- International Institute for Tropical Agriculture, Ibadan, Nigeria
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Marnin D Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | | | - Chiedozie Egesi
- International Institute for Tropical Agriculture, Ibadan, Nigeria
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Robert S Kawuki
- National Crops Resources Research Institute, Kampala, Uganda
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25
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Baseggio M, Murray M, Wu D, Ziegler G, Kaczmar N, Chamness J, Hamilton JP, Buell CR, Vatamaniuk OK, Buckler ES, Smith ME, Baxter I, Tracy WF, Gore MA. Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels. G3 (Bethesda) 2021; 11:6287658. [PMID: 34849806 PMCID: PMC8496296 DOI: 10.1093/g3journal/jkab186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/25/2021] [Indexed: 01/05/2023]
Abstract
Despite being one of the most consumed vegetables in the United States, the elemental profile of sweet corn (Zea mays L.) is limited in its dietary contributions. To address this through genetic improvement, a genome-wide association study was conducted for the concentrations of 15 elements in fresh kernels of a sweet corn association panel. In concordance with mapping results from mature maize kernels, we detected a probable pleiotropic association of zinc and iron concentrations with nicotianamine synthase5 (nas5), which purportedly encodes an enzyme involved in synthesis of the metal chelator nicotianamine. In addition, a pervasive association signal was identified for cadmium concentration within a recombination suppressed region on chromosome 2. The likely causal gene underlying this signal was heavy metal ATPase3 (hma3), whose counterpart in rice, OsHMA3, mediates vacuolar sequestration of cadmium and zinc in roots, whereby regulating zinc homeostasis and cadmium accumulation in grains. In our association panel, hma3 associated with cadmium but not zinc accumulation in fresh kernels. This finding implies that selection for low cadmium will not affect zinc levels in fresh kernels. Although less resolved association signals were detected for boron, nickel, and calcium, all 15 elements were shown to have moderate predictive abilities via whole-genome prediction. Collectively, these results help enhance our genomics-assisted breeding efforts centered on improving the elemental profile of fresh sweet corn kernels.
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Affiliation(s)
- Matheus Baseggio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Matthew Murray
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Gregory Ziegler
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Nicholas Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - James Chamness
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Olena K Vatamaniuk
- Soil and Crop Sciences Section, Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,US Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, NY 14853, USA
| | - Margaret E Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - William F Tracy
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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26
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Wu D, Tanaka R, Li X, Ramstein GP, Cu S, Hamilton JP, Buell CR, Stangoulis J, Rocheford T, Gore MA. High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain. G3 (Bethesda) 2021; 11:6156830. [PMID: 33677522 PMCID: PMC8759812 DOI: 10.1093/g3journal/jkab059] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/21/2021] [Indexed: 12/18/2022]
Abstract
Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2,000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes rte2 (rotten ear2) and irt1 (iron-regulated transporter1) with boron and nickel, respectively. We also further resolved loci that were previously found to be associated with one or more of five elements (copper, iron, manganese, molybdenum, and/or zinc), with two metal chelator and five metal transporter candidate causal genes identified. The nas5 (nicotianamine synthase5) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.
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Affiliation(s)
- Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | | | - Suong Cu
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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27
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Rogers AR, Dunne JC, Romay C, Bohn M, Buckler ES, Ciampitti IA, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Hood E, Hooker DC, Knoll J, Lee EC, Lorenz A, Lynch JP, McKay J, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Sekhon R, Singh M, Smith M, Springer N, Thelen K, Thomison P, Thompson A, Tuinstra M, Wallace J, Wisser RJ, Xu W, Gilmour AR, Kaeppler SM, De Leon N, Holland JB. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. G3 (Bethesda) 2021; 11:6062399. [PMID: 33585867 DOI: 10.1093/g3journal/jkaa050] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/12/2022]
Abstract
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA
| | - Jeffrey C Dunne
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,USDA-ARS Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY 14853, USA
| | | | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA 50131, USA
| | - Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit, University of Missouri, Columbia, MO 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD 57769, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Elizabeth Hood
- College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA
| | - David C Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON N0P 2C0, Canada
| | - Joseph Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph N1G 2W1, Canada
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA 16802, USA
| | - John McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,Plant Sciences Institute, Iowa State University, Ames, IA 50011, USA
| | - Rajandeep Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
| | - Maninder Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Margaret Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nathan Springer
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Kurt Thelen
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210, USA
| | - Addie Thompson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Jason Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens GA 30602, USA
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
| | - Wenwei Xu
- Texas A& M AgriLife Research, Texas A& M University, Lubbock, TX 79403, USA
| | | | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Natalia De Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA.,Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA.,USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695-7620, USA
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28
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Mabry ME, Turner-Hissong SD, Gallagher EY, McAlvay AC, An H, Edger PP, Moore JD, Pink DAC, Teakle GR, Stevens CJ, Barker G, Labate J, Fuller DQ, Allaby RG, Beissinger T, Decker JE, Gore MA, Pires JC. The Evolutionary History of Wild, Domesticated, and Feral Brassica Oleracea (Brassicaceae). Mol Biol Evol 2021; 38:4419-4434. [PMID: 34157722 PMCID: PMC8476135 DOI: 10.1093/molbev/msab183] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Understanding the evolutionary history of crops, including identifying wild relatives, helps to provide insight for conservation and crop breeding efforts. Cultivated Brassica oleracea has intrigued researchers for centuries due to its wide diversity in forms, which include cabbage, broccoli, cauliflower, kale, kohlrabi, and Brussels sprouts. Yet, the evolutionary history of this species remains understudied. With such different vegetables produced from a single species, B. oleracea is a model organism for understanding the power of artificial selection. Persistent challenges in the study of B. oleracea include conflicting hypotheses regarding domestication and the identity of the closest living wild relative. Using newly generated RNA-seq data for a diversity panel of 224 accessions, which represents 14 different B. oleracea crop types and nine potential wild progenitor species, we integrate phylogenetic and population genetic techniques with ecological niche modeling, archaeological, and literary evidence to examine relationships among cultivars and wild relatives to clarify the origin of this horticulturally important species. Our analyses point to the Aegean endemic B. cretica as the closest living relative of cultivated B. oleracea, supporting an origin of cultivation in the Eastern Mediterranean region. Additionally, we identify several feral lineages, suggesting that cultivated plants of this species can revert to a wild-like state with relative ease. By expanding our understanding of the evolutionary history in B. oleracea, these results contribute to a growing body of knowledge on crop domestication that will facilitate continued breeding efforts including adaptation to changing environmental conditions.
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Affiliation(s)
- Makenzie E Mabry
- Division of Biological Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, U.S.A
| | | | - Evan Y Gallagher
- Division of Biological Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, U.S.A
| | - Alex C McAlvay
- Institute of Economic Botany, The New York Botanical Garden, Bronx, NY, U.S.A
| | - Hong An
- Division of Biological Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, U.S.A
| | - Patrick P Edger
- Department of Horticulture, Michigan State University, MI, USA
| | | | - David A C Pink
- Agriculture and Environment Department, Harper Adams University, UK
| | | | - Chris J Stevens
- School of Archaeology and Museology, Peking University, Beijing, China.,Institute of Archaeology, University College London, London, UK
| | - Guy Barker
- School of Life Science, University of Warwick, UK
| | - Joanne Labate
- USDA, ARS Plant Genetic Resources Unit, Cornell AgriTech, Geneva, NY, USA
| | - Dorian Q Fuller
- Institute of Archaeology, University College London, London, UK.,School of Cultural Heritage, Northwest University, Xi'an, Shaanxi, China.,Department of Archaeology, Max Planck Institute for the Science of Human History, Jena, Germany
| | | | - Timothy Beissinger
- Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, Goettingen, Germany
| | - Jared E Decker
- Division of Animal Sciences, University of Missouri, Columbia, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, U.S.A
| | - J Chris Pires
- Division of Biological Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, U.S.A
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29
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Diepenbrock CH, Ilut DC, Magallanes-Lundback M, Kandianis CB, Lipka AE, Bradbury PJ, Holland JB, Hamilton JP, Wooldridge E, Vaillancourt B, Góngora-Castillo E, Wallace JG, Cepela J, Mateos-Hernandez M, Owens BF, Tiede T, Buckler ES, Rocheford T, Buell CR, Gore MA, DellaPenna D. Eleven biosynthetic genes explain the majority of natural variation in carotenoid levels in maize grain. Plant Cell 2021; 33:882-900. [PMID: 33681994 PMCID: PMC8226291 DOI: 10.1093/plcell/koab032] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 01/26/2021] [Indexed: 05/03/2023]
Abstract
Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.
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Affiliation(s)
| | - Daniel C Ilut
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
| | - Catherine B Kandianis
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Alexander E Lipka
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Peter J Bradbury
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- United States Department of Agriculture—Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - James B Holland
- United States Department of Agriculture—Agricultural Research Service, Plant Science Research Unit, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Edmund Wooldridge
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Brieanne Vaillancourt
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Elsa Góngora-Castillo
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Jason G Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia 30602
| | - Jason Cepela
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Maria Mateos-Hernandez
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Brenda F Owens
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Tyler Tiede
- Present addresses: Nacre Innovations, Houston, Texas 77002 (C.B.K.); Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801 (A.E.L.); University of Michigan, Ann Arbor, MI 48109 (E.W.); Centro de Investigación Científica de Yucatan, CONACYT—Unidad de Biotecnologia, Merida, Yucatan 97200, Mexico (E.G.-C.); Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota 55455 (J.C.); Bayer, Stonington, Illinois 62567 (M.M.-H.); BASF, Dawson, Georgia 39842 (B.F.O.); and Corteva Agriscience, St. Paul, Minnesota 55108 (T.T.)
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- United States Department of Agriculture—Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, Indiana 47907
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824
| | - Michael A Gore
- Authors for correspondence: (C.H.D.), (M.A.G.), and (D.D.P.)
| | - Dean DellaPenna
- Authors for correspondence: (C.H.D.), (M.A.G.), and (D.D.P.)
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30
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Vogel G, LaPlant KE, Mazourek M, Gore MA, Smart CD. A combined BSA-Seq and linkage mapping approach identifies genomic regions associated with Phytophthora root and crown rot resistance in squash. Theor Appl Genet 2021; 134:1015-1031. [PMID: 33388885 DOI: 10.1007/s00122-020-03747-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/05/2020] [Indexed: 06/12/2023]
Abstract
Two QTL mapping approaches were used to identify a total of six QTL associated with Phytophthora root and crown rot resistance in a biparental squash population. Phytophthora root and crown rot, caused by the soilborne oomycete pathogen Phytophthora capsici, leads to severe yield losses in squash (Cucurbita pepo). To identify quantitative trait loci (QTL) involved in resistance to this disease, we crossed a partially resistant squash breeding line with a susceptible zucchini cultivar and evaluated over 13,000 F2 seedlings in a greenhouse screen. Bulked segregant analysis with whole genome resequencing (BSA-Seq) resulted in the identification of five genomic regions-on chromosomes 4, 5, 8, 12, and 16-featuring significant allele frequency differentiation between susceptible and resistant bulks in each of two independent replicates. In addition, we conducted linkage mapping using a population of 176 F3 families derived from individually genotyped F2 individuals. Variation in disease severity among these families was best explained by a four-QTL model, comprising the same loci identified via BSA-Seq on chromosomes 4, 5, and 8 as well as an additional locus on chromosome 19, for a combined total of six QTL identified between both methods. Loci, whether those identified by BSA-Seq or linkage mapping, were of small-to-moderate effect, collectively accounting for 28-35% and individually for 2-10% of the phenotypic variance explained. However, a multiple linear regression model using one marker in each BSA-Seq QTL could predict F2:3 disease severity with only a slight drop in cross-validation accuracy compared to genomic prediction models using genome-wide markers. These results suggest that marker-assisted selection could be a suitable approach for improving Phytophthora crown and root rot resistance in squash.
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Affiliation(s)
- Gregory Vogel
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY, 14456, USA
| | - Kyle E LaPlant
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael Mazourek
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Christine D Smart
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY, 14456, USA.
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31
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Campbell MT, Hu H, Yeats TH, Brzozowski LJ, Caffe-Treml M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices. Front Genet 2021; 12:643733. [PMID: 33868378 PMCID: PMC8044359 DOI: 10.3389/fgene.2021.643733] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.
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Affiliation(s)
- Malachy T. Campbell
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Haixiao Hu
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Trevor H. Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Lauren J. Brzozowski
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Melanie Caffe-Treml
- Seed Technology Lab 113, Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, United States
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin P. Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - Mark E. Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Michael A. Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- R.W. Holley Center for Agriculture & Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY, United States
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32
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Campbell MT, Hu H, Yeats TH, Caffe-Treml M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.). Genetics 2021; 217:iyaa043. [PMID: 33789350 PMCID: PMC8045723 DOI: 10.1093/genetics/iyaa043] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 12/08/2020] [Indexed: 12/13/2022] Open
Abstract
Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.
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Affiliation(s)
- Malachy T Campbell
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Haixiao Hu
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57007, USA
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- R.W. Holley Center for Agriculture & Health US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853, USA
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33
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Jarquin D, de Leon N, Romay C, Bohn M, Buckler ES, Ciampitti I, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Holland JB, Hooker D, Kaeppler SM, Knoll J, Lee EC, Lawrence-Dill CJ, Lynch JP, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Smith M, Springer N, Thomison P, Tuinstra M, Wisser RJ, Xu W, Yu J, Lorenz A. Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project. Front Genet 2021; 11:592769. [PMID: 33763106 PMCID: PMC7982677 DOI: 10.3389/fgene.2020.592769] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 12/21/2020] [Indexed: 11/29/2022] Open
Abstract
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
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Affiliation(s)
- Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, United States
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL, United States
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States.,U.S. Department of Agriculture - Agricultural Research Service Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY, United States
| | - Ignacio Ciampitti
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA, United States.,U.S. Department of Agriculture - Agricultural Research Service Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, United States
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, United States
| | - Sherry Flint-Garcia
- U.S. Department of Agriculture - Agricultural Research Service Plant Genetics Research Unit, University of Missouri, Columbia, MO, United States
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD, United States
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - James B Holland
- U.S. Department of Agriculture - Agricultural Research Service Plant Science Research Unit, North Carolina State University, Raleigh, NC, United States
| | - David Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON, Canada
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, United States
| | - Joseph Knoll
- U.S. Department of Agriculture - Agricultural Research Service Crop Genetics and Breeding Research Unit, Tifton, GA, United States
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Carolyn J Lawrence-Dill
- Department of Agronomy, Iowa State University, Ames, IA, United States.,Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, United States.,Plant Sciences Institute, Iowa State University, Ames, IA, United States
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA, United States
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL, United States
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Patrick S Schnable
- U.S. Department of Agriculture - Agricultural Research Service Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, United States.,Plant Sciences Institute, Iowa State University, Ames, IA, United States
| | - Margaret Smith
- U.S. Department of Agriculture - Agricultural Research Service Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY, United States
| | - Nathan Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, United States
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, United States
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, United States
| | - Wenwei Xu
- Texas A&M AgriLife Research, Texas A&M University, Lubbock, TX, United States
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
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34
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Kim MS, Lozano R, Kim JH, Bae DN, Kim ST, Park JH, Choi MS, Kim J, Ok HC, Park SK, Gore MA, Moon JK, Jeong SC. The patterns of deleterious mutations during the domestication of soybean. Nat Commun 2021; 12:97. [PMID: 33397978 PMCID: PMC7782591 DOI: 10.1038/s41467-020-20337-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 11/25/2020] [Indexed: 01/29/2023] Open
Abstract
Globally, soybean is a major protein and oil crop. Enhancing our understanding of the soybean domestication and improvement process helps boost genomics-assisted breeding efforts. Here we present a genome-wide variation map of 10.6 million single-nucleotide polymorphisms and 1.4 million indels for 781 soybean individuals which includes 418 domesticated (Glycine max), 345 wild (Glycine soja), and 18 natural hybrid (G. max/G. soja) accessions. We describe the enhanced detection of 183 domestication-selective sweeps and the patterns of putative deleterious mutations during domestication and improvement. This predominantly selfing species shows 7.1% reduction of overall deleterious mutations in domesticated soybean relative to wild soybean and a further 1.4% reduction from landrace to improved accessions. The detected domestication-selective sweeps also show reduced levels of deleterious alleles. Importantly, genotype imputation with this resource increases the mapping resolution of genome-wide association studies for seed protein and oil traits in a soybean diversity panel.
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Affiliation(s)
- Myung-Shin Kim
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
- Plant Immunity Research Center, Plant Genomics and Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Korea
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Ji Hong Kim
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
| | - Dong Nyuk Bae
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
| | - Sang-Tae Kim
- Department of Life Science, The Catholic University of Korea, Bucheon, 14662, Korea
| | - Jung-Ho Park
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
| | - Man Soo Choi
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Jaehyun Kim
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Hyun-Choong Ok
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Soo-Kwon Park
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jung-Kyung Moon
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea.
- Agricultural Genome Center, National Academy of Agricultural Sciences, Rural Development Administration, Jeonju, Jeonbuk, 55365, Korea.
| | - Soon-Chun Jeong
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea.
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35
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Lozano R, Gazave E, Dos Santos JPR, Stetter MG, Valluru R, Bandillo N, Fernandes SB, Brown PJ, Shakoor N, Mockler TC, Cooper EA, Taylor Perkins M, Buckler ES, Ross-Ibarra J, Gore MA. Comparative evolutionary genetics of deleterious load in sorghum and maize. Nat Plants 2021; 7:17-24. [PMID: 33452486 DOI: 10.1038/s41477-020-00834-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 12/09/2020] [Indexed: 06/12/2023]
Abstract
Sorghum and maize share a close evolutionary history that can be explored through comparative genomics1,2. To perform a large-scale comparison of the genomic variation between these two species, we analysed ~13 million variants identified from whole-genome resequencing of 499 sorghum lines together with 25 million variants previously identified in 1,218 maize lines. Deleterious mutations in both species were prevalent in pericentromeric regions, enriched in non-syntenic genes and present at low allele frequencies. A comparison of deleterious burden between sorghum and maize revealed that sorghum, in contrast to maize, departed from the domestication-cost hypothesis that predicts a higher deleterious burden among domesticates compared with wild lines. Additionally, sorghum and maize population genetic summary statistics were used to predict a gene deleterious index with an accuracy greater than 0.5. This research represents a key step towards understanding the evolutionary dynamics of deleterious variants in sorghum and provides a comparative genomics framework to start prioritizing these variants for removal through genome editing and breeding.
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Affiliation(s)
- Roberto Lozano
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Elodie Gazave
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- Institute of Biotechnology, Cornell University, Ithaca, NY, USA
| | - Jhonathan P R Dos Santos
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Markus G Stetter
- Botanical Institute, Biozentrum, University of Cologne, Cologne, Germany
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA
- University of Lincoln, Lincoln, UK
| | - Nonoy Bandillo
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Samuel B Fernandes
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Patrick J Brown
- Department of Plant Sciences, University of California Davis, Davis, CA, USA
| | - Nadia Shakoor
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Todd C Mockler
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Elizabeth A Cooper
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - M Taylor Perkins
- Department of Evolution and Ecology, University of California Davis, Davis, CA, USA
| | - Edward S Buckler
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R. W. Holley Center for Agriculture and Health, Ithaca, NY, USA
| | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California Davis, Davis, CA, USA.
- Center for Population Biology and Genome Center, University of California Davis, Davis, CA, USA.
| | - Michael A Gore
- Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.
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Vogel G, Gore MA, Smart CD. Genome-Wide Association Study in New York Phytophthora capsici Isolates Reveals Loci Involved in Mating Type and Mefenoxam Sensitivity. Phytopathology 2021; 111:204-216. [PMID: 32539639 DOI: 10.1094/phyto-04-20-0112-fi] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Phytophthora capsici is a soilborne oomycete plant pathogen that causes severe vegetable crop losses in New York (NY) state and worldwide. This pathogen is difficult to manage, in part due to its production of long-lasting sexual spores and its tendency to quickly evolve fungicide resistance. We single nucleotide polymorphism (SNP) genotyped 252 P. capsici isolates, predominantly from NY, in order to conduct a genome-wide association study for mating type and mefenoxam sensitivity. The population structure and extent of chromosomal copy number variation in this collection of isolates were also characterized. Population structure analyses showed isolates largely clustered by the field site where they were collected, with values of FST between pairs of fields ranging from 0.10 to 0.31. Thirty-three isolates were putative aneuploids, demonstrating evidence for having up to four linkage groups present in more than two copies, and an additional two isolates appeared to be genome-wide triploids. Mating type was mapped to a region on scaffold 4, consistent with previous findings, and mefenoxam sensitivity was associated with several SNP markers at a novel locus on scaffold 62. We identified several candidate genes for mefenoxam sensitivity, including a homolog of yeast ribosome synthesis factor Rrp5, but failed to locate near the scaffold 62 locus any subunits of RNA polymerase I, the hypothesized target site of phenylamide fungicides in oomycetes. This work expands our knowledge of the population biology of P. capsici and provides a foundation for functional validation of candidate genes associated with epidemiologically important phenotypes.
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Affiliation(s)
- Gregory Vogel
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Christine D Smart
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456
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Wang DR, Venturas MD, Mackay DS, Hunsaker DJ, Thorp KR, Gore MA, Pauli D. Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought. New Phytol 2020; 228:898-909. [PMID: 32557592 PMCID: PMC7586954 DOI: 10.1111/nph.16751] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 06/07/2020] [Indexed: 06/11/2023]
Abstract
Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production. In conjunction with an ecophysiological process-based model, heterogeneous data (plant hydraulic traits, spatially-distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects. Cotton was found to have R-shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time-dependent manner. Our study shows how a process-based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.
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Affiliation(s)
- Diane R. Wang
- Department of GeographyUniversity at BuffaloBuffaloNY14261USA
- Present address:
Department of AgronomyPurdue UniversityWest LafayetteIN47907USA
| | | | - D. Scott Mackay
- Department of GeographyUniversity at BuffaloBuffaloNY14261USA
| | | | - Kelly R. Thorp
- US Arid‐Land Agricultural Research CenterMaricopaAZ37860USA
| | - Michael A. Gore
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNY14853USA
| | - Duke Pauli
- School of Plant SciencesUniversity of ArizonaTucsonAZ85721USA
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38
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Brzozowski LJ, Gore MA, Agrawal AA, Mazourek M. Divergence of defensive cucurbitacins in independent Cucurbita pepo domestication events leads to differences in specialist herbivore preference. Plant Cell Environ 2020; 43:2812-2825. [PMID: 32666553 DOI: 10.1111/pce.13844] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 06/23/2020] [Indexed: 05/19/2023]
Abstract
Crop domestication and improvement often concurrently affect plant resistance to pests and production of secondary metabolites, creating challenges for isolating the ecological implications of selection for specific metabolites. Cucurbitacins are bitter triterpenoids with extreme phenotypic differences between Cucurbitaceae lineages, yet we lack integrated models of herbivore preference, cucurbitacin accumulation, and underlying genetic mechanisms. In Cucurbita pepo, we dissected the effect of cotyledon cucurbitacins on preference of a specialist insect pest (Acalymma vittatum) for multiple tissues, assessed genetic loci underlying cucurbitacin accumulation in diverse germplasm and a biparental F2 population (from a cross between two independent domesticates), and characterized quantitative associations between gene expression and metabolites during seedling development. Acalymma vittatum affinity for cotyledons is mediated by cucurbitacins, but other traits contribute to whole-plant resistance. Cotyledon cucurbitacin accumulation was associated with population structure, and our genetic mapping identified a single locus, Bi-4, containing genes relevant to transport and regulation - not biosynthesis - that diverged between lineages. These candidate genes were expressed during seedling development, most prominently a putative secondary metabolite transporter. Taken together, these findings support the testable hypothesis that breeding for plant resistance to insects involves targeting genes for regulation and transport of defensive metabolites, in addition to core biosynthesis genes.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
| | - Anurag A Agrawal
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA
| | - Michael Mazourek
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA
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Anche MT, Kaczmar NS, Morales N, Clohessy JW, Ilut DC, Gore MA, Robbins KR. Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize. Theor Appl Genet 2020; 133:2853-2868. [PMID: 32613265 PMCID: PMC7497340 DOI: 10.1007/s00122-020-03637-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 06/16/2020] [Indexed: 05/12/2023]
Abstract
Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.
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Affiliation(s)
- Mahlet T. Anche
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
| | - Nicholas S. Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
- Present Address: Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
| | - Nicolas Morales
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
| | - James W. Clohessy
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
- Present Address: North Florida Research and Education Center, Plant Pathology Department, University of Florida, Quincy, FL 32351 USA
| | - Daniel C. Ilut
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
| | - Kelly R. Robbins
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
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Matschi S, Vasquez MF, Bourgault R, Steinbach P, Chamness J, Kaczmar N, Gore MA, Molina I, Smith LG. Structure-function analysis of the maize bulliform cell cuticle and its potential role in dehydration and leaf rolling. Plant Direct 2020; 4:e00282. [PMID: 33163853 PMCID: PMC7598327 DOI: 10.1002/pld3.282] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/15/2020] [Accepted: 10/01/2020] [Indexed: 05/03/2023]
Abstract
The hydrophobic cuticle of plant shoots serves as an important interaction interface with the environment. It consists of the lipid polymer cutin, embedded with and covered by waxes, and provides protection against stresses including desiccation, UV radiation, and pathogen attack. Bulliform cells form in longitudinal strips on the adaxial leaf surface, and have been implicated in the leaf rolling response observed in drought-stressed grass leaves. In this study, we show that bulliform cells of the adult maize leaf epidermis have a specialized cuticle, and we investigate its function along with that of bulliform cells themselves. Bulliform cells displayed increased shrinkage compared to other epidermal cell types during dehydration of the leaf, providing a potential mechanism to facilitate leaf rolling. Analysis of natural variation was used to relate bulliform strip patterning to leaf rolling rate, providing further evidence of a role for bulliform cells in leaf rolling. Bulliform cell cuticles showed a distinct ultrastructure with increased cuticle thickness compared to other leaf epidermal cells. Comparisons of cuticular conductance between adaxial and abaxial leaf surfaces, and between bulliform-enriched mutants versus wild-type siblings, showed a correlation between elevated water loss rates and presence or increased density of bulliform cells, suggesting that bulliform cuticles are more water-permeable. Biochemical analysis revealed altered cutin composition and increased cutin monomer content in bulliform-enriched tissues. In particular, our findings suggest that an increase in 9,10-epoxy-18-hydroxyoctadecanoic acid content, and a lower proportion of ferulate, are characteristics of bulliform cuticles. We hypothesize that elevated water permeability of the bulliform cell cuticle contributes to the differential shrinkage of these cells during leaf dehydration, thereby facilitating the function of bulliform cells in stress-induced leaf rolling observed in grasses.
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Affiliation(s)
- Susanne Matschi
- Section of Cell and Developmental BiologyUniversity of California San DiegoLa JollaCAUSA
- Present address:
Department Biochemistry of Plant InteractionsLeibniz Institute of Plant BiochemistryWeinberg 3Halle (Saale)Germany
| | - Miguel F. Vasquez
- Section of Cell and Developmental BiologyUniversity of California San DiegoLa JollaCAUSA
| | | | - Paul Steinbach
- Howard Hughes Medical InstituteUniversity of California San DiegoLa JollaCAUSA
| | - James Chamness
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
- Present address:
Department of Genetics, Cell Biology, and DevelopmentUniversity of MinnesotaSaint PaulMN55108USA
| | - Nicholas Kaczmar
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
| | - Michael A. Gore
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
| | - Isabel Molina
- Department of BiologyAlgoma UniversitySault Ste. MarieONCanada
| | - Laurie G. Smith
- Section of Cell and Developmental BiologyUniversity of California San DiegoLa JollaCAUSA
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Qiao P, Bourgault R, Mohammadi M, Gore MA, Molina I, Scanlon MJ. A maize LIPID TRANSFER PROTEIN may bridge the gap between PHYTOCHROME-mediated light signaling and cuticle biosynthesis. Plant Signal Behav 2020; 15:1790824. [PMID: 32631108 PMCID: PMC8550183 DOI: 10.1080/15592324.2020.1790824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 06/28/2020] [Accepted: 06/29/2020] [Indexed: 05/22/2023]
Abstract
Plant epidermal cuticles are composed of hydrophobic lipids that provide a barrier to non-stomatal water loss, and arose in land plants as an adaptation to the dry terrestrial environment. The expanding maize adult leaf displays a dynamic, proximodistal gradient of cuticle development, from the leaf base to the tip. Recently, our gene co-expression network analyses together with reverse genetic analyses suggested a previously undescribed function for PHYTOCHROME-mediated light signaling during cuticular wax deposition. The present work extends these findings by identifying a role for a specific LIPID TRANSFER PROTEIN (LTP) in cuticle development, and validating it via transgenic experiments in Arabidopsis. Given that LTPs and cuticles both evolved in land plants and are absent from aquatic green algae, we propose that during plant evolution, LTPs arose as one of the innovations of land plants that enabled development of the cuticle.
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Affiliation(s)
- Pengfei Qiao
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Richard Bourgault
- Department of Biology, Essar Convergence Centre, Algoma University, Sault Ste. Marie, Canada
| | - Marc Mohammadi
- Department of Biology, Essar Convergence Centre, Algoma University, Sault Ste. Marie, Canada
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Isabel Molina
- Department of Biology, Essar Convergence Centre, Algoma University, Sault Ste. Marie, Canada
| | - Michael J. Scanlon
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- CONTACT Michael J. Scanlon Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY14853, USA
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Hu H, Gutierrez‐Gonzalez JJ, Liu X, Yeats TH, Garvin DF, Hoekenga OA, Sorrells ME, Gore MA, Jannink J. Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed. Plant Biotechnol J 2020; 18:1211-1222. [PMID: 31677224 PMCID: PMC7152608 DOI: 10.1111/pbi.13286] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 10/24/2019] [Accepted: 10/26/2019] [Indexed: 05/04/2023]
Abstract
Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3' mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co-expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
| | | | - Xinfang Liu
- Corn Research InstituteLiaoning Academy of Agricultural SciencesShenyangChina
| | - Trevor H. Yeats
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
| | | | | | - Mark E. Sorrells
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
| | - Michael A. Gore
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
| | - Jean‐Luc Jannink
- Plant Breeding and Genetics SectionSchool of Integrative Plant ScienceCornell UniversityIthacaNYUSA
- USDA‐ARSRobert W. Holley Center for Agriculture and HealthIthacaNYUSA
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Iragaba P, Kawuki RS, Bauchet G, Ramu P, Tufan HA, Earle ED, Gore MA, Wolfe M. Genomic characterization of Ugandan smallholder farmer-preferred cassava varieties. Crop Sci 2020; 60:1450-1461. [PMID: 32742003 PMCID: PMC7386927 DOI: 10.1002/csc2.20152] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 02/18/2020] [Accepted: 02/24/2020] [Indexed: 06/08/2023]
Abstract
Understanding the genetic relationships among farmer-preferred cassava (Manihot esculenta Crantz) varieties is indispensable to genetic improvement efforts. In this study, we present a genetic analysis of 547 samples of cassava grown by 192 smallholder farmers, which were sampled at random within four districts in Uganda. We genotyped these samples at 287,952 single nucleotide polymorphisms using genotyping-by-sequencing and co-analyzed them with 349 cassava samples from the national breeding program in Uganda. The samples collected from smallholders consisted of 86 genetically unique varieties, as assessed using a genetic distance-based approach. Of these varieties, most were cultivated in only one district (30 in Kibaale, 19 in Masindi, 14 in Arua, and three in Apac), and only three were cultivated across all districts. The genetic differentiation we observed among farming districts in Uganda (mean fixation index [F ST] = .003) is similar to divergence observed within other countries. Despite the fact that none of the breeding lines were directly observed in farmer fields, genetic divergence between the populations was low (F ST = .020). Interestingly, we detected the presence of introgressions from the wild relative M. glaziovii Müll. Arg. on chromosomes 1 and 4, which implies ancestry with cassava breeding lines. Given the apparently similar pool of alleles in the breeding germplasm, it is likely that breeders have the raw genetic material they require to match the farmer-preferred trait combinations necessary for adoption. Our study highlights the importance of understanding the genetic makeup of cassava currently grown by smallholder farmers and relative to that of plant breeding germplasm.
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Affiliation(s)
- Paula Iragaba
- Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell Univ.IthacaNY14853USA
| | - Robert S. Kawuki
- National Crops Resources Research Institute (NaCRRI)PO Box 7084KampalaUganda
| | | | - Punna Ramu
- Cornell University, Institute for Genomic Diversity175 Biotechnology BuildingIthacaNY14853USA
| | - Hale A. Tufan
- Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell Univ.IthacaNY14853USA
- International Programs/College of Agriculture and Life ScienceB75 Mann LibraryIthacaNY14853USA
| | - Elizabeth D. Earle
- Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell Univ.IthacaNY14853USA
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell Univ.IthacaNY14853USA
| | - Marnin Wolfe
- Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell Univ.IthacaNY14853USA
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Jensen SE, Charles JR, Muleta K, Bradbury PJ, Casstevens T, Deshpande SP, Gore MA, Gupta R, Ilut DC, Johnson L, Lozano R, Miller Z, Ramu P, Rathore A, Romay MC, Upadhyaya HD, Varshney RK, Morris GP, Pressoir G, Buckler ES, Ramstein GP. A sorghum practical haplotype graph facilitates genome-wide imputation and cost-effective genomic prediction. Plant Genome 2020; 13:e20009. [PMID: 33016627 DOI: 10.1002/tpg2.20009] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 01/04/2020] [Indexed: 05/22/2023]
Abstract
Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage-only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57-.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low-coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost-effective option for genomic selection.
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Affiliation(s)
- Sarah E Jensen
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean Rigaud Charles
- Chibas and Department of Agriculture and Environmental Sciences, Quisqueya University, Port-au-Prince, Haiti
| | - Kebede Muleta
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
| | - Peter J Bradbury
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
| | - Terry Casstevens
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Santosh P Deshpande
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Rajeev Gupta
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Daniel C Ilut
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Lynn Johnson
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Zachary Miller
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Punna Ramu
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Abhishek Rathore
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Hari D Upadhyaya
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Geoffrey P Morris
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
| | - Gael Pressoir
- Chibas and Department of Agriculture and Environmental Sciences, Quisqueya University, Port-au-Prince, Haiti
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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Baseggio M, Murray M, Magallanes-Lundback M, Kaczmar N, Chamness J, Buckler ES, Smith ME, DellaPenna D, Tracy WF, Gore MA. Natural variation for carotenoids in fresh kernels is controlled by uncommon variants in sweet corn. Plant Genome 2020; 13:e20008. [PMID: 33016632 DOI: 10.1002/tpg2.20008] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 10/30/2019] [Indexed: 06/11/2023]
Abstract
Sweet corn (Zea mays L.) is highly consumed in the United States, but does not make major contributions to the daily intake of carotenoids (provitamin A carotenoids, lutein and zeaxanthin) that would help in the prevention of health complications. A genome-wide association study of seven kernel carotenoids and twelve derivative traits was conducted in a sweet corn inbred line association panel ranging from light to dark yellow in endosperm color to elucidate the genetic basis of carotenoid levels in fresh kernels. In agreement with earlier studies of maize kernels at maturity, we detected an association of β-carotene hydroxylase (crtRB1) with β-carotene concentration and lycopene epsilon cyclase (lcyE) with the ratio of flux between the α- and β-carotene branches in the carotenoid biosynthetic pathway. Additionally, we found that 5% or less of the evaluated inbred lines possessing the shrunken2 (sh2) endosperm mutation had the most favorable lycE allele or crtRB1 haplotype for elevating β-branch carotenoids (β-carotene and zeaxanthin) or β-carotene, respectively. Genomic prediction models with genome-wide markers obtained moderately high predictive abilities for the carotenoid traits, especially lutein, and outperformed models with less markers that targeted candidate genes implicated in the synthesis, retention, and/or genetic control of kernel carotenoids. Taken together, our results constitute an important step toward increasing carotenoids in fresh sweet corn kernels.
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Affiliation(s)
- Matheus Baseggio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Matthew Murray
- Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Nicholas Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - James Chamness
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- Institute for Genomic Diversity, Cornell Univ., Ithaca, NY, 14853, USA
- US Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
| | - Margaret E Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - William F Tracy
- Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
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46
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McFarland BA, AlKhalifah N, Bohn M, Bubert J, Buckler ES, Ciampitti I, Edwards J, Ertl D, Gage JL, Falcon CM, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Holland JB, Hood E, Hooker D, Jarquin D, Kaeppler SM, Knoll J, Kruger G, Lauter N, Lee EC, Lima DC, Lorenz A, Lynch JP, McKay J, Miller ND, Moose SP, Murray SC, Nelson R, Poudyal C, Rocheford T, Rodriguez O, Romay MC, Schnable JC, Schnable PS, Scully B, Sekhon R, Silverstein K, Singh M, Smith M, Spalding EP, Springer N, Thelen K, Thomison P, Tuinstra M, Wallace J, Walls R, Wills D, Wisser RJ, Xu W, Yeh CT, de Leon N. Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets. BMC Res Notes 2020; 13:71. [PMID: 32051026 PMCID: PMC7017475 DOI: 10.1186/s13104-020-4922-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/27/2020] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.
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Affiliation(s)
| | | | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jessica Bubert
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Edward S Buckler
- Cornell University, Ithaca, NY, 14853, USA.,USDA-ARS, Beltsville, MD, USA
| | | | - Jode Edwards
- USDA-ARS, Beltsville, MD, USA.,Iowa State University, Ames, IA, 50011, USA
| | - David Ertl
- Iowa Corn Growers Association, Johnston, IA, 50131, USA
| | | | | | - Sherry Flint-Garcia
- USDA-ARS, Beltsville, MD, USA.,University of Missouri, Columbia, MO, 65211, USA
| | | | | | | | - James B Holland
- USDA-ARS, Beltsville, MD, USA.,North Carolina State University, Raleigh, NC, 27695, USA
| | | | | | | | | | | | - Greg Kruger
- University of Nebraska, Lincoln, NE, 68583, USA
| | - Nick Lauter
- USDA-ARS, Beltsville, MD, USA.,Iowa State University, Ames, IA, 50011, USA
| | | | | | - Aaron Lorenz
- University of Minnesota, St. Paul, MN, 55108, USA
| | | | - John McKay
- Colorado State University, Fort Collins, CO, 80523, USA
| | | | - Stephen P Moose
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Seth C Murray
- Texas A&M University, College Station, TX, 77843, USA
| | | | | | | | | | | | | | | | - Brian Scully
- USDA-ARS, Beltsville, MD, USA.,University of Florida, Gainesville, FL, 32611, USA
| | | | | | | | | | | | | | - Kurt Thelen
- Michigan State University, East Lansing, MI, 48824, USA
| | | | | | | | | | - David Wills
- University of Missouri, Columbia, MO, 65211, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
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47
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Dos Santos JPR, Fernandes SB, McCoy S, Lozano R, Brown PJ, Leakey ADB, Buckler ES, Garcia AAF, Gore MA. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. G3 (Bethesda) 2020; 10:769-781. [PMID: 31852730 PMCID: PMC7003104 DOI: 10.1534/g3.119.400759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 12/15/2019] [Indexed: 11/23/2022]
Abstract
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.
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Affiliation(s)
- Jhonathan P R Dos Santos
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
| | | | | | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science
| | - Patrick J Brown
- Section of Agricultural Plant Biology, Department of Plant Sciences, University of California Davis, 95616, and
| | - Andrew D B Leakey
- Department of Crop Science
- Institute for Genomic Biology
- Department of Plant Biology, University of Illinois at Urbana Champaign, 61801
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- United States Department of Agriculture, Agricultural Research Service, R. W. Holley Center, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Antonio A F Garcia
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil,
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
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48
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Carlson MO, Montilla-Bascon G, Hoekenga OA, Tinker NA, Poland J, Baseggio M, Sorrells ME, Jannink JL, Gore MA, Yeats TH. Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat ( Avena sativa L.). G3 (Bethesda) 2019; 9:2963-2975. [PMID: 31296616 PMCID: PMC6723141 DOI: 10.1534/g3.119.400228] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/08/2019] [Indexed: 01/06/2023]
Abstract
Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.
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Affiliation(s)
- Maryn O Carlson
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | | | | | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, and
| | - Matheus Baseggio
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- R.W. Holley Center for Agriculture and Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
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49
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Khoury CK, Kisel Y, Kantar M, Barber E, Ricciardi V, Klirs C, Kucera L, Mehrabi Z, Johnson N, Klabin S, Valiño Á, Nowakowski K, Bartomeus I, Ramankutty N, Miller A, Schipanski M, Gore MA, Novy A. Science-graphic art partnerships to increase research impact. Commun Biol 2019; 2:295. [PMID: 31396575 PMCID: PMC6684576 DOI: 10.1038/s42003-019-0516-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 06/25/2019] [Indexed: 12/04/2022] Open
Abstract
Graphics are becoming increasingly important for scientists to effectively communicate their findings to broad audiences, but most researchers lack expertise in visual media. We suggest collaboration between scientists and graphic designers as a way forward and discuss the results of a pilot project to test this type of collaboration.
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Affiliation(s)
- Colin K. Khoury
- International Center for Tropical Agriculture (CIAT), Km 17, Recta Cali-Palmira, Apartado Aéreo 6713, 763537 Cali, Colombia
| | | | - Michael Kantar
- Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, 3190 Malie Way, Honolulu, HI 96822 USA
| | - Ellie Barber
- Aspen Global Change Institute, 104 Midland Ave #205, Basalt, CO 81621 USA
| | - Vincent Ricciardi
- The Institute for Resources, Environment, and Sustainability, University of British Columbia, 429-2202 Main Mall, Vancouver, BC V6T 1Z4 Canada
- School of Public Policy and Global Affairs, University of British Columbia, 251-1855 West Mall, Vancouver, BC V6T 1Z2 Canada
| | - Carni Klirs
- World Resources Institute, 10G Street, NE Suite 800, Washington, D.C. 20002 USA
| | - Leah Kucera
- SERVIR Science Coordination Office, NASA Marshall Space Flight Center, National Space Science & Technology Center, 320 Sparkman Drive, Huntsville, AL 35805 USA
| | - Zia Mehrabi
- The Institute for Resources, Environment, and Sustainability, University of British Columbia, 429-2202 Main Mall, Vancouver, BC V6T 1Z4 Canada
- School of Public Policy and Global Affairs, University of British Columbia, 251-1855 West Mall, Vancouver, BC V6T 1Z2 Canada
| | | | | | | | | | - Ignasi Bartomeus
- Integrative Ecology Department, Estación Biológica de Doñana (EBD-CSIC), Avenida Américo Vespucio 26, Isla de la Cartuja, Sevilla, E-41092 Spain
| | - Navin Ramankutty
- The Institute for Resources, Environment, and Sustainability, University of British Columbia, 429-2202 Main Mall, Vancouver, BC V6T 1Z4 Canada
- School of Public Policy and Global Affairs, University of British Columbia, 251-1855 West Mall, Vancouver, BC V6T 1Z2 Canada
| | - Allison Miller
- St. Louis University, Department of Biology, 3507 Laclede Avenue, St. Louis, MO 63103 USA
- Danforth Plant Science Center, 975 North Warson Road, St. Louis, MO 63132 USA
| | - Meagan Schipanski
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523-1170 USA
| | - Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
| | - Ari Novy
- San Diego Botanic Garden, 230 Quail Gardens Drive, Encinitas, CA 92024 USA
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50
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Mbah EU, Nwankwo BC, Njoku DN, Gore MA. Genotypic evaluation of twenty-eight high- and low-cyanide cassava in low-land tropics, southeast Nigeria. Heliyon 2019; 5:e01855. [PMID: 31194086 PMCID: PMC6546964 DOI: 10.1016/j.heliyon.2019.e01855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 02/20/2019] [Accepted: 05/28/2019] [Indexed: 12/01/2022] Open
Abstract
A two-year field experiment was carried out in a randomized complete block design with two replications in 2015/16 and 2016/17 cropping seasons at the National Root Crops Research Institute, Umudike (05° 29′N; 07° 33′E; 122 m above sea level) in Nigeria. The objectives of the study were to assess growth, disease status and yield responses of twenty-eight (28) newly developed high- and low-cyanide cassava genotypes in low-land humid tropics of Umudike, Nigeria. Plant height, stem girth, canopy diameter, number of leaves/plant, cassava mosaic disease (CMD) and cassava bacterial blight (CBB) incidence and severity as well as bulking rate and fresh root yield varied significantly (P < 0.05) amongst the high- and low-cyanide cassava genotypes in both cropping seasons. Also, the results showed that bitter cassava genotypes exhibited greater tolerance to CMD than sweet cassava. However, there was no significant (P > 0.05) difference in bulking rate and fresh root yield between the two groups. The Pearson's and Spearman's ranked associations between fresh root yield of the cassava genotypes and other variables analysed across the two cropping seasons were highly significant (P ≤ 0.01) and positive contrary to the other variables. However, they exhibited different degrees of associations amongst themselves, especially CMD incidence that indicated highly significant and positive association with severity. The principal component analysis across the two cropping seasons indicated eigen-values of the four axes > unity with cumulative variance of 68.98 %. Most of the characters that contributed to the 22.35 % observed variability in principal component (PC1) were CMD incidence and severity, and number of leaves/plant while PC2 also exhibited high vector load from plant attributes such as number of leaves/plant, bulking rate ha−1 and canopy diameter. The bi-plot clustering indicated that genotypes (BI-56, NR110439 and B1-29) exhibited strong similarity amongst themselves across the tested variables. The combined fresh root yield sequence of the first ten high yielder genotypes was in the order: NR110439 > TMS010354 > NR110315 > NR 110238 > NR 110228 > NR 060169 > BI-117 > BI-50 > NR110084 > NR 110181. These cassava genotypes were considered to be better endowed genetically, hence their improvement can be encouraged to ensure high and sustainable root yield. A poly-linear and positive regression was recorded between CMD and root yield as well as between CBB and root yield indicating that they affected fresh root yield of high- and low-cyanide cassava genotypes and demands attention also in cassava improvement studies.
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Affiliation(s)
- Emmanuel Ukaobasi Mbah
- Department of Agronomy, College of Crop and Soil Sciences, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
| | - Blessing Chinwoke Nwankwo
- Department of Agronomy, College of Crop and Soil Sciences, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
| | | | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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