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Roscher-Ehrig L, Weber SE, Abbadi A, Malenica M, Abel S, Hemker R, Snowdon RJ, Wittkop B, Stahl A. Phenomic Selection for Hybrid Rapeseed Breeding. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0215. [PMID: 39049840 PMCID: PMC11268845 DOI: 10.34133/plantphenomics.0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/19/2024] [Indexed: 07/27/2024]
Abstract
Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.
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Affiliation(s)
| | - Sven E. Weber
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | | | | | | | | | - Rod J. Snowdon
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants,
Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
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2
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Meyenberg C, Braun V, Longin CFH, Thorwarth P. Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:188. [PMID: 39037501 PMCID: PMC11263437 DOI: 10.1007/s00122-024-04695-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/10/2024] [Indexed: 07/23/2024]
Abstract
KEY MESSAGE Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models. The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky-Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02-0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment ( G × E ) interaction variance.
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Affiliation(s)
- Carina Meyenberg
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | - Vincent Braun
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | | | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany.
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3
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Laurençon M, Legrix J, Wagner MH, Demilly D, Baron C, Rolland S, Ducournau S, Laperche A, Nesi N. Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:156. [PMID: 38858297 PMCID: PMC11164772 DOI: 10.1007/s00122-024-04659-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/25/2024] [Indexed: 06/12/2024]
Abstract
KEY MESSAGE Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.
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Affiliation(s)
- Marianne Laurençon
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Julie Legrix
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Marie-Hélène Wagner
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Didier Demilly
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Cécile Baron
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sophie Rolland
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sylvie Ducournau
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Anne Laperche
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France.
| | - Nathalie Nesi
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
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DeSalvio AJ, Adak A, Murray SC, Jarquín D, Winans ND, Crozier D, Rooney WL. Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments. THE PLANT GENOME 2024; 17:e20454. [PMID: 38715204 DOI: 10.1002/tpg2.20454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/12/2024] [Accepted: 04/01/2024] [Indexed: 07/02/2024]
Abstract
For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.
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Affiliation(s)
- Aaron J DeSalvio
- Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, Texas, USA
| | - Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Diego Jarquín
- Department of Agronomy, University of Florida, Gainesville, Florida, USA
| | - Noah D Winans
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - Daniel Crozier
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
| | - William L Rooney
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA
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Melchinger AE, Fernando R, Melchinger AJ, Schön CC. Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:104. [PMID: 38622324 PMCID: PMC11018695 DOI: 10.1007/s00122-024-04592-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/05/2024] [Indexed: 04/17/2024]
Abstract
KEY MESSAGE Selection response in truncation selection across multiple sets of candidates hinges on their post-selection proportions, which can deviate grossly from their initial proportions. For BLUPs, using a uniform threshold for all candidates maximizes the selection response, irrespective of differences in population parameters. Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response ( Δ G Tot ) for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizingΔ G Tot requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove thatΔ G Tot is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.
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Affiliation(s)
- Albrecht E Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Rohan Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | | | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
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Wang H, Huang Y, Li Y, Cui Y, Xiang X, Zhu Y, Wang Q, Wang X, Ma G, Xiao Q, Huang X, Gao X, Wang J, Lu X, Larkins BA, Wang W, Wu Y. An ARF gene mutation creates flint kernel architecture in dent maize. Nat Commun 2024; 15:2565. [PMID: 38519520 PMCID: PMC10960022 DOI: 10.1038/s41467-024-46955-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Dent and flint kernel architectures are important characteristics that affect the physical properties of maize kernels and their grain end uses. The genes controlling these traits are unknown, so it is difficult to combine the advantageous kernel traits of both. We found mutation of ARFTF17 in a dent genetic background reduces IAA content in the seed pericarp, creating a flint-like kernel phenotype. ARFTF17 is highly expressed in the pericarp and encodes a protein that interacts with and inhibits MYB40, a transcription factor with the dual functions of repressing PIN1 expression and transactivating genes for flavonoid biosynthesis. Enhanced flavonoid biosynthesis could reduce the metabolic flux responsible for auxin biosynthesis. The decreased IAA content of the dent pericarp appears to reduce cell division and expansion, creating a shorter, denser kernel. Introgression of the ARFTF17 mutation into dent inbreds and hybrids improved their kernel texture, integrity, and desiccation, without affecting yield.
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Affiliation(s)
- Haihai Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Yongcai Huang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yujie Li
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Yahui Cui
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Xiaoli Xiang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Yidong Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Qiong Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Xiaoqing Wang
- Forestry and Pomology Research Institute, Shanghai Academy of Agriculture Sciences, Shanghai, 201403, China
| | - Guangjin Ma
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Qiao Xiao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Xing Huang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoyan Gao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Jiechen Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Xiaoduo Lu
- Institute of Molecular Breeding for Maize, Qilu Normal University, Jinan, 250200, China
| | - Brian A Larkins
- School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Wenqin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China.
| | - Yongrui Wu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China.
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Li Y, Yang X, Tong L, Wang L, Xue L, Luan Q, Jiang J. Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery. FRONTIERS IN PLANT SCIENCE 2023; 14:1156430. [PMID: 37670863 PMCID: PMC10475579 DOI: 10.3389/fpls.2023.1156430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023]
Abstract
Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.
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Affiliation(s)
- Yanjie Li
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Xinyu Yang
- Soybean Research Institute, National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Long Tong
- Chongqing Academy of Forestry, Chongqing, China
| | - Lingling Wang
- Forestry and Water Conservancy Bureau of Fuyang District in Hangzhou, Hangzhou, China
| | - Liang Xue
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Qifu Luan
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Jingmin Jiang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
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Puppala N, Nayak SN, Sanz-Saez A, Chen C, Devi MJ, Nivedita N, Bao Y, He G, Traore SM, Wright DA, Pandey MK, Sharma V. Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance. Front Genet 2023; 14:1121462. [PMID: 36968584 PMCID: PMC10030941 DOI: 10.3389/fgene.2023.1121462] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023] Open
Abstract
Climate change is significantly impacting agricultural production worldwide. Peanuts provide food and nutritional security to millions of people across the globe because of its high nutritive values. Drought and heat stress alone or in combination cause substantial yield losses to peanut production. The stress, in addition, adversely impact nutritional quality. Peanuts exposed to drought stress at reproductive stage are prone to aflatoxin contamination, which imposes a restriction on use of peanuts as health food and also adversely impact peanut trade. A comprehensive understanding of the impact of drought and heat stress at physiological and molecular levels may accelerate the development of stress tolerant productive peanut cultivars adapted to a given production system. Significant progress has been achieved towards the characterization of germplasm for drought and heat stress tolerance, unlocking the physiological and molecular basis of stress tolerance, identifying significant marker-trait associations as well major QTLs and candidate genes associated with drought tolerance, which after validation may be deployed to initiate marker-assisted breeding for abiotic stress adaptation in peanut. The proof of concept about the use of transgenic technology to add value to peanuts has been demonstrated. Advances in phenomics and artificial intelligence to accelerate the timely and cost-effective collection of phenotyping data in large germplasm/breeding populations have also been discussed. Greater focus is needed to accelerate research on heat stress tolerance in peanut. A suits of technological innovations are now available in the breeders toolbox to enhance productivity and nutritional quality of peanuts in harsh environments. A holistic breeding approach that considers drought and heat-tolerant traits to simultaneously address both stresses could be a successful strategy to produce climate-resilient peanut genotypes with improved nutritional quality.
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Affiliation(s)
- Naveen Puppala
- Agricultural Science Center at Clovis, New Mexico State University, Las Cruces, NM, United States
- *Correspondence: Naveen Puppala,
| | - Spurthi N. Nayak
- Department of Biotechnology, University of Agricultural Sciences, Dharwad, India
| | - Alvaro Sanz-Saez
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Charles Chen
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Mura Jyostna Devi
- USDA-ARS Vegetable Crops Research, Madison, WI, United States
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States
| | - Nivedita Nivedita
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, United States
| | - Yin Bao
- Biosystems Engineering Department, Auburn University, Auburn, AL, United States
| | - Guohao He
- Department of Plant and Soil Sciences, Tuskegee University, Tuskegee, AL, United States
| | - Sy M. Traore
- Department of Plant and Soil Sciences, Tuskegee University, Tuskegee, AL, United States
| | - David A. Wright
- Department of Biotechnology, Iowa State University, Ames, IA, United States
| | - Manish K. Pandey
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
| | - Vinay Sharma
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India
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9
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Adak A, Murray SC, Anderson SL. Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3 (BETHESDA, MD.) 2022; 13:6851143. [PMID: 36445027 PMCID: PMC9836347 DOI: 10.1093/g3journal/jkac294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/21/2022] [Indexed: 11/30/2022]
Abstract
A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Corresponding author: Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
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