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Tong K, Chen X, Yan S, Dai L, Liao Y, Li Z, Wang T. PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics. Genes (Basel) 2024; 15:603. [PMID: 38790232 PMCID: PMC11120712 DOI: 10.3390/genes15050603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/05/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.
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Affiliation(s)
- Kai Tong
- School of Biological Engineering, Sichuan University of Science & Engineering, Yibin 644000, China; (K.T.); (L.D.); (Y.L.)
| | - Xiaojing Chen
- National Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
- National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, China
| | - Shen Yan
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
| | - Liangli Dai
- School of Biological Engineering, Sichuan University of Science & Engineering, Yibin 644000, China; (K.T.); (L.D.); (Y.L.)
| | - Yuxue Liao
- School of Biological Engineering, Sichuan University of Science & Engineering, Yibin 644000, China; (K.T.); (L.D.); (Y.L.)
| | - Zhaoling Li
- School of Biological Engineering, Sichuan University of Science & Engineering, Yibin 644000, China; (K.T.); (L.D.); (Y.L.)
| | - Ting Wang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Big Agri-Data, Ministry of Agriculture and Rural Areas, Beijing 100081, China
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2
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Sadeqi MB, Ballvora A, Dadshani S, Léon J. Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat. Int J Mol Sci 2023; 24:14275. [PMID: 37762585 PMCID: PMC10531695 DOI: 10.3390/ijms241814275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias-variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models.
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Affiliation(s)
- Mohammad Bahman Sadeqi
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
| | - Agim Ballvora
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
| | - Said Dadshani
- INRES-Plant Nutrition, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany;
| | - Jens Léon
- INRES-Plant Breeding, Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany; (M.B.S.); (J.L.)
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3
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Atta K, Mondal S, Gorai S, Singh AP, Kumari A, Ghosh T, Roy A, Hembram S, Gaikwad DJ, Mondal S, Bhattacharya S, Jha UC, Jespersen D. Impacts of salinity stress on crop plants: improving salt tolerance through genetic and molecular dissection. FRONTIERS IN PLANT SCIENCE 2023; 14:1241736. [PMID: 37780527 PMCID: PMC10540871 DOI: 10.3389/fpls.2023.1241736] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/14/2023] [Indexed: 10/03/2023]
Abstract
Improper use of water resources in irrigation that contain a significant amount of salts, faulty agronomic practices such as improper fertilization, climate change etc. are gradually increasing soil salinity of arable lands across the globe. It is one of the major abiotic factors that inhibits overall plant growth through ionic imbalance, osmotic stress, oxidative stress, and reduced nutrient uptake. Plants have evolved with several adaptation strategies at morphological and molecular levels to withstand salinity stress. Among various approaches, harnessing the crop genetic variability across different genepools and developing salinity tolerant crop plants offer the most sustainable way of salt stress mitigation. Some important major genetic determinants controlling salinity tolerance have been uncovered using classical genetic approaches. However, its complex inheritance pattern makes breeding for salinity tolerance challenging. Subsequently, advances in sequence based breeding approaches and functional genomics have greatly assisted in underpinning novel genetic variants controlling salinity tolerance in plants at the whole genome level. This current review aims to shed light on physiological, biochemical, and molecular responses under salt stress, defense mechanisms of plants, underlying genetics of salt tolerance through bi-parental QTL mapping and Genome Wide Association Studies, and implication of Genomic Selection to breed salt tolerant lines.
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Affiliation(s)
- Kousik Atta
- ICAR-Indian Agricultural Research Institute, New Delhi, India
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Saptarshi Mondal
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - Shouvik Gorai
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Aditya Pratap Singh
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
- School of Agriculture, GIET University, Gunupur, Rayagada, Odisha, India
| | - Amrita Kumari
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Tuhina Ghosh
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Arkaprava Roy
- ICAR-Indian Agricultural Research Institute, New Delhi, India
- ICAR- National Institute of Biotic Stress Management, Raipur, India
| | - Suryakant Hembram
- WBAS (Research), Government of West Bengal, Field Crop Research Station, Burdwan, India
| | | | - Subhasis Mondal
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | | | | | - David Jespersen
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
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Yan Q, Fruzangohar M, Taylor J, Gong D, Walter J, Norman A, Shi JQ, Coram T. Improved genomic prediction using machine learning with Variational Bayesian sparsity. PLANT METHODS 2023; 19:96. [PMID: 37660084 PMCID: PMC10474716 DOI: 10.1186/s13007-023-01073-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 08/22/2023] [Indexed: 09/04/2023]
Abstract
BACKGROUND Genomic prediction has become a powerful modelling tool for assessing line performance in plant and livestock breeding programmes. Among the genomic prediction modelling approaches, linear based models have proven to provide accurate predictions even when the number of genetic markers exceeds the number of data samples. However, breeding programmes are now compiling data from large numbers of lines and test environments for analyses, rendering these approaches computationally prohibitive. Machine learning (ML) now offers a solution to this problem through the construction of fully connected deep learning architectures and high parallelisation of the predictive task. However, the fully connected nature of these architectures immediately generates an over-parameterisation of the network that needs addressing for efficient and accurate predictions. RESULTS In this research we explore the use of an ML architecture governed by variational Bayesian sparsity in its initial layers that we have called VBS-ML. The use of VBS-ML provides a mechanism for feature selection of important markers linked to the trait, immediately reducing the network over-parameterisation. Selected markers then propagate to the remaining fully connected feed-forward components of the ML network to form the final genomic prediction. We illustrated the approach with four large Australian wheat breeding data sets that range from 2665 lines to 10375 lines genotyped across a large set of markers. For all data sets, the use of the VBS-ML architecture improved genomic prediction accuracy over legacy linear based modelling approaches. CONCLUSIONS An ML architecture governed under a variational Bayesian paradigm was shown to improve genomic prediction accuracy over legacy modelling approaches. This VBS-ML approach can be used to dramatically decrease the parameter burden on the network and provide a computationally feasible approach for improving genomic prediction conducted with large breeding population numbers and genetic markers.
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Affiliation(s)
- Qingsen Yan
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Mario Fruzangohar
- School of Food, Agriculture and Wine, University of Adelaide, Adelaide, Australia
| | - Julian Taylor
- School of Food, Agriculture and Wine, University of Adelaide, Adelaide, Australia
| | - Dong Gong
- School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia
| | - James Walter
- Australian Grains Technologies, Roseworthy, Australia
| | - Adam Norman
- Australian Grains Technologies, Roseworthy, Australia
| | - Javen Qinfeng Shi
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Tristan Coram
- Australian Grains Technologies, Roseworthy, Australia
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Bhat JA, Feng X, Mir ZA, Raina A, Siddique KHM. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding. PHYSIOLOGIA PLANTARUM 2023; 175:e13969. [PMID: 37401892 DOI: 10.1111/ppl.13969] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/11/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.
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Affiliation(s)
| | - Xianzhong Feng
- Zhejiang Lab, Hangzhou, China
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Zahoor A Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Aamir Raina
- Department of Botany, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, Western Australia, Australia
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6
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Hashem M, Sandhu KS, Ismail SM, Börner A, Sallam A. Validation and marker-assisted selection of DArT-genomic regions associated with wheat yield-related traits under normal and drought conditions. Front Genet 2023; 14:1195566. [PMID: 37292145 PMCID: PMC10245129 DOI: 10.3389/fgene.2023.1195566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/10/2023] [Indexed: 06/10/2023] Open
Abstract
Quantitative trait loci (QTL) is one of the most important steps in marker-assisted selection. Few studies have validated quantitative trait loci for marker-assisted selection of yield traits under drought stress conditions in wheat. A set of 138 highly diverse wheat genotypes were tested under normal and drought stress conditions for 2 years. Plant height, heading date, spike length, grain number per spike, grain yield per spike, and 1000-kernel weight were scored. High genetic variation was found among genotypes in all traits scored under both conditions in the 2 years. The same panel was genotyped using a diversity-array technology (DArT) marker, and a genome-wide association study was performed to find alleles associated with yield traits under all conditions. A set of 191 significant DArT markers were identified in this study. The results of the genome-wide association study revealed eight common markers in wheat that were significantly associated with the same traits under both conditions in the 2 years. Out of the eight markers, seven were located on the D genome except one marker. Four validated markers were located on the 3D chromosome and found in complete linkage disequilibrium. Moreover, these four markers were significantly associated with the heading date under both conditions and the grain yield per spike under drought stress condition in the 2 years. This high-linkage disequilibrium genomic region was located within the TraesCS3D02G002400 gene model. Furthermore, of the eight validated markers, seven were previously reported to be associated with yield traits under normal and drought conditions. The results of this study provided very promising DArT markers that can be used for marker-assisted selection to genetically improve yield traits under normal and drought conditions.
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Affiliation(s)
- Mostafa Hashem
- Department of Genetics, Faculty of Agriculture, Assiut University, Assuit, Egypt
| | | | - Saleh M. Ismail
- Soils and Water Department, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Andreas Börner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Ahmed Sallam
- Department of Genetics, Faculty of Agriculture, Assiut University, Assuit, Egypt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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7
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Olgun M, Karaduman Y, Belen S, Akin A, Yalçin M, Başçiftçi ZB, Arpacioğlu NGA, Erkara IP, Sezer O, Ardiç M. Investigation of climate-quality relationship in bread wheat (T. aestivum L.) by novel statistical approach (ESOGÜ quality index). ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:533. [PMID: 37010642 DOI: 10.1007/s10661-023-11108-w] [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: 05/18/2022] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
In this study, precipitation; temperature (maximum, minimum, and average temperature) values of Eskişehir, Konya, Afyonkarahisar, Uşak, and Kütahya for years (2007-2018); and protein content, macro sedimentation (MSDS), thousand kernel weight (KW), test weight (TW) relations, and the effect of climate values on quality were investigated. The Kriging method was used by ArcGIS software for creating quality maps of Eskişehir, Konya, Afyonkarahisar, Uşak, and Kütahya provinces in the light of obtained data from these examined quality criteria, yield, and climate factors. The quality of bread wheat, which includes protein content, macro sedimentation, thousand kernel weight, and test weight, is highly affected by the subject precipitation, maximum temperature, minimum temperature, average temperature, and precipitation. While the months of November, March, and April and the total annual precipitations affect the quality, the most effective precipitation is the months of April and November. Again, the fact that the winter months are hot, especially in January and February, causes the plant to be inadequate to withstand the winter, causing the plant to be more affected by the low temperatures in the early spring and to reduce the quality due to insufficient plant growth. Climatic factors affect quality in total, not alone, but cumulatively. It was concluded that the best quality wheat can be obtained from Konya, Eskişehir, and Afyonkarahisar provinces. It was concluded that ESOGÜ quality index (EQI), evaluating and integrating protein content, macro sedimentation, thousand kernel weight, and test weigh together, can be used safely in bread wheat genotypes.
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Affiliation(s)
- Murat Olgun
- Field Crop Department, Agricultural Faculty, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Yaşar Karaduman
- Field Crop Department, Agricultural Faculty, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Savaş Belen
- Transitional Zone Agricultural Research Institute, Eskişehir, Turkey
| | - Arzu Akin
- Transitional Zone Agricultural Research Institute, Eskişehir, Turkey
| | - Mustafa Yalçin
- Geomatic Engineering Department, Faculty of Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Zekiye Budak Başçiftçi
- Field Crop Department, Agricultural Faculty, Eskişehir Osmangazi University, Eskişehir, Turkey
| | | | - Ismühan Potoğlu Erkara
- Department of Biology, Faculty of Science, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Okan Sezer
- Department of Biology, Faculty of Science, Eskişehir Osmangazi University, Eskişehir, Turkey.
| | - Murat Ardiç
- Department of Biology, Faculty of Science, Eskişehir Osmangazi University, Eskişehir, Turkey
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8
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Bisht A, Saini DK, Kaur B, Batra R, Kaur S, Kaur I, Jindal S, Malik P, Sandhu PK, Kaur A, Gill BS, Wani SH, Kaur B, Mir RR, Sandhu KS, Siddique KHM. Multi-omics assisted breeding for biotic stress resistance in soybean. Mol Biol Rep 2023; 50:3787-3814. [PMID: 36692674 DOI: 10.1007/s11033-023-08260-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 01/09/2023] [Indexed: 01/25/2023]
Abstract
Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.
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Affiliation(s)
- Ashita Bisht
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
- CSK Himachal Pradesh Krishi Vishvavidyalaya, Highland Agricultural Research and Extension Centre, 175142, Kukumseri, Lahaul and Spiti, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India.
| | - Baljeet Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
| | - Ritu Batra
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, 25004, Meerut, India
| | - Sandeep Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
| | - Ishveen Kaur
- Agriculture, Environmental and Sustainability Sciences, College of sciences, University of Texas Rio Grande Valley, 78539, Edinburg, TX, USA
| | - Suruchi Jindal
- Division of Molecular Biology and Genetic Engineering, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India
| | - Palvi Malik
- , Gurdev Singh Khush Institute of Genetics, Plant Breeding and Biotechnology, Punjab Agricultural University,, 141004, Ludhiana, India
| | - Pawanjit Kaur Sandhu
- Department of Chemistry, University of British Columbia, V1V 1V7, Okanagan, Kelowna, Canada
| | - Amandeep Kaur
- Division of Molecular Biology and Genetic Engineering, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India
| | - Balwinder Singh Gill
- Department of Plant Breeding and Genetics, Punjab Agricultural University, 141004, Ludhiana, India
| | - Shabir Hussain Wani
- MRCFC Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir, Shalimar, India
| | - Balwinder Kaur
- Department of Entomology, UF/IFAS Research and Education Center, 33430, Belle Glade, Florida, USA
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, 193201, India
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, 99163, Pullman, WA, USA.
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, 6001, Perth, WA, Australia.
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Yoosefzadeh Najafabadi M, Hesami M, Eskandari M. Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs. Genes (Basel) 2023; 14:genes14040777. [PMID: 37107535 PMCID: PMC10137951 DOI: 10.3390/genes14040777] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/11/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.
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Affiliation(s)
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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10
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Kumar M, Kumar S, Sandhu KS, Kumar N, Saripalli G, Prakash R, Nambardar A, Sharma H, Gautam T, Balyan HS, Gupta PK. GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:14. [PMID: 37313293 PMCID: PMC10248620 DOI: 10.1007/s11032-023-01357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 06/15/2023]
Abstract
In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01357-5.
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Affiliation(s)
- Manoj Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | | | - Neeraj Kumar
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC USA
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD USA
| | - Ram Prakash
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Akash Nambardar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Hemant Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Tinku Gautam
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
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11
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Singh J, Chhabra B, Raza A, Yang SH, Sandhu KS. Important wheat diseases in the US and their management in the 21st century. FRONTIERS IN PLANT SCIENCE 2023; 13:1010191. [PMID: 36714765 PMCID: PMC9877539 DOI: 10.3389/fpls.2022.1010191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/28/2022] [Indexed: 05/27/2023]
Abstract
Wheat is a crop of historical significance, as it marks the turning point of human civilization 10,000 years ago with its domestication. Due to the rapid increase in population, wheat production needs to be increased by 50% by 2050 and this growth will be mainly based on yield increases, as there is strong competition for scarce productive arable land from other sectors. This increasing demand can be further achieved using sustainable approaches including integrated disease pest management, adaption to warmer climates, less use of water resources and increased frequency of abiotic stress tolerances. Out of 200 diseases of wheat, 50 cause economic losses and are widely distributed. Each year, about 20% of wheat is lost due to diseases. Some major wheat diseases are rusts, smut, tan spot, spot blotch, fusarium head blight, common root rot, septoria blotch, powdery mildew, blast, and several viral, nematode, and bacterial diseases. These diseases badly impact the yield and cause mortality of the plants. This review focuses on important diseases of the wheat present in the United States, with comprehensive information of causal organism, economic damage, symptoms and host range, favorable conditions, and disease management strategies. Furthermore, major genetic and breeding efforts to control and manage these diseases are discussed. A detailed description of all the QTLs, genes reported and cloned for these diseases are provided in this review. This study will be of utmost importance to wheat breeding programs throughout the world to breed for resistance under changing environmental conditions.
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Affiliation(s)
- Jagdeep Singh
- Department of Crop, Soil & Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Bhavit Chhabra
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, United States
| | - Ali Raza
- College of Agriculture, Oil Crops Research Institute, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Seung Hwan Yang
- Department of Integrative Biotechnology, Chonnam National University, Yeosu, Republic of Korea
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12
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Jubair S, Domaratzki M. Crop genomic selection with deep learning and environmental data: A survey. Front Artif Intell 2023; 5:1040295. [PMID: 36703955 PMCID: PMC9871498 DOI: 10.3389/frai.2022.1040295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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Affiliation(s)
- Sheikh Jubair
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada,*Correspondence: Sheikh Jubair ✉
| | - Mike Domaratzki
- Department of Computer Science, University of Western Ontario, London, ON, Canada
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13
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Wang K, Abid MA, Rasheed A, Crossa J, Hearne S, Li H. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants. MOLECULAR PLANT 2023; 16:279-293. [PMID: 36366781 DOI: 10.1016/j.molp.2022.11.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/28/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms.
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Affiliation(s)
- Kelin Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT - China Office, 12 Zhongguancun South Street, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | | | - Awais Rasheed
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT - China Office, 12 Zhongguancun South Street, Beijing 100081, China; Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Texcoco, D.F. 06600, Mexico
| | - Sarah Hearne
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Texcoco, D.F. 06600, Mexico
| | - Huihui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT - China Office, 12 Zhongguancun South Street, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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14
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Zhang J, Wang X, Liu J, Zhang D, Lu Y, Zhou Y, Sun L, Hou S, Fan X, Shen S, Zhao J. Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:0007. [PMID: 37266137 PMCID: PMC10230957 DOI: 10.34133/plantphenomics.0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 11/07/2022] [Indexed: 06/03/2023]
Abstract
The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage (Brassica rapa L. ssp. pekinensis) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. The R2 of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, the R2 of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.
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Affiliation(s)
- Jun Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Xinxin Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- Mountain Area Research Institute, Hebei Agricultural University, 071001 Baoding, China
| | - Jingyan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Dongfang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yin Lu
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yuhong Zhou
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Lei Sun
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Shenglin Hou
- Hebei Academy of Agriculture and Forestry Sciences, 050000 Shijiazhuang, China
| | - Xiaofei Fan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Shuxing Shen
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Jianjun Zhao
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
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15
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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16
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John M, Haselbeck F, Dass R, Malisi C, Ricca P, Dreischer C, Schultheiss SJ, Grimm DG. A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species. FRONTIERS IN PLANT SCIENCE 2022; 13:932512. [PMID: 36407627 PMCID: PMC9673477 DOI: 10.3389/fpls.2022.932512] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/25/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
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Affiliation(s)
- Maura John
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany
| | - Florian Haselbeck
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany
| | | | | | | | | | | | - Dominik G. Grimm
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany
- Technical University of Munich, Department of Informatics, Garching, Germany
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17
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Kim KW, Nawade B, Nam J, Chu SH, Ha J, Park YJ. Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice. FRONTIERS IN PLANT SCIENCE 2022; 13:1036177. [PMID: 36352876 PMCID: PMC9637963 DOI: 10.3389/fpls.2022.1036177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world's population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder's interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, -log10 p-value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.
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Affiliation(s)
- Kyu-Won Kim
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Bhagwat Nawade
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Jungrye Nam
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Sang-Ho Chu
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Jungmin Ha
- Department of Plant Science, Gangneung-Wonju National University, Gangneung, South Korea
| | - Yong-Jin Park
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan, South Korea
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18
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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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19
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Sandhu KS, Shiv A, Kaur G, Meena MR, Raja AK, Vengavasi K, Mall AK, Kumar S, Singh PK, Singh J, Hemaprabha G, Pathak AD, Krishnappa G, Kumar S. Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane. PLANTS 2022; 11:plants11162139. [PMID: 36015442 PMCID: PMC9412483 DOI: 10.3390/plants11162139] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/08/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022]
Abstract
Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.
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Affiliation(s)
- Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163, USA
| | - Aalok Shiv
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - Mintu Ram Meena
- Regional Center, ICAR-Sugarcane Breeding Institute, Karnal 132001, India
| | - Arun Kumar Raja
- Division of Crop Production, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
| | - Krishnapriya Vengavasi
- Division of Crop Production, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
| | - Ashutosh Kumar Mall
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Sanjeev Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Praveen Kumar Singh
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Jyotsnendra Singh
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Govind Hemaprabha
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
| | - Ashwini Dutt Pathak
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
| | - Gopalareddy Krishnappa
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore 641007, India
- Correspondence: (G.K.); (S.K.)
| | - Sanjeev Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow 226002, India
- Correspondence: (G.K.); (S.K.)
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20
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Zhang Z, Pope M, Shakoor N, Pless R, Mockler TC, Stylianou A. Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum. Front Artif Intell 2022; 5:872858. [PMID: 35860344 PMCID: PMC9289439 DOI: 10.3389/frai.2022.872858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 06/09/2022] [Indexed: 11/13/2022] Open
Abstract
We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.
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Affiliation(s)
- Zeyu Zhang
- Department of Computer Science, George Washington University, Washington, DC, United States
| | - Madison Pope
- Department of Computer Science, Saint Louis University, Saint Louis, MO, United States
| | - Nadia Shakoor
- Donald Danforth Plant Science Center, Mockler Lab, Saint Louis, MO, United States
| | - Robert Pless
- Department of Computer Science, George Washington University, Washington, DC, United States
| | - Todd C. Mockler
- Donald Danforth Plant Science Center, Mockler Lab, Saint Louis, MO, United States
| | - Abby Stylianou
- Department of Computer Science, Saint Louis University, Saint Louis, MO, United States
- *Correspondence: Abby Stylianou
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21
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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22
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Danilevicz MF, Gill M, Anderson R, Batley J, Bennamoun M, Bayer PE, Edwards D. Plant Genotype to Phenotype Prediction Using Machine Learning. Front Genet 2022; 13:822173. [PMID: 35664329 PMCID: PMC9159391 DOI: 10.3389/fgene.2022.822173] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
- *Correspondence: David Edwards,
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Sun Z, Li Q, Jin S, Song Y, Xu S, Wang X, Cai J, Zhou Q, Ge Y, Zhang R, Zang J, Jiang D. Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing. PLANT PHENOMICS 2022; 2022:9757948. [PMID: 35441150 PMCID: PMC8988204 DOI: 10.34133/2022/9757948] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/07/2022] [Indexed: 01/14/2023]
Abstract
Wheat yield and grain protein content (GPC) are two main optimization targets for breeding and cultivation. Remote sensing provides nondestructive and early predictions of yield and GPC, respectively. However, whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear. In this study, we made a systematic comparison of different deep learning models in terms of data fusion, time-series feature extraction, and multitask learning. The results showed that time-series data fusion significantly improved yield and GPC prediction accuracy with R2 values of 0.817 and 0.809. Multitask learning achieved simultaneous prediction of yield and GPC with comparable accuracy to the single-task model. We further proposed a two-to-two model that combines data fusion (two kinds of data sources for input) and multitask learning (two outputs) and compared different feature extraction layers, including RNN (recurrent neural network), LSTM (long short-term memory), CNN (convolutional neural network), and attention module. The two-to-two model with the attention module achieved the best prediction accuracy for yield (R2 = 0.833) and GPC (R2 = 0.846). The temporal distribution of feature importance was visualized based on the attention feature values. Although the temporal patterns of structural traits and spectral traits were inconsistent, the overall importance of both structural traits and spectral traits at the postanthesis stage was more important than that at the preanthesis stage. This study provides new insights into the simultaneous prediction of yield and GPC using deep learning from time-series proximal sensing, which may contribute to the accurate and efficient predictions of agricultural production.
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Affiliation(s)
- Zhuangzhuang Sun
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Qing Li
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Yunlin Song
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiao Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Jian Cai
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Qin Zhou
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Ruinan Zhang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Jingrong Zang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Regional Technique Innovation Center for Wheat Production, Ministry of Agriculture, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
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Nguyen Ba AN, Lawrence KR, Rego-Costa A, Gopalakrishnan S, Temko D, Michor F, Desai MM. Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast. eLife 2022; 11:73983. [PMID: 35147078 PMCID: PMC8979589 DOI: 10.7554/elife.73983] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/11/2022] [Indexed: 11/25/2022] Open
Abstract
Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.
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Affiliation(s)
- Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | - Artur Rego-Costa
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | | | | | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University
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25
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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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26
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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: 14] [Impact Index Per Article: 7.0] [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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27
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Paux E, Lafarge S, Balfourier F, Derory J, Charmet G, Alaux M, Perchet G, Bondoux M, Baret F, Barillot R, Ravel C, Sourdille P, Le Gouis J. Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection. BIOLOGY 2022; 11:149. [PMID: 35053148 PMCID: PMC8773325 DOI: 10.3390/biology11010149] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 12/21/2022]
Abstract
There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker-trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.
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Affiliation(s)
- Etienne Paux
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Stéphane Lafarge
- Limagrain, Chappes Research Center, Route d’Ennezat, 63720 Chappes, France; (S.L.); (J.D.)
| | - François Balfourier
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Jérémy Derory
- Limagrain, Chappes Research Center, Route d’Ennezat, 63720 Chappes, France; (S.L.); (J.D.)
| | - Gilles Charmet
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Michael Alaux
- Université Paris-Saclay—INRAE, URGI, 78026 Versailles, France;
- Université Paris-Saclay—INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026 Versailles, France
| | - Geoffrey Perchet
- Vegepolys Valley, Maison du Végétal, 26 Rue Jean Dixmeras, 49066 Angers, France;
| | - Marion Bondoux
- INRAE—Transfert, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France;
| | - Frédéric Baret
- UMR EMMAH, INRAE—Université d’Avignon et des Pays de Vaucluse, 84914 Avignon, France;
| | | | - Catherine Ravel
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Pierre Sourdille
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
| | - Jacques Le Gouis
- UMR GDEC Genetics, Diversity & Ecophysiology of Cereals, INRAE—Université Clermont-Auvergne, 5, Chemin de Beaulieu, 63000 Clermont-Ferrand, France; (E.P.); (F.B.); (G.C.); (C.R.); (P.S.)
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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Gardiner LJ, Krishna R. Bluster or Lustre: Can AI Improve Crops and Plant Health? PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122707. [PMID: 34961177 PMCID: PMC8707749 DOI: 10.3390/plants10122707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/24/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.
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30
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Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa ( Medicago sativa L.). Cells 2021; 10:cells10123372. [PMID: 34943880 PMCID: PMC8699225 DOI: 10.3390/cells10123372] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 12/27/2022] Open
Abstract
Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.
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Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning. SUSTAINABILITY 2021. [DOI: 10.3390/su132212613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).
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32
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Aoun M, Carter A, Thompson YA, Ward B, Morris CF. Environment characterization and genomic prediction for end-use quality traits in soft white winter wheat. THE PLANT GENOME 2021; 14:e20128. [PMID: 34396703 DOI: 10.1002/tpg2.20128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/08/2021] [Indexed: 06/13/2023]
Abstract
End-use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end-use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi-environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end-use quality traits. Genetic correlations between environments based on factor analytic models showed low-to-moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single-nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end-use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1-11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.
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Affiliation(s)
- Meriem Aoun
- Dep. of Crop and Soil Sciences, Washington State Univ., Pullman, WA, 99164, USA
| | - Arron Carter
- Dep. of Crop and Soil Sciences, Washington State Univ., Pullman, WA, 99164, USA
| | - Yvonne A Thompson
- USDA-ARS Western Wheat & Pulse Quality Laboratory, Washington State Univ., Pullman, WA, 99164, USA
| | - Brian Ward
- USDA-ARS Plant Science Research Campus, Raleigh, NC, 27695, USA
- Dep. of Horticulture and Crop Science, Ohio State University, Wooster, OH, 44691, USA
| | - Craig F Morris
- USDA-ARS Western Wheat & Pulse Quality Laboratory, Washington State Univ., Pullman, WA, 99164, USA
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Sandhu K, Patil SS, Pumphrey M, Carter A. Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program. THE PLANT GENOME 2021; 14:e20119. [PMID: 34482627 DOI: 10.1002/tpg2.20119] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/18/2021] [Indexed: 06/13/2023]
Abstract
Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine- and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine- and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and -0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine- and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine- and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine- and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.
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Affiliation(s)
- Karansher Sandhu
- Department of Crop and Soil Sciences, WA State University, Pullman, WA, 99164, USA
| | - Shruti Sunil Patil
- School of Electrical Engineering and Computer Science, WA State University, Pullman, WA, 99164, USA
| | - Michael Pumphrey
- Department of Crop and Soil Sciences, WA State University, Pullman, WA, 99164, USA
| | - Arron Carter
- Department of Crop and Soil Sciences, WA State University, Pullman, WA, 99164, USA
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Singh J, Aggarwal R, Bashyal BM, Darshan K, Parmar P, Saharan MS, Hussain Z, Solanke AU. Transcriptome Reprogramming of Tomato Orchestrate the Hormone Signaling Network of Systemic Resistance Induced by Chaetomium globosum. FRONTIERS IN PLANT SCIENCE 2021; 12:721193. [PMID: 34630468 PMCID: PMC8495223 DOI: 10.3389/fpls.2021.721193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 08/16/2021] [Indexed: 06/12/2023]
Abstract
Chaetomium globosum is a potential biological control agent effective against various plant pathogens. Several reports are available on the mycoparastism and antibiosis mechanisms of C. globosum against plant pathogenic fungi, whereas a few states induced resistance. The potential induced defense component of C. globosum (Cg-2) was evaluated against early blight disease of tomato (Solanum lycopersicum) and further, global RNA sequencing was performed to gain deep insight into its mechanism. The expression of marker genes of hormone signaling pathways, such as PR1, PiII, PS, PAL, Le4, and GluB were analyzed using real-time quantitative reverse transcription PCR (qRT-PCR) to determine the best time point for RNA sequencing. The transcriptome data revealed that 22,473 differentially expressed genes (DEGs) were expressed in tomato at 12 h post Cg-2 inoculation as compared with control plants and among these 922 DEGs had a fold change of -2 to +2 with p < 0.05. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that most of the DEGs were belonging to metabolic pathways, biosynthesis of secondary metabolites, plant-pathogen interaction, chlorophyll metabolism, and plant hormone signal transduction. Gene Ontology (GO) analysis revealed that DEGs were enriched mainly related to binding activity (GO:0005488), catalytic activity (GO:0003824), metabolic process (GO:0008152), cellular process (GO:0009987), response to stimulus (GO:0050896), biological regulation (GO:0065007), and transcription regulator activity (GO:0140110). The gene modulations in hormone signaling transduction, phenylpropanoid biosynthesis, and mitogen-activated protein kinases (MPK) signaling indicated the upregulation of genes in these pathways. The results revealed active participation of jasmonic acid (JA) and salicylic acid (SA) signaling transduction pathways which further indicated the involvement of induced systemic resistance (ISR) and systemic acquired resistance (SAR) in the systemic resistance induced by Cg-2 in tomato.
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Affiliation(s)
- Jagmohan Singh
- Fungal Molecular Biology Laboratory, Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Rashmi Aggarwal
- Fungal Molecular Biology Laboratory, Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Bishnu Maya Bashyal
- Fungal Molecular Biology Laboratory, Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - K Darshan
- Forest Protection Division, IC FRE-Tropical Forest Research Institute, Jabalpur, India
| | - Pooja Parmar
- Fungal Molecular Biology Laboratory, Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - M S Saharan
- Fungal Molecular Biology Laboratory, Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Zakir Hussain
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Amolkumar U Solanke
- ICAR-National Institute for Plant Biotechnology, ICAR-IARI, New Delhi, India
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Kaur B, Sandhu KS, Kamal R, Kaur K, Singh J, Röder MS, Muqaddasi QH. Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects. PLANTS 2021; 10:plants10101989. [PMID: 34685799 PMCID: PMC8541486 DOI: 10.3390/plants10101989] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 12/22/2022]
Abstract
Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.
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Affiliation(s)
- Balwinder Kaur
- Everglades Research and Education Center, University of Florida, 3200 E. Palm Beach Rd., Belle Glade, FL 33430, USA;
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163, USA;
| | - Roop Kamal
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
| | - Kawalpreet Kaur
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada;
| | - Jagmohan Singh
- Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India;
| | - Marion S. Röder
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
| | - Quddoos H. Muqaddasi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Stadt Seeland, Germany; (R.K.); or (M.S.R.)
- Correspondence: or
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Sandhu KS, Aoun M, Morris CF, Carter AH. Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models. BIOLOGY 2021; 10:689. [PMID: 34356544 PMCID: PMC8301459 DOI: 10.3390/biology10070689] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/13/2021] [Accepted: 07/17/2021] [Indexed: 01/12/2023]
Abstract
Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year's dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.
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Affiliation(s)
- Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (K.S.S.); (M.A.)
| | - Meriem Aoun
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (K.S.S.); (M.A.)
| | - Craig F. Morris
- USDA-ARS Western Wheat Quality Laboratory, E-202 Food Quality Building, Washington State University, Pullman, WA 99164, USA;
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (K.S.S.); (M.A.)
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Sandhu KS, Mihalyov PD, Lewien MJ, Pumphrey MO, Carter AH. Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:613300. [PMID: 33643347 PMCID: PMC7907601 DOI: 10.3389/fpls.2021.613300] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/25/2021] [Indexed: 05/10/2023]
Abstract
Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014-2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | | | | | - Michael O. Pumphrey
- 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
- *Correspondence: Arron H. Carter,
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