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Gaur A, Jindal Y, Singh V, Tiwari R, Juliana P, Kaushik D, Kumar KJY, Ahlawat OP, Singh G, Sheoran S. GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:177. [PMID: 38972024 DOI: 10.1007/s00122-024-04680-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 06/11/2024] [Indexed: 07/08/2024]
Abstract
KEY MESSAGE Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.
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Affiliation(s)
- Arpit Gaur
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | - Yogesh Jindal
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
| | - Vikram Singh
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
| | - Ratan Tiwari
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Deepak Kaushik
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
| | | | - Om Parkash Ahlawat
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | - Gyanendra Singh
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | - Sonia Sheoran
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India.
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Kaushal S, Gill HS, Billah MM, Khan SN, Halder J, Bernardo A, Amand PS, Bai G, Glover K, Maimaitijiang M, Sehgal SK. Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning. FRONTIERS IN PLANT SCIENCE 2024; 15:1410249. [PMID: 38872880 PMCID: PMC11169824 DOI: 10.3389/fpls.2024.1410249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 05/06/2024] [Indexed: 06/15/2024]
Abstract
Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.
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Affiliation(s)
- Swas Kaushal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Harsimardeep S. Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Mohammad Maruf Billah
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Shahid Nawaz Khan
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Amy Bernardo
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Paul St. Amand
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Guihua Bai
- Hard Winter Wheat Genetics Research Unit, USDA-ARS, Manhattan, KS, United States
| | - Karl Glover
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
| | - Maitiniyazi Maimaitijiang
- Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States
| | - Sunish K. Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, United States
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Sherstneva O, Abdullaev F, Kior D, Yudina L, Gromova E, Vodeneev V. Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging. FRONTIERS IN PLANT SCIENCE 2024; 15:1344826. [PMID: 38371404 PMCID: PMC10869465 DOI: 10.3389/fpls.2024.1344826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/23/2024] [Indexed: 02/20/2024]
Abstract
Early prediction of important agricultural traits in wheat opens up broad prospects for the development of approaches to accelerate the selection of genotypes for further breeding trials. This study is devoted to the search for predictors of biomass accumulation and tolerance of wheat to abiotic stressors. Hyperspectral (HS) and chlorophyll fluorescence (ChlF) parameters were analyzed as predictors under laboratory conditions. The predictive ability of reflectance and normalized difference indices (NDIs), as well as their relationship with parameters of photosynthetic activity, which is a key process influencing organic matter production and crop yields, were analyzed. HS parameters calculated using the wavelengths in Red (R) band and the spectral range next to the red edge (FR-NIR) were found to be correlated with biomass accumulation. The same ranges showed potential for predicting wheat tolerance to elevated temperatures. The relationship of HS predictors with biomass accumulation and heat tolerance were of opposite sign. A number of ChlF parameters also showed statistically significant correlation with biomass accumulation and heat tolerance. A correlation between HS and ChlF parameters, that demonstrated potential for predicting biomass accumulation and tolerance, has been shown. No predictors of drought tolerance were found among the HS and ChlF parameters analyzed.
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Affiliation(s)
- Oksana Sherstneva
- Department of Biophysics, N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
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5
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Janni M, Maestri E, Gullì M, Marmiroli M, Marmiroli N. Plant responses to climate change, how global warming may impact on food security: a critical review. FRONTIERS IN PLANT SCIENCE 2024; 14:1297569. [PMID: 38250438 PMCID: PMC10796516 DOI: 10.3389/fpls.2023.1297569] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Global agricultural production must double by 2050 to meet the demands of an increasing world human population but this challenge is further exacerbated by climate change. Environmental stress, heat, and drought are key drivers in food security and strongly impacts on crop productivity. Moreover, global warming is threatening the survival of many species including those which we rely on for food production, forcing migration of cultivation areas with further impoverishing of the environment and of the genetic variability of crop species with fall out effects on food security. This review considers the relationship of climatic changes and their bearing on sustainability of natural and agricultural ecosystems, as well as the role of omics-technologies, genomics, proteomics, metabolomics, phenomics and ionomics. The use of resource saving technologies such as precision agriculture and new fertilization technologies are discussed with a focus on their use in breeding plants with higher tolerance and adaptability and as mitigation tools for global warming and climate changes. Nevertheless, plants are exposed to multiple stresses. This study lays the basis for the proposition of a novel research paradigm which is referred to a holistic approach and that went beyond the exclusive concept of crop yield, but that included sustainability, socio-economic impacts of production, commercialization, and agroecosystem management.
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Affiliation(s)
- Michela Janni
- Institute of Bioscience and Bioresources (IBBR), National Research Council (CNR), Bari, Italy
- Institute of Materials for Electronics and Magnetism (IMEM), National Research Council (CNR), Parma, Italy
| | - Elena Maestri
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Mariolina Gullì
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Marta Marmiroli
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Nelson Marmiroli
- Consorzio Interuniversitario Nazionale per le Scienze Ambientali (CINSA) Interuniversity Consortium for Environmental Sciences, Parma/Venice, Italy
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6
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Gracia-Romero A, Vatter T, Kefauver SC, Rezzouk FZ, Segarra J, Nieto-Taladriz MT, Aparicio N, Araus JL. Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits. FRONTIERS IN PLANT SCIENCE 2023; 14:1254301. [PMID: 37731983 PMCID: PMC10508639 DOI: 10.3389/fpls.2023.1254301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/03/2023] [Indexed: 09/22/2023]
Abstract
An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.
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Affiliation(s)
- Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Thomas Vatter
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Fatima Zahra Rezzouk
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Joel Segarra
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | | | - Nieves Aparicio
- Agro-technological Institute of Castilla y León (ITACyL), Valladolid, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
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Farhad M, Tripathi SB, Singh RP, Joshi AK, Bhati PK, Vishwakarma MK, Kumar U. GWAS for Early-Establishment QTLs and Their Linkage to Major Phenology-Affecting Genes ( Vrn, Ppd, and Eps) in Bread Wheat. Genes (Basel) 2023; 14:1507. [PMID: 37510411 PMCID: PMC10378780 DOI: 10.3390/genes14071507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/13/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Farmers in northern and central Indian regions prefer to plant wheat early in the season to take advantage of the remaining soil moisture. By planting crops before the start of the season, it is possible to extend the time frame for spring wheat. The early-wheat-establishment experiment began in the 2017 growing season at the Borlaug Institute for South Asia (BISA) in Ludhiana, India, and, after three years of intensive study, numerous agronomic, physiological, and yield data points were gathered. This study aimed to identify wheat lines suitable for early establishment through an analysis of the agro-morphological traits and the genetic mapping of associated genes or quantitative trait loci (QTLs). Advancing the planting schedule by two-three weeks proved to be advantageous in terms of providing a longer duration for crop growth and reducing the need for irrigation. This is attributed to the presence of residual soil moisture resulting from the monsoon season. Early sowing facilitated the selection of genotypes able to withstand early elevated temperatures and a prolonged phenological period. The ideotype, which includes increased photo-growing degree days for booting and heading, as well as a longer grain-filling period, is better suited to early planting than timely planting. Senescence was delayed in combination with a slower rate of canopy temperature rise, which was an excellent trait for early-adapted ideotypes. Thus, a novel approach to wheat breeding would include a screening of genotypes for early planting and an ideotype design with consistent and appropriate features. A genome-wide association study (GWAS) revealed multiple QTLs linked to early adaptation in terms of the yield and its contributing traits. Among them, 44 novel QTLs were also found along with known loci. Furthermore, the study discovered that the phenology regulatory genes, such as Vrn and Ppd, are in the same genomic region, thereby contributing to early adaptation.
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Affiliation(s)
- Md Farhad
- Bangladesh Wheat and Maize Research Institute (BWMRI), Dinajpur 5200, Bangladesh
| | - Shashi B Tripathi
- TERI School of Advanced Studies, Vasant Kunj, New Delhi 110070, India
| | - Ravi P Singh
- International Maize and Wheat Improvement Centre (CIMMYT), Carretera México-Veracruz Km. 45, El Batán, Texcoco C.P. 56237, Mexico
| | - Arun K Joshi
- Borlaug Institute for South Asia (BISA), New Delhi 110012, India
| | - Pradeep K Bhati
- Borlaug Institute for South Asia (BISA), New Delhi 110012, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), New Delhi 110012, India
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Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, Seal S, Das M, Banerjee S, Chowdhury S, Adeyemi SB, Chen JT. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes (Basel) 2023; 14:1484. [PMID: 37510388 PMCID: PMC10380062 DOI: 10.3390/genes14071484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.
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Affiliation(s)
- Dwaipayan Sinha
- Department of Botany, Government General Degree College, Mohanpur 721436, India
| | - Arun Kumar Maurya
- Department of Botany, Multanimal Modi College, Modinagar, Ghaziabad 201204, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Muhammad Majeed
- Department of Botany, University of Gujrat, Punjab 50700, Pakistan
| | - Rachna Agarwal
- Applied Genomics Section, Bhabha Atomic Research Centre, Mumbai 400085, India
| | - Rashmi Mukherjee
- Research Center for Natural and Applied Sciences, Department of Botany (UG & PG), Raja Narendralal Khan Women's College, Gope Palace, Midnapur 721102, India
| | - Sharmistha Ganguly
- Department of Dravyaguna, Institute of Post Graduate Ayurvedic Education and Research, Kolkata 700009, India
| | - Robina Aziz
- Department of Botany, Government, College Women University, Sialkot 51310, Pakistan
| | - Manika Bhatia
- TERI School of Advanced Studies, New Delhi 110070, India
| | - Aqsa Majgaonkar
- Department of Botany, St. Xavier's College (Autonomous), Mumbai 400001, India
| | - Sanchita Seal
- Department of Botany, Polba Mahavidyalaya, Polba 712148, India
| | - Moumita Das
- V. Sivaram Research Foundation, Bangalore 560040, India
| | - Swastika Banerjee
- Department of Botany, Kairali College of +3 Science, Champua, Keonjhar 758041, India
| | - Shahana Chowdhury
- Department of Biotechnology, Faculty of Engineering Sciences, German University Bangladesh, TNT Road, Telipara, Chandona Chowrasta, Gazipur 1702, Bangladesh
| | - Sherif Babatunde Adeyemi
- Ethnobotany/Phytomedicine Laboratory, Department of Plant Biology, Faculty of Life Sciences, University of Ilorin, Ilorin P.M.B 1515, Nigeria
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
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Anilkumar C, Muhammed Azharudheen TP, Sah RP, Sunitha NC, Devanna BN, Marndi BC, Patra BC. Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding. Heredity (Edinb) 2023; 130:335-345. [PMID: 36792661 PMCID: PMC10163052 DOI: 10.1038/s41437-023-00599-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 02/17/2023] Open
Abstract
It is hypothesized that the genome-wide genic markers may increase the prediction accuracy of genomic selection for quantitative traits. To test this hypothesis, a set of candidate gene-based markers for yield and grain traits-related genes cloned across the rice genome were custom-designed. A multi-model, multi-locus genome-wide association study (GWAS) was performed using new genic markers developed to test their effectiveness for gene discovery. Two multi-locus models, FarmCPU and mrMLM, along with a single-locus mixed linear model (MLM), identified 28 significant marker-trait associations. These associations revealed novel causative alleles for grain weight and pleiotropic associations with other traits. For instance, the marker YD91 derived from the gene OsAAP3 on chromosome 1 was consistently associated with grain weight, while the gene has a significant effect on grain yield. Furthermore, nine genomic selection methods, including regression-based and machine learning-based models, were used to predict grain weight using a leave-one-out five-fold cross-validation approach to optimize the genomic selection model with genic markers. Among nine prediction models, Kernel Hilbert Space Regression (RKHS) is the best among regression-based models, and Random Forest Regression (RFR) is the best among machine learning-based models. Genomic prediction accuracies with and without GWAS significant markers were compared to assess the effectiveness of markers. The rapid decreases in prediction accuracy upon dropping GWAS significant markers indicate the effectiveness of new genic markers in genomic selection. Apart from that, the candidate gene-based markers were found to be more effective in genomic selection programs for better accuracy.
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Campbell AM, Hauton C, Baker-Austin C, van Aerle R, Martinez-Urtaza J. An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens. Curr Opin Biotechnol 2023; 80:102898. [PMID: 36739640 DOI: 10.1016/j.copbio.2023.102898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/22/2022] [Accepted: 01/02/2023] [Indexed: 02/05/2023]
Abstract
It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.
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Affiliation(s)
- Amy M Campbell
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK; Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Chris Hauton
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK
| | - Craig Baker-Austin
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Ronny van Aerle
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Jaime Martinez-Urtaza
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK; Department of Genetics and Microbiology, Autonomous University of Barcelona, Barcelona, Spain.
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11
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Eckardt NA, Ainsworth EA, Bahuguna RN, Broadley MR, Busch W, Carpita NC, Castrillo G, Chory J, DeHaan LR, Duarte CM, Henry A, Jagadish SVK, Langdale JA, Leakey ADB, Liao JC, Lu KJ, McCann MC, McKay JK, Odeny DA, Jorge de Oliveira E, Platten JD, Rabbi I, Rim EY, Ronald PC, Salt DE, Shigenaga AM, Wang E, Wolfe M, Zhang X. Climate change challenges, plant science solutions. THE PLANT CELL 2023; 35:24-66. [PMID: 36222573 PMCID: PMC9806663 DOI: 10.1093/plcell/koac303] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.
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Affiliation(s)
- Nancy A Eckardt
- Senior Features Editor, The Plant Cell, American Society of Plant Biologists, USA
| | - Elizabeth A Ainsworth
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, Illinois 61801, USA
| | - Rajeev N Bahuguna
- Centre for Advanced Studies on Climate Change, Dr Rajendra Prasad Central Agricultural University, Samastipur 848125, Bihar, India
| | - Martin R Broadley
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Rothamsted Research, West Common, Harpenden, Hertfordshire, AL5 2JQ, UK
| | - Wolfgang Busch
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Nicholas C Carpita
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
| | - Gabriel Castrillo
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Future Food Beacon of Excellence, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Joanne Chory
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | | | - Carlos M Duarte
- Red Sea Research Center (RSRC) and Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Amelia Henry
- International Rice Research Institute, Rice Breeding Innovations Platform, Los Baños, Laguna 4031, Philippines
| | - S V Krishna Jagadish
- Department of Plant and Soil Science, Texas Tech University, Lubbock, Texas 79410, USA
| | - Jane A Langdale
- Department of Biology, University of Oxford, Oxford, OX1 3RB, UK
| | - Andrew D B Leakey
- Department of Plant Biology, Department of Crop Sciences, and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - James C Liao
- Institute of Biological Chemistry, Academia Sinica, Taipei 11528, Taiwan
| | - Kuan-Jen Lu
- Institute of Biological Chemistry, Academia Sinica, Taipei 11528, Taiwan
| | - Maureen C McCann
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
| | - John K McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Damaris A Odeny
- The International Crops Research Institute for the Semi-Arid Tropics–Eastern and Southern Africa, Gigiri 39063-00623, Nairobi, Kenya
| | | | - J Damien Platten
- International Rice Research Institute, Rice Breeding Innovations Platform, Los Baños, Laguna 4031, Philippines
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), PMB 5320 Ibadan, Oyo, Nigeria
| | - Ellen Youngsoo Rim
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
| | - Pamela C Ronald
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
- Innovative Genomics Institute, Berkeley, California 94704, USA
| | - David E Salt
- School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, UK
- Future Food Beacon of Excellence, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Alexandra M Shigenaga
- Department of Plant Pathology and the Genome Center, University of California, Davis, California 95616, USA
| | - Ertao Wang
- National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Marnin Wolfe
- Auburn University, Dept. of Crop Soil and Environmental Sciences, College of Agriculture, Auburn, Alabama 36849, USA
| | - Xiaowei Zhang
- National Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
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12
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Sun G, Lu H, Zhao Y, Zhou J, Jackson R, Wang Y, Xu L, Wang A, Colmer J, Ober E, Zhao Q, Han B, Zhou J. AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice. THE NEW PHYTOLOGIST 2022; 236:1584-1604. [PMID: 35901246 PMCID: PMC9796158 DOI: 10.1111/nph.18314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/31/2022] [Indexed: 05/11/2023]
Abstract
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
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Affiliation(s)
- Gang Sun
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Hengyun Lu
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Yan Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Robert Jackson
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Yongchun Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ling‐xiang Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Ahong Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Joshua Colmer
- Earlham InstituteNorwich Research ParkNorwichNR4 7UHUK
| | - Eric Ober
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ji Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
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13
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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14
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Anilkumar C, Sunitha NC, Devate NB, Ramesh S. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. PLANTA 2022; 256:87. [PMID: 36149531 DOI: 10.1007/s00425-022-03996-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
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Affiliation(s)
- C Anilkumar
- ICAR-National Rice Research Institute, Cuttack, India
| | - N C Sunitha
- University of Agricultural Sciences, Bangalore, India
| | | | - S Ramesh
- University of Agricultural Sciences, Bangalore, India.
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15
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Khan H, Krishnappa G, Kumar S, Mishra CN, Krishna H, Devate NB, Rathan ND, Parkash O, Yadav SS, Srivastava P, Biradar S, Kumar M, Singh GP. Genome-wide association study for grain yield and component traits in bread wheat ( Triticum aestivum L.). Front Genet 2022; 13:982589. [PMID: 36092913 PMCID: PMC9458894 DOI: 10.3389/fgene.2022.982589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/20/2022] [Indexed: 11/25/2022] Open
Abstract
Genomic regions governing days to heading (DH), grain filling duration (GFD), grain number per spike (GNPS), grain weight per spike (GWPS), plant height (PH), and grain yield (GY) were investigated in a set of 280 diverse bread wheat genotypes. The genome-wide association studies (GWAS) panel was genotyped using a 35K Axiom Array and phenotyped in five environments. The GWAS analysis showed a total of 27 Bonferroni-corrected marker-trait associations (MTAs) on 15 chromosomes representing all three wheat subgenomes. The GFD showed the highest MTAs (8), followed by GWPS (7), GY (4), GNPS (3), PH (3), and DH (2). Furthermore, 20 MTAs were identified with more than 10% phenotypic variation. A total of five stable MTAs (AX-95024590, AX-94425015, AX-95210025 AX-94539354, and AX-94978133) were identified in more than one environment and associated with the expression of DH, GFD, GNPS, and GY. Similarly, two novel pleiotropic genomic regions with associated MTAs i.e. AX-94978133 (4D) and AX-94539354 (6A) harboring co-localized QTLs governing two or more traits were also identified. In silico analysis revealed that the SNPs were located on important putative candidate genes such as F-box-like domain superfamily, Lateral organ boundaries, LOB, Thioredoxin-like superfamily Glutathione S-transferase, RNA-binding domain superfamily, UDP-glycosyltransferase family, Serine/threonine-protein kinase, Expansin, Patatin, Exocyst complex component Exo70, DUF1618 domain, Protein kinase domain involved in the regulation of grain size, grain number, growth and development, grain filling duration, and abiotic stress tolerance. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection (MAS).
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Affiliation(s)
- Hanif Khan
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, India
| | - Gopalareddy Krishnappa
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, India
- ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Satish Kumar
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hari Krishna
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | | | - Om Parkash
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, India
| | - Sonu Singh Yadav
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Suma Biradar
- University of Agricultural Sciences, Dharwad, India
| | - Monu Kumar
- ICAR-Indian Agricultural Research Institute, Jharkhand, India
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16
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Juliana P, He X, Poland J, Roy KK, Malaker PK, Mishra VK, Chand R, Shrestha S, Kumar U, Roy C, Gahtyari NC, Joshi AK, Singh RP, Singh PK. Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1965-1983. [PMID: 35416483 PMCID: PMC9205839 DOI: 10.1007/s00122-022-04087-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
KEY MESSAGE Genomic selection is a promising tool to select for spot blotch resistance and index-based selection can simultaneously select for spot blotch resistance, heading and plant height. A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat.
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Affiliation(s)
- Philomin Juliana
- Borlaug Institute for South Asia (BISA), Ludhiana, Punjab, India
| | - Xinyao He
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | - Jesse Poland
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Krishna K Roy
- Bangladesh Wheat and Maize Research Institute, Nashipur, Dinajpur, 5200, Bangladesh
| | - Paritosh K Malaker
- Bangladesh Wheat and Maize Research Institute, Nashipur, Dinajpur, 5200, Bangladesh
| | - Vinod K Mishra
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Ramesh Chand
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Sandesh Shrestha
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, USA
| | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, Punjab, India
| | - Chandan Roy
- Department of Plant Breeding and Genetics, Bihar Agricultural University, Sabour, Bihar, 813210, India
| | - Navin C Gahtyari
- ICAR-Vivekanand Parvatiya Krishi Anushandhan Sansthan, Almora, Uttarakhand, 263601, India
| | - Arun K Joshi
- Borlaug Institute for South Asia (BISA), Ludhiana, Punjab, India
- CIMMYT-India, NASC Complex, DPS Marg, New Delhi, India
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico.
| | - Pawan K Singh
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico.
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17
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Atanda SA, Govindan V, Singh R, Robbins KR, Crossa J, Bentley AR. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1939-1950. [PMID: 35348821 PMCID: PMC9205816 DOI: 10.1007/s00122-022-04085-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/16/2022] [Indexed: 06/08/2023]
Abstract
Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
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Affiliation(s)
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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18
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InDels Identification and Association Analysis with Spike and Awn Length in Chinese Wheat Mini-Core Collection. Int J Mol Sci 2022; 23:ijms23105587. [PMID: 35628397 PMCID: PMC9146729 DOI: 10.3390/ijms23105587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 11/17/2022] Open
Abstract
Diversity surveys of germplasm are important for gaining insight into the genomic basis for crop improvement; especially InDels, which are poorly understood in hexaploid common wheat. Here, we describe a map of 89,923 InDels from exome sequencing of 262 accessions of a Chinese wheat mini-core collection. Population structure analysis, principal component analysis and selective sweep analysis between landraces and cultivars were performed. Further genome-wide association study (GWAS) identified five QTL (Quantitative Trait Loci) that were associated with spike length, two of them, on chromosomes 2B and 6A, were detected in 10 phenotypic data sets. Assisted with RNA-seq data, we identified 14 and 21 genes, respectively that expressed in spike and rachis within the two QTL regions that can be further investigated for candidate genes discovery. Moreover, InDels were found to be associated with awn length on chromosomes 5A, 6B and 4A, which overlapped with previously reported genetic loci B1 (Tipped 1), B2 (Tipped 2) and Hd (Hooded). One of the genes TaAGL6 that was previously shown to affect floral organ development was found at the B2 locus to affect awn length development. Our study shows that trait-associated InDels may contribute to wheat improvement and may be valuable molecular markers for future wheat breeding.
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19
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Lozada DN, Bosland PW, Barchenger DW, Haghshenas-Jaryani M, Sanogo S, Walker S. Chile Pepper ( Capsicum) Breeding and Improvement in the "Multi-Omics" Era. FRONTIERS IN PLANT SCIENCE 2022; 13:879182. [PMID: 35592583 PMCID: PMC9113053 DOI: 10.3389/fpls.2022.879182] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.
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Affiliation(s)
- Dennis N. Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
| | - Paul W. Bosland
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
| | | | - Mahdi Haghshenas-Jaryani
- Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Soumaila Sanogo
- Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM, United States
| | - Stephanie Walker
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, United States
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Li A, Hao C, Wang Z, Geng S, Jia M, Wang F, Han X, Kong X, Yin L, Tao S, Deng Z, Liao R, Sun G, Wang K, Ye X, Jiao C, Lu H, Zhou Y, Liu D, Fu X, Zhang X, Mao L. Wheat breeding history reveals synergistic selection of pleiotropic genomic sites for plant architecture and grain yield. MOLECULAR PLANT 2022; 15:504-519. [PMID: 35026438 DOI: 10.1016/j.molp.2022.01.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/24/2021] [Accepted: 01/07/2022] [Indexed: 05/10/2023]
Abstract
Diversity surveys of crop germplasm are important for gaining insights into the genomic basis for plant architecture and grain yield improvement, which is still poorly understood in wheat. In this study, we exome sequenced 287 wheat accessions that were collected in the past 100 years. Population genetics analysis identified that 6.7% of the wheat genome falls within the selective sweeps between landraces and cultivars, which harbors the genes known for yield improvement. These regions were asymmetrically distributed on the A and B subgenomes with regulatory genes being favorably selected. Genome-wide association study (GWAS) identified genomic loci associated with traits for yield potential, and two underlying genes, TaARF12 encoding an auxin response factor and TaDEP1 encoding the G-protein γ-subunit, were located and characterized to pleiotropically regulate both plant height and grain weight. Elite single-nucleotide haplotypes with increased allele frequency in cultivars relative to the landraces were identified and found to have accumulated over the course of breeding. Interestingly, we found that TaARF12 and TaDEP1 function in epistasis with the classical plant height Rht-1 locus, leading to propose a "Green Revolution"-based working model for historical wheat breeding. Collectively, our study identifies selection signatures that fine-tune the gibberellin pathway during modern wheat breeding and provides a wealth of genomic diversity resources for the wheat research community.
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Affiliation(s)
- Aili Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Chenyang Hao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhenyu Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shuaifeng Geng
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Meiling Jia
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Fang Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiang Han
- The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xingchen Kong
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lingjie Yin
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shu Tao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhongyin Deng
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ruyi Liao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guoliang Sun
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ke Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xingguo Ye
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chengzhi Jiao
- Novogene Bioinformatics Institute, Beijing 100083, China
| | - Hongfeng Lu
- Novogene Bioinformatics Institute, Beijing 100083, China
| | - Yun Zhou
- Collaborative Innovation Center of Crop Stress Biology & Institute of Plant Stress Biology, School of Life Science, Henan University, Kaifeng 475004, China
| | - Dengcai Liu
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, Sichuan 611130, China
| | - Xiangdong Fu
- The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Xueyong Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Long Mao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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21
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Sandhu KS, Patil SS, Aoun M, Carter AH. Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat. Front Genet 2022; 13:831020. [PMID: 35173770 PMCID: PMC8841657 DOI: 10.3389/fgene.2022.831020] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Shruti Sunil Patil
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States1
| | - Meriem Aoun
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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22
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Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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23
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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24
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Morota G, Jarquin D, Campbell MT, Iwata H. Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods Mol Biol 2022; 2539:269-296. [PMID: 35895210 DOI: 10.1007/978-1-0716-2537-8_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Affiliation(s)
- Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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25
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Westhues CC, Mahone GS, da Silva S, Thorwarth P, Schmidt M, Richter JC, Simianer H, Beissinger TM. Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks. FRONTIERS IN PLANT SCIENCE 2021; 12:699589. [PMID: 34880880 PMCID: PMC8647909 DOI: 10.3389/fpls.2021.699589] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 10/15/2021] [Indexed: 05/26/2023]
Abstract
The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.
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Affiliation(s)
- Cathy C. Westhues
- Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, Goettingen, Germany
- Center for Integrated Breeding Research, University of Goettingen, Goettingen, Germany
| | | | - Sofia da Silva
- Kleinwanzlebener Saatzucht (KWS) SAAT SE, Einbeck, Germany
| | | | - Malthe Schmidt
- Kleinwanzlebener Saatzucht (KWS) SAAT SE, Einbeck, Germany
| | | | - Henner Simianer
- Center for Integrated Breeding Research, University of Goettingen, Goettingen, Germany
- Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, Goettingen, Germany
| | - Timothy M. Beissinger
- Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, Goettingen, Germany
- Center for Integrated Breeding Research, University of Goettingen, Goettingen, Germany
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26
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Tomar V, Singh D, Dhillon GS, Chung YS, Poland J, Singh RP, Joshi AK, Gautam Y, Tiwari BS, Kumar U. Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:720123. [PMID: 34691100 PMCID: PMC8531512 DOI: 10.3389/fpls.2021.720123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2-3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.
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Affiliation(s)
- Vipin Tomar
- Borlaug Institute for South Asia, Ludhiana, India
- Department of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
- International Maize and Wheat Improvement Center, New Delhi, India
| | - Daljit Singh
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Guriqbal Singh Dhillon
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, India
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju-si, South Korea
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Ravi Prakash Singh
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia, Ludhiana, India
- International Maize and Wheat Improvement Center, New Delhi, India
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | - Budhi Sagar Tiwari
- Department of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Uttam Kumar
- Borlaug Institute for South Asia, Ludhiana, India
- International Maize and Wheat Improvement Center, New Delhi, India
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
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27
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Rufo R, López A, Lopes MS, Bellvert J, Soriano JM. Identification of Quantitative Trait Loci Hotspots Affecting Agronomic Traits and High-Throughput Vegetation Indices in Rainfed Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:735192. [PMID: 34616417 PMCID: PMC8489662 DOI: 10.3389/fpls.2021.735192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/20/2021] [Indexed: 06/13/2023]
Abstract
Understanding the genetic basis of agronomic traits is essential for wheat breeding programs to develop new cultivars with enhanced grain yield under climate change conditions. The use of high-throughput phenotyping (HTP) technologies for the assessment of agronomic performance through drought-adaptive traits opens new possibilities in plant breeding. HTP together with a genome-wide association study (GWAS) mapping approach can be a useful method to dissect the genetic control of complex traits in wheat to enhance grain yield under drought stress. This study aimed to identify molecular markers associated with agronomic and remotely sensed vegetation index (VI)-related traits under rainfed conditions in bread wheat and to use an in silico candidate gene (CG) approach to search for upregulated CGs under abiotic stress. The plant material consisted of 170 landraces and 184 modern cultivars from the Mediterranean basin. The collection was phenotyped for agronomic and VI traits derived from multispectral images over 3 and 2 years, respectively. The GWAS identified 2,579 marker-trait associations (MTAs). The quantitative trait loci (QTL) overview index statistic detected 11 QTL hotspots involving more than one trait in at least 2 years. A CG analysis detected 12 CGs upregulated under abiotic stress in six QTL hotspots and 46 downregulated CGs in 10 QTL hotspots. The current study highlights the utility of VI to identify chromosome regions that contribute to yield and drought tolerance under rainfed Mediterranean conditions.
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Affiliation(s)
- Rubén Rufo
- Sustainable Field Crops Programme, Institute for Food and Agricultural Research and Technology (IRTA), Lleida, Spain
| | - Andrea López
- Sustainable Field Crops Programme, Institute for Food and Agricultural Research and Technology (IRTA), Lleida, Spain
| | - Marta S. Lopes
- Sustainable Field Crops Programme, Institute for Food and Agricultural Research and Technology (IRTA), Lleida, Spain
| | - Joaquim Bellvert
- Efficient Use of Water in Agriculture Program, Institute for Food and Agricultural Research and Technology (IRTA), Parc Científici TecnològicAgroalimentari de Gardeny (PCiTAL), Fruitcentre, Lleida, Spain
| | - Jose M. Soriano
- Sustainable Field Crops Programme, Institute for Food and Agricultural Research and Technology (IRTA), Lleida, Spain
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28
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Runcie DE, Qu J, Cheng H, Crawford L. MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits. Genome Biol 2021; 22:213. [PMID: 34301310 PMCID: PMC8299638 DOI: 10.1186/s13059-021-02416-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 06/23/2021] [Indexed: 12/21/2022] Open
Abstract
Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
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Affiliation(s)
- Daniel E. Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Jiayi Qu
- Department of Plant Sciences, University of California Davis, Davis, CA USA
| | - Hao Cheng
- Department of Plant Sciences, University of California Davis, Davis, CA USA
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29
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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-GENES GENOMES GENETICS 2021; 11:6062399. [PMID: 33585867 DOI: 10.1093/g3journal/jkaa050] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [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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30
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Camarillo-Castillo F, Huggins TD, Mondal S, Reynolds MP, Tilley M, Hays DB. High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat. PLANT METHODS 2021; 17:58. [PMID: 34098962 PMCID: PMC8185930 DOI: 10.1186/s13007-021-00759-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 05/26/2021] [Indexed: 05/02/2023]
Abstract
BACKGROUND Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density. RESULTS Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance. CONCLUSIONS Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.
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Affiliation(s)
- Fátima Camarillo-Castillo
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico.
| | - Trevis D Huggins
- USDA ARS, Dale Bumper National Rice Research Center, Stuttgart, AR, 72160, USA
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico
| | - Matthew P Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico, D.F, 06600, Mexico
| | - Michael Tilley
- Agricultural Research Service, Center for Grain and Animal Health Research, USDA, 1515 College Ave., Manhattan, KS, 66502, USA
| | - Dirk B Hays
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77840, USA
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31
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Langridge P, Reynolds M. Breeding for drought and heat tolerance in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1753-1769. [PMID: 33715017 DOI: 10.1007/s00122-021-03795-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/16/2021] [Indexed: 05/02/2023]
Abstract
Many approaches have been adopted to enhance the heat and drought tolerance of wheat with mixed success. An assessment of the relative merits of different strategies is presented. Wheat is the most widely grown crop globally and plays a key role in human nutrition. However, it is grown in environments that are prone to heat and drought stress, resulting in severely reduced yield in some seasons. Increased climate variability is expected to have a particularly adverse effect of wheat production. Breeding for stable yield across both good and bad seasons while maintaining high yield under optimal conditions is a high priority for most wheat breeding programs and has been a focus of research activities. Multiple strategies have been explored to enhance the heat and drought tolerance of wheat including extensive genetic analysis and modify the expression of genes involved in stress responses, targeting specific physiological traits and direct selection under a range of stress scenarios. These approaches have been combined with improvements in phenotyping, the development of genetic and genomic resources, and extended screening and analysis techniques. The results have greatly expanded our knowledge and understanding of the factors that influence yield under stress, but not all have delivered the hoped-for progress. Here, we provide an overview of the different strategies and an assessment of the most promising approaches.
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Affiliation(s)
- Peter Langridge
- School of Agriculture Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia.
- Wheat Initiative, Julius-Kühn-Institute, 14195, Berlin, Germany.
| | - Matthew Reynolds
- International Maize and Wheat Improvement Centre (CIMMYT), Int. AP 6-641, 06600, Mexico, D.F., Mexico
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Reynolds M, Atkin OK, Bennett M, Cooper M, Dodd IC, Foulkes MJ, Frohberg C, Hammer G, Henderson IR, Huang B, Korzun V, McCouch SR, Messina CD, Pogson BJ, Slafer GA, Taylor NL, Wittich PE. Addressing Research Bottlenecks to Crop Productivity. TRENDS IN PLANT SCIENCE 2021; 26:607-630. [PMID: 33893046 DOI: 10.1016/j.tplants.2021.03.011] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 05/22/2023]
Abstract
Asymmetry of investment in crop research leads to knowledge gaps and lost opportunities to accelerate genetic gain through identifying new sources and combinations of traits and alleles. On the basis of consultation with scientists from most major seed companies, we identified several research areas with three common features: (i) relatively underrepresented in the literature; (ii) high probability of boosting productivity in a wide range of crops and environments; and (iii) could be researched in 'precompetitive' space, leveraging previous knowledge, and thereby improving models that guide crop breeding and management decisions. Areas identified included research into hormones, recombination, respiration, roots, and source-sink, which, along with new opportunities in phenomics, genomics, and bioinformatics, make it more feasible to explore crop genetic resources and improve breeding strategies.
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Affiliation(s)
- Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico, El Batan, Texcoco, Mexico.
| | - Owen K Atkin
- Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University Canberra, Acton, ACT 2601, Australia.
| | - Malcolm Bennett
- Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, LE12 5RD, UK.
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ian C Dodd
- The Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
| | - M John Foulkes
- Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, LE12 5RD, UK
| | - Claus Frohberg
- BASF BBC-Innovation Center Gent, Technologiepark-Zwijnaarde 101, 9052 Gent, Belgium
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Ian R Henderson
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK
| | - Bingru Huang
- Department of Plant Biology and Pathology, Rutgers University, 59 Dudley Road, New Brunswick, NJ 08901, USA.
| | | | - Susan R McCouch
- Plant Breeding & Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850, USA.
| | - Carlos D Messina
- Corteva Agriscience, 7250 NW 62nd Avenue, Johnston, IA 50310, USA.
| | - Barry J Pogson
- Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, The Australian National University Canberra, Acton, ACT 2601, Australia
| | - Gustavo A Slafer
- Department of Crop and Forest Sciences, University of Lleida, AGROTECNIO, CERCA Center, Av. R. Roure 191, 25198 Lleida, Spain; ICREA, Catalonian Institution for Research and Advanced Studies, Barcelona, Spain.
| | - Nicolas L Taylor
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences and Institute of Agriculture, The University of Western Australia, Crawley, WA, Australia
| | - Peter E Wittich
- Syngenta Seeds B.V., Westeinde 62, 1601 BK, Enkhuizen, The Netherlands.
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33
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Galán RJ, Bernal-Vasquez AM, Jebsen C, Piepho HP, Thorwarth P, Steffan P, Gordillo A, Miedaner T. Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1409-1422. [PMID: 33630103 PMCID: PMC8081675 DOI: 10.1007/s00122-021-03779-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/19/2021] [Indexed: 05/15/2023]
Abstract
Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
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Affiliation(s)
- Rodrigo José Galán
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | | | | | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany
| | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
- KWS SAAT SE, Grimsehlstraße 31, 37574, Einbeck, Germany
| | - Philipp Steffan
- KWS LOCHOW GMBH, Ferdinand-von-Lochow Straße 5, 29303, Bergen, Germany
| | - Andres Gordillo
- KWS LOCHOW GMBH, Ferdinand-von-Lochow Straße 5, 29303, Bergen, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany.
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34
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Gowda M, Makumbi D, Das B, Nyaga C, Kosgei T, Crossa J, Beyene Y, Montesinos-López OA, Olsen MS, Prasanna BM. Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:941-958. [PMID: 33388884 PMCID: PMC7925482 DOI: 10.1007/s00122-020-03744-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/02/2020] [Indexed: 06/01/2023]
Abstract
KEY MESSAGE Genome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction. Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.
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Affiliation(s)
- Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya.
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Christine Nyaga
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Titus Kosgei
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
- Moi University, P. O. Box 3900-30100, Eldoret, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo, Postal 6-641, 06600, Mexico, D.F, Mexico
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | | | - Michael S Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, 00621, Nairobi, Kenya
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35
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Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives. FORESTS 2020. [DOI: 10.3390/f11111190] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.
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36
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Cortés AJ, López-Hernández F, Osorio-Rodriguez D. Predicting Thermal Adaptation by Looking Into Populations' Genomic Past. Front Genet 2020; 11:564515. [PMID: 33101385 PMCID: PMC7545011 DOI: 10.3389/fgene.2020.564515] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/24/2020] [Indexed: 12/18/2022] Open
Abstract
Molecular evolution offers an insightful theory to interpret the genomic consequences of thermal adaptation to previous events of climate change beyond range shifts. However, disentangling often mixed footprints of selective and demographic processes from those due to lineage sorting, recombination rate variation, and genomic constrains is not trivial. Therefore, here we condense current and historical population genomic tools to study thermal adaptation and outline key developments (genomic prediction, machine learning) that might assist their utilization for improving forecasts of populations' responses to thermal variation. We start by summarizing how recent thermal-driven selective and demographic responses can be inferred by coalescent methods and in turn how quantitative genetic theory offers suitable multi-trait predictions over a few generations via the breeder's equation. We later assume that enough generations have passed as to display genomic signatures of divergent selection to thermal variation and describe how these footprints can be reconstructed using genome-wide association and selection scans or, alternatively, may be used for forward prediction over multiple generations under an infinitesimal genomic prediction model. Finally, we move deeper in time to comprehend the genomic consequences of thermal shifts at an evolutionary time scale by relying on phylogeographic approaches that allow for reticulate evolution and ecological parapatric speciation, and end by envisioning the potential of modern machine learning techniques to better inform long-term predictions. We conclude that foreseeing future thermal adaptive responses requires bridging the multiple spatial scales of historical and predictive environmental change research under modern cohesive approaches such as genomic prediction and machine learning frameworks.
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Affiliation(s)
- Andrés J Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia.,Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Daniela Osorio-Rodriguez
- Division of Geological and Planetary Sciences, California Institute of Technology (Caltech), Pasadena, CA, United States
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37
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Arriagada O, Marcotuli I, Gadaleta A, Schwember AR. Molecular Mapping and Genomics of Grain Yield in Durum Wheat: A Review. Int J Mol Sci 2020; 21:ijms21197021. [PMID: 32987666 PMCID: PMC7582296 DOI: 10.3390/ijms21197021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/14/2020] [Accepted: 09/17/2020] [Indexed: 02/07/2023] Open
Abstract
Durum wheat is the most relevant cereal for the whole of Mediterranean agriculture, due to its intrinsic adaptation to dryland and semi-arid environments and to its strong historical cultivation tradition. It is not only relevant for the primary production sector, but also for the food industry chains associated with it. In Mediterranean environments, wheat is mostly grown under rainfed conditions and the crop is frequently exposed to environmental stresses, with high temperatures and water scarcity especially during the grain filling period. For these reasons, and due to recurrent disease epidemics, Mediterranean wheat productivity often remains under potential levels. Many studies, using both linkage analysis (LA) and a genome-wide association study (GWAS), have identified the genomic regions controlling the grain yield and the associated markers that can be used for marker-assisted selection (MAS) programs. Here, we have summarized all the current studies identifying quantitative trait loci (QTLs) and/or candidate genes involved in the main traits linked to grain yield: kernel weight, number of kernels per spike and number of spikes per unit area.
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Affiliation(s)
- Osvin Arriagada
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, 306-22 Santiago, Chile;
| | - Ilaria Marcotuli
- Department of Agricultural and Environmental Science, University of Bari Aldo Moro, 70121 Bari, Italy; (I.M.); (A.G.)
| | - Agata Gadaleta
- Department of Agricultural and Environmental Science, University of Bari Aldo Moro, 70121 Bari, Italy; (I.M.); (A.G.)
| | - Andrés R. Schwember
- Departamento de Ciencias Vegetales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, 306-22 Santiago, Chile;
- Correspondence: ; Tel.: +56-223544123
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Juliana P, Singh RP, Braun HJ, Huerta-Espino J, Crespo-Herrera L, Govindan V, Mondal S, Poland J, Shrestha S. Genomic Selection for Grain Yield in the CIMMYT Wheat Breeding Program-Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2020; 11:564183. [PMID: 33042185 PMCID: PMC7522222 DOI: 10.3389/fpls.2020.564183] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/27/2020] [Indexed: 06/11/2023]
Abstract
Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in common wheat. In this study, we have evaluated the potential of genomic selection (GS) in breeding for GY in wheat by modeling a large dataset of 48,562 GY observations from the International Maize and Wheat Improvement Center (CIMMYT), including 36 yield trials evaluated between 2012 and 2019 in Obregón, Sonora, Mexico. Our key objective was to determine the value that GS can add to the current three-stage yield testing strategy at CIMMYT, and we draw inferences from predictive modeling of GY using 420 different populations, environments, cycles, and model combinations. First, we evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean prediction accuracies (PAs) of 0.56, 0.5, and 0.42 in Stages 1, 2, and 3 of yield testing, respectively. However, these PAs were similar to the mean pedigree-based PAs indicating that genomic relationships added no value to pedigree relationships in the yield testing stages, characterized by small family-sizes. Second, we evaluated genomic predictions for minimizing GY testing across stages/years in Obregón and observed mean PAs of 0.41, 0.31, and 0.37, respectively when GY in the full irrigation bed planting (FI BP), drought stress (DS), and late-sown heat stress environments were predicted across years using genotype × environment (G × E) interaction models. Third, we evaluated genomic predictions for minimizing the number of yield testing environments and observed that in Stage 2, the FI BP, full irrigation flat planting and early-sown heat stress environments (mean PA of 0.37 ± 0.12) and the reduced irrigation and DS environments (mean PA of 0.45 ± 0.07) had moderate predictabilities among them. However, in both predictions across years and environments, the PAs were inconsistent across years and the G × E models had no advantage over the baseline model with environment and line effects. Overall, our results provide excellent insights into the predictability of a quantitative trait like GY and will have important implications on the future design of GS for GY in wheat breeding programs globally.
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Affiliation(s)
- Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Prakash Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hans-Joachim Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Julio Huerta-Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Chapingo, Mexico
| | | | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Sandesh Shrestha
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
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Using insurance data to quantify the multidimensional impacts of warming temperatures on yield risk. Nat Commun 2020; 11:4542. [PMID: 32917888 PMCID: PMC7486405 DOI: 10.1038/s41467-020-17707-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 06/02/2020] [Indexed: 11/13/2022] Open
Abstract
Previous research predicts significant negative yield impacts from warming temperatures, but estimating the effects on yield risk and disentangling the relative causes of these losses remains challenging. Here we present new evidence on these issues by leveraging a unique publicly available dataset consisting of roughly 30,000 county-by-year observations on insurance-based measures of yield risk from 1989–2014 for U.S. corn and soybeans. Our results suggest that yield risk will increase in response to warmer temperatures, with a 1 °C increase associated with yield risk increases of approximately 32% and 11% for corn and soybeans, respectively. Using cause of loss information, we also find that additional losses under warming temperatures primarily result from additional reported occurrences of drought, with reported losses due to heat stress playing a smaller role. An implication of our findings is that the cost of purchasing crop insurance will increase for producers as a result of warming temperatures. The impacts of climate change on agricultural productivity remain debated. Here, the authors present new evidence for the magnitude and causes of U.S. crop insurance losses, using a database of production risk from 1989–2014 across 1733 counties for corn and 1632 counties for soybeans, and find that crop production risk will increase in response to warmer temperatures.
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Borrenpohl D, Huang M, Olson E, Sneller C. The value of early-stage phenotyping for wheat breeding in the age of genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:2499-2520. [PMID: 32488300 DOI: 10.1007/s00122-020-03613-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 05/15/2020] [Indexed: 06/11/2023]
Abstract
Genomic selection using data from an on-going breeding program can improve gain from selection, relative to phenotypic selection, by significantly increasing the number of lines that can be evaluated. The early stages of phenotyping involve few observations and can be quite inaccurate. Genomic selection (GS) could improve selection accuracy and alter resource allocation. Our objectives were (1) to compare the prediction accuracy of GS and phenotyping in stage-1 and stage-2 field evaluations and (2) to assess the value of stage-1 phenotyping for advancing lines to stage-2 testing. We built training populations from 1769 wheat breeding lines that were genotyped and phenotyped for yield, test weight, Fusarium head blight resistance, heading date, and height. The lines were in cohorts, and analyses were done by cohort. Phenotypes or GS estimated breeding values were used to determine the trait value of stage-1 lines, and these values were correlated with their phenotypes from stage-2 trials. This was repeated for stage-2 to stage-3 trials. The prediction accuracy of GS and phenotypes was similar to each other regardless of the amount (0, 50, 100%) of stage-1 data incorporated in the GS model. Ranking of stage-1 lines by GS predictions that used no stage-1 phenotypic data had marginally lower correspondence to stage-2 phenotypic rankings than rankings of stage-1 lines based on phenotypes. Stage-1 lines ranked high by GS had slightly inferior phenotypes in stage-2 trials than lines ranked high by phenotypes. Cost analysis indicated that replacing stage-1 phenotyping with GS would allow nearly three times more stage-1 candidates to be assessed and provide 0.84-2.23 times greater gain from selection. We conclude that GS can complement or replace phenotyping in early stages of phenotyping.
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Affiliation(s)
- Daniel Borrenpohl
- Department of Horticulture and Crop Science, Ohio Agriculture Research and Development Center, The Ohio State University, 1680 Madison Av, Wooster, OH, 44691, USA
| | - Mao Huang
- Department of Horticulture and Crop Science, Ohio Agriculture Research and Development Center, The Ohio State University, 1680 Madison Av, Wooster, OH, 44691, USA
| | - Eric Olson
- Department of Plant, Soil, and Microbial Science, Michigan State University, 1066 Bogue St, East Lansing, MI, 48824, USA
| | - Clay Sneller
- Department of Horticulture and Crop Science, Ohio Agriculture Research and Development Center, The Ohio State University, 1680 Madison Av, Wooster, OH, 44691, USA.
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UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions. REMOTE SENSING 2020. [DOI: 10.3390/rs12152445] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVIrededge), Simple Ratio (SR), green Chlorophyll Index (CIgreen), and Rededge Chlorophyll Index (CIrededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CIgreen, CIrededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates.
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Genomic Selection in Winter Wheat Breeding Using a Recommender Approach. Genes (Basel) 2020; 11:genes11070779. [PMID: 32664601 PMCID: PMC7397162 DOI: 10.3390/genes11070779] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/06/2020] [Accepted: 07/09/2020] [Indexed: 12/04/2022] Open
Abstract
Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.
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Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, Pequeno DNL, Pinto F, Pinera-Chavez FJ, Poland J, Rivera-Amado C, Saint Pierre C, Sukumaran S. Breeder friendly phenotyping. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2020; 295:110396. [PMID: 32534615 DOI: 10.1016/j.plantsci.2019.110396] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/12/2019] [Accepted: 12/26/2019] [Indexed: 05/18/2023]
Abstract
The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.
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Affiliation(s)
| | - Scott Chapman
- CISRO Agriculture and Food, The University of Queensland, Australia
| | | | - Gemma Molero
- International Maize and Wheat Improvement Centre, Mexico
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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45
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1885-1898. [PMID: 32097472 PMCID: PMC7094083 DOI: 10.1093/jxb/erz545] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 02/19/2020] [Indexed: 05/08/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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46
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020. [PMID: 32097472 DOI: 10.17632/pkxpkw6j43.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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47
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Baba T, Momen M, Campbell MT, Walia H, Morota G. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. PLoS One 2020; 15:e0228118. [PMID: 32012182 PMCID: PMC6996807 DOI: 10.1371/journal.pone.0228118] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 01/07/2020] [Indexed: 12/28/2022] Open
Abstract
Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Weak to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.870.38-0.80). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.
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Affiliation(s)
- Toshimi Baba
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
| | - Mehdi Momen
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
| | - Malachy T. Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States of America
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
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48
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Biswas DK, Coulman B, Biligetu B, Fu YB. Advancing Bromegrass Breeding Through Imaging Phenotyping and Genomic Selection: A Review. FRONTIERS IN PLANT SCIENCE 2020; 10:1673. [PMID: 32010160 PMCID: PMC6974688 DOI: 10.3389/fpls.2019.01673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 11/28/2019] [Indexed: 05/24/2023]
Abstract
Breeding forage crops for high yields of digestible biomass along with improved resource-use efficiency and wide adaptation is essential to meet future challenges in forage production imposed by growing demand, declining resources, and changing climate. Bromegrasses (Bromus spp.) are economically important forage species in the temperate regions of world, but genetic gain in forage yield of bromegrass is relatively low. In particular, limited breeding efforts have been made in improving abiotic stress tolerance and resource-use efficiency. We conducted a literature review on bromegrass breeding achievements and challenges, global climate change impacts on bromegrass species, and explored the feasibility of applying high-throughput imaging phenotyping techniques and genomic selection for further advances in forage yield and quality selection. Overall genetic gain in forage yield of bromegrass has been low, but genetic improvement in forage yield of smooth bromegrass (Bromus inermis Leyss) is somewhat higher than that of meadow bromegrass (Bromus riparius Rehm). This low genetic gain in bromegrass yield is due to a few factors such as its genetic complexity, lack of long-term breeding effort, and inadequate plant adaptation to changing climate. Studies examining the impacts of global climate change on bromegrass species show that global warming, heat stress, and drought have negative effects on forage yield. A number of useful physiological traits have been identified for genetic improvement to minimize yield loss. Available reports suggest that high-throughput imaging phenotyping techniques, including visual and infrared thermal imaging, imaging hyperspectral spectroscopy, and imaging chlorophyll fluorescence, are capable of capturing images of morphological, physiological, and biochemical traits related to plant growth, yield, and adaptation to abiotic stresses at different scales of organization. The more complex traits such as photosynthetic radiation-use efficiency, water-use efficiency, and nitrogen-use efficiency can be effectively assessed by utilizing combinations of imaging hyperspectral spectroscopy, infrared thermal imaging, and imaging chlorophyll fluorescence techniques in a breeding program. Genomic selection has been applied in the breeding of forage species and the applications show its potential in high ploidy, outcrossing species like bromegrass to improve the accuracy of parental selection and improve genetic gain. Together, these new technologies hold promise for improved genetic gain and wide adaptation in future bromegrass breeding.
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Affiliation(s)
- Dilip K. Biswas
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Bruce Coulman
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Bill Biligetu
- Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Yong-Bi Fu
- Plant Gene Resources of Canada, Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
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Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments. G3-GENES GENOMES GENETICS 2019; 9:2925-2934. [PMID: 31300481 PMCID: PMC6723131 DOI: 10.1534/g3.119.400508] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.
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High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture. AGRONOMY-BASEL 2019. [DOI: 10.3390/agronomy9050258] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.
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