1
|
Wu H, Gao B, Zhang R, Huang Z, Yin Z, Hu X, Yang CX, Du ZQ. Residual network improves the prediction accuracy of genomic selection. Anim Genet 2024; 55:599-611. [PMID: 38746973 DOI: 10.1111/age.13445] [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: 10/21/2023] [Revised: 04/21/2024] [Accepted: 04/29/2024] [Indexed: 07/04/2024]
Abstract
Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.
Collapse
Affiliation(s)
- Huaxuan Wu
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
| | - Bingxi Gao
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
| | - Rong Zhang
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
| | - Zehang Huang
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Xiaoxiang Hu
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Cai-Xia Yang
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
| | - Zhi-Qiang Du
- College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China
| |
Collapse
|
2
|
Li J, Zhang D, Yang F, Zhang Q, Pan S, Zhao X, Zhang Q, Han Y, Yang J, Wang K, Zhao C. TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield. PLANT COMMUNICATIONS 2024; 5:100975. [PMID: 38751121 DOI: 10.1016/j.xplc.2024.100975] [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: 12/04/2023] [Revised: 04/14/2024] [Accepted: 05/11/2024] [Indexed: 06/24/2024]
Abstract
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.
Collapse
Affiliation(s)
- Jinlong Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Dongfeng Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Feng Yang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Qiusi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shouhui Pan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xiangyu Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Qi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Yanyun Han
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Kaiyi Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
| |
Collapse
|
3
|
Graça M, Nobre R, Sousa L, Ilic A. Distributed transformer for high order epistasis detection in large-scale datasets. Sci Rep 2024; 14:14579. [PMID: 38918413 PMCID: PMC11199512 DOI: 10.1038/s41598-024-65317-5] [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: 02/24/2024] [Accepted: 06/19/2024] [Indexed: 06/27/2024] Open
Abstract
Understanding the genetic basis of complex diseases is one of the most important challenges in current precision medicine. To this end, Genome-Wide Association Studies aim to correlate Single Nucleotide Polymorphisms (SNPs) to the presence or absence of certain traits. However, these studies do not consider interactions between several SNPs, known as epistasis, which explain most genetic diseases. Analyzing SNP combinations to detect epistasis is a major computational task, due to the enormous search space. A possible solution is to employ deep learning strategies for genomic prediction, but the lack of explainability derived from the black-box nature of neural networks is a challenge yet to be addressed. Herein, a novel, flexible, portable, and scalable framework for network interpretation based on transformers is proposed to tackle any-order epistasis. The results on various epistasis scenarios show that the proposed framework outperforms state-of-the-art methods for explainability, while being scalable to large datasets and portable to various deep learning accelerators. The proposed framework is validated on three WTCCC datasets, identifying SNPs related to genes known in the literature that have direct relationships with the studied diseases.
Collapse
Affiliation(s)
- Miguel Graça
- INESC-ID, Instituto Superior Técnico, 1000-029, Lisbon, Portugal.
| | - Ricardo Nobre
- INESC-ID, Instituto Superior Técnico, 1000-029, Lisbon, Portugal
| | - Leonel Sousa
- INESC-ID, Instituto Superior Técnico, 1000-029, Lisbon, Portugal
| | - Aleksandar Ilic
- INESC-ID, Instituto Superior Técnico, 1000-029, Lisbon, Portugal
| |
Collapse
|
4
|
Bose S, Banerjee S, Kumar S, Saha A, Nandy D, Hazra S. Review of applications of artificial intelligence (AI) methods in crop research. J Appl Genet 2024; 65:225-240. [PMID: 38216788 DOI: 10.1007/s13353-023-00826-z] [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: 08/13/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/14/2024]
Abstract
Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.
Collapse
Affiliation(s)
- Suvojit Bose
- Department of Vegetables and Spice Crops, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, 736165, West Bengal, India
| | | | - Soumya Kumar
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Akash Saha
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Debalina Nandy
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Soham Hazra
- Department of Agriculture, Brainware University, Barasat, 700125, West Bengal, India.
| |
Collapse
|
5
|
Yurtseven A, Buyanova S, Agrawal AA, Bochkareva OO, Kalinina OV. Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis. BMC Microbiol 2023; 23:404. [PMID: 38124060 PMCID: PMC10731705 DOI: 10.1186/s12866-023-03147-7] [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: 09/12/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. RESULTS In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. CONCLUSIONS Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.
Collapse
Affiliation(s)
- Alper Yurtseven
- Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, Saarbrücken, 66123, Saarland, Germany.
- Graduate School of Computer Science, Saarland University, Saarbrücken, 66123, Saarland, Germany.
| | - Sofia Buyanova
- Institute of Science and Technology Austria (ISTA), Am Campus 1, Klosterneuburg, 3400, Austria
| | - Amay Ajaykumar Agrawal
- Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, Saarbrücken, 66123, Saarland, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken, 66123, Saarland, Germany
| | - Olga O Bochkareva
- Institute of Science and Technology Austria (ISTA), Am Campus 1, Klosterneuburg, 3400, Austria
- Centre for Microbiology and Environmental Systems Science, Division of Computational System Biology, University of Vienna, Djerassiplatz 1 A, Wien, 1030, Austria
| | - Olga V Kalinina
- Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, Saarbrücken, 66123, Saarland, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken, 66123, Saarland, Germany
- Faculty of Medicine, Saarland University, Homburg, 66421, Saarland, Germany
| |
Collapse
|
6
|
Wu C, Zhang Y, Ying Z, Li L, Wang J, Yu H, Zhang M, Feng X, Wei X, Xu X. A transformer-based genomic prediction method fused with knowledge-guided module. Brief Bioinform 2023; 25:bbad438. [PMID: 38058185 PMCID: PMC10701102 DOI: 10.1093/bib/bbad438] [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: 09/07/2023] [Revised: 10/15/2023] [Accepted: 11/03/2023] [Indexed: 12/08/2023] Open
Abstract
Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.
Collapse
Affiliation(s)
- Cuiling Wu
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Yiyi Zhang
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Zhiwen Ying
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Ling Li
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Jun Wang
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Hui Yu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
| | - Mengchen Zhang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Xianzhong Feng
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
| | - Xinghua Wei
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Xiaogang Xu
- School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
| |
Collapse
|
7
|
Khvorykh GV, Sapozhnikov NA, Limborska SA, Khrunin AV. Evaluation of Density-Based Spatial Clustering for Identifying Genomic Loci Associated with Ischemic Stroke in Genome-Wide Data. Int J Mol Sci 2023; 24:15355. [PMID: 37895035 PMCID: PMC10607504 DOI: 10.3390/ijms242015355] [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: 07/22/2023] [Revised: 09/19/2023] [Accepted: 09/28/2023] [Indexed: 10/29/2023] Open
Abstract
The genetic architecture of ischemic stroke (IS), which is one of the leading causes of death worldwide, is complex and underexplored. The traditional approach for associative gene mapping is genome-wide association studies (GWASs), testing individual single-nucleotide polymorphisms (SNPs) across the genomes of case and control groups. The purpose of this research is to develop an alternative approach in which groups of SNPs are examined rather than individual ones. We proposed, validated and applied to real data a new workflow consisting of three key stages: grouping SNPs in clusters, inferring the haplotypes in the clusters and testing haplotypes for the association with phenotype. To group SNPs, we applied the clustering algorithms DBSCAN and HDBSCAN to linkage disequilibrium (LD) matrices, representing pairwise r2 values between all genotyped SNPs. These clustering algorithms have never before been applied to genotype data as part of the workflow of associative studies. In total, 883,908 SNPs and insertion/deletion polymorphisms from people of European ancestry (4929 cases and 652 controls) were processed. The subsequent testing for frequencies of haplotypes restored in the clusters of SNPs revealed dozens of genes associated with IS and suggested the complex role that protocadherin molecules play in IS. The developed workflow was validated with the use of a simulated dataset of similar ancestry and the same sample sizes. The results of classic GWASs are also provided and discussed. The considered clustering algorithms can be applied to genotypic data to identify the genomic loci associated with different qualitative traits, using the workflow presented in this research.
Collapse
Affiliation(s)
| | | | | | - Andrey V. Khrunin
- National Research Centre “Kurchatov Institute”, Kurchatov Sq. 2, Moscow 123182, Russia; (G.V.K.); (N.A.S.); (S.A.L.)
| |
Collapse
|
8
|
Canella Vieira C, Zhou J, Jarquin D, Zhou J, Diers B, Riechers DE, Nguyen HT, Shannon G. Genetic architecture of soybean tolerance to off-target dicamba. FRONTIERS IN PLANT SCIENCE 2023; 14:1230068. [PMID: 37877091 PMCID: PMC10590897 DOI: 10.3389/fpls.2023.1230068] [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: 05/27/2023] [Accepted: 09/27/2023] [Indexed: 10/26/2023]
Abstract
The adoption of dicamba-tolerant (DT) soybean in the United States resulted in extensive off-target dicamba damage to non-DT vegetation across soybean-producing states. Although soybeans are highly sensitive to dicamba, the intensity of observed symptoms and yield losses are affected by the genetic background of genotypes. Thus, the objective of this study was to detect novel marker-trait associations and expand on previously identified genomic regions related to soybean response to off-target dicamba. A total of 551 non-DT advanced breeding lines derived from 232 unique bi-parental populations were phenotyped for off-target dicamba across nine environments for three years. Breeding lines were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip. Filtered SNPs were included as predictors in Random Forest (RF) and Support Vector Machine (SVM) models in a forward stepwise selection loop to identify the combination of SNPs yielding the highest classification accuracy. Both RF and SVM models yielded high classification accuracies (0.76 and 0.79, respectively) with minor extreme misclassifications (observed tolerant predicted as susceptible, and vice-versa). Eight genomic regions associated with off-target dicamba tolerance were identified on chromosomes 6 [Linkage Group (LG) C2], 8 (LG A2), 9 (LG K), 10 (LG O), and 19 (LG L). Although the genetic architecture of tolerance is complex, high classification accuracies were obtained when including the major effect SNP identified on chromosome 6 as the sole predictor. In addition, candidate genes with annotated functions associated with phases II (conjugation of hydroxylated herbicides to endogenous sugar molecules) and III (transportation of herbicide conjugates into the vacuole) of herbicide detoxification in plants were co-localized with significant markers within each genomic region. Genomic prediction models, as reported in this study, can greatly facilitate the identification of genotypes with superior tolerance to off-target dicamba.
Collapse
Affiliation(s)
- Caio Canella Vieira
- Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States
| | - Jing Zhou
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Jianfeng Zhou
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Brian Diers
- Department of Crop Sciences, University of Illinois, Urbana, IL, United States
| | - Dean E. Riechers
- Department of Crop Sciences, University of Illinois, Urbana, IL, United States
| | - Henry T. Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Grover Shannon
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| |
Collapse
|
9
|
Ferrão LFV, Dhakal R, Dias R, Tieman D, Whitaker V, Gore MA, Messina C, Resende MFR. Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics. Curr Opin Biotechnol 2023; 83:102968. [PMID: 37515935 DOI: 10.1016/j.copbio.2023.102968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/31/2023]
Abstract
Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.
Collapse
Affiliation(s)
- Luís Felipe V Ferrão
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Rakshya Dhakal
- Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Raquel Dias
- Microbiology and Cell Science Department, University of Florida, Gainesville, FL, United States
| | - Denise Tieman
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Vance Whitaker
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Carlos Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States
| | - Márcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States; Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States.
| |
Collapse
|
10
|
Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
Collapse
Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Mowlaei ME, Shi X. FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms. Genes (Basel) 2023; 14:genes14051059. [PMID: 37239419 DOI: 10.3390/genes14051059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Phenotype prediction is a pivotal task in genetics in order to identify how genetic factors contribute to phenotypic differences. This field has seen extensive research, with numerous methods proposed for predicting phenotypes. Nevertheless, the intricate relationship between genotypes and complex phenotypes, including common diseases, has resulted in an ongoing challenge to accurately decipher the genetic contribution. (2) Results: In this study, we propose a novel feature selection framework for phenotype prediction utilizing a genetic algorithm (FSF-GA) that effectively reduces the feature space to identify genotypes contributing to phenotype prediction. We provide a comprehensive vignette of our method and conduct extensive experiments using a widely used yeast dataset. (3) Conclusions: Our experimental results show that our proposed FSF-GA method delivers comparable phenotype prediction performance as compared to baseline methods, while providing features selected for predicting phenotypes. These selected feature sets can be used to interpret the underlying genetic architecture that contributes to phenotypic variation.
Collapse
Affiliation(s)
- Mohammad Erfan Mowlaei
- Department of Computer and Information Sciences, Temple University, 925 N. 12th Street, Philadelphia, PA 19122, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, 925 N. 12th Street, Philadelphia, PA 19122, USA
| |
Collapse
|
13
|
Kick DR, Wallace JG, Schnable JC, Kolkman JM, Alaca B, Beissinger TM, Edwards J, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta DE, Singh MP, Thompson A, Weldekidan T, Washburn JD. Yield prediction through integration of genetic, environment, and management data through deep learning. G3 (BETHESDA, MD.) 2023; 13:jkad006. [PMID: 36625555 PMCID: PMC10085787 DOI: 10.1093/g3journal/jkad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 07/28/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023]
Abstract
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
Collapse
Affiliation(s)
- Daniel R Kick
- United States Department of Agriculture, Agricultural Research Service Plant Genetics Research Unit, Columbia, MO 65211, USA
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Jason G Wallace
- Department of Crop & Soil Science, University of Georgia, Athens, GA 30602, USA
| | - James C Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Judith M Kolkman
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Barış Alaca
- Division of Plant Breeding Methodology, Department of Crop Science, University of Goettingen, Goettingen 37073, Germany
- Center for Integrated Breeding Research, University of Goettingen, Goettingen 37073, Germany
| | - Timothy M Beissinger
- Division of Plant Breeding Methodology, Department of Crop Science, University of Goettingen, Goettingen 37073, Germany
- Center for Integrated Breeding Research, University of Goettingen, Goettingen 37073, Germany
| | - Jode Edwards
- United States Department of Agriculture, Agricultural Research Service, Ames, IA 50011, USA
| | - David Ertl
- Research and Business Development, Iowa Corn Promotion Board, Johnston, IA 50131, USA
| | - Sherry Flint-Garcia
- United States Department of Agriculture, Agricultural Research Service Plant Genetics Research Unit, Columbia, MO 65211, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Joseph E Knoll
- United States Department of Agriculture, Agricultural Research Service Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Dayane C Lima
- Plant Breeding and Plant Genetics Program, University of Wisconsin, Madison, WI 53706, USA
| | - Danilo E Moreta
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service Plant Genetics Research Unit, Columbia, MO 65211, USA
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
14
|
Du J, Wang C, Wang L, Mao S, Zhu B, Li Z, Fan X. Automatic block-wise genotype-phenotype association detection based on hidden Markov model. BMC Bioinformatics 2023; 24:138. [PMID: 37029361 PMCID: PMC10082540 DOI: 10.1186/s12859-023-05265-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 03/31/2023] [Indexed: 04/09/2023] Open
Abstract
BACKGROUND For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.
Collapse
Affiliation(s)
- Jin Du
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
| | - Chaojie Wang
- School of Mathematical Science, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Lijun Wang
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Shanjun Mao
- College of Finance and Statistics, Hunan University, Changsha, Hunan Province, China
| | - Bencong Zhu
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Zheng Li
- Department of Surgery, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Xiaodan Fan
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
| |
Collapse
|
15
|
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.
Collapse
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.
| |
Collapse
|
16
|
Abstract
Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.
Collapse
|
17
|
Jeon D, Kang Y, Lee S, Choi S, Sung Y, Lee TH, Kim C. Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction. FRONTIERS IN PLANT SCIENCE 2023; 14:1092584. [PMID: 36743488 PMCID: PMC9892199 DOI: 10.3389/fpls.2023.1092584] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/05/2023] [Indexed: 06/18/2023]
Abstract
As the world's population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future.
Collapse
Affiliation(s)
- Donghyun Jeon
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
| | - Yuna Kang
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Solji Lee
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Sehyun Choi
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Yeonjun Sung
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
| | - Tae-Ho Lee
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, Republic of Korea
| | - Changsoo Kim
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
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.
Collapse
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.
| |
Collapse
|
20
|
Tirnaz S, Zandberg J, Thomas WJW, Marsh J, Edwards D, Batley J. Application of crop wild relatives in modern breeding: An overview of resources, experimental and computational methodologies. FRONTIERS IN PLANT SCIENCE 2022; 13:1008904. [PMID: 36466237 PMCID: PMC9712971 DOI: 10.3389/fpls.2022.1008904] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/25/2022] [Indexed: 06/01/2023]
Abstract
Global agricultural industries are under pressure to meet the future food demand; however, the existing crop genetic diversity might not be sufficient to meet this expectation. Advances in genome sequencing technologies and availability of reference genomes for over 300 plant species reveals the hidden genetic diversity in crop wild relatives (CWRs), which could have significant impacts in crop improvement. There are many ex-situ and in-situ resources around the world holding rare and valuable wild species, of which many carry agronomically important traits and it is crucial for users to be aware of their availability. Here we aim to explore the available ex-/in- situ resources such as genebanks, botanical gardens, national parks, conservation hotspots and inventories holding CWR accessions. In addition we highlight the advances in availability and use of CWR genomic resources, such as their contribution in pangenome construction and introducing novel genes into crops. We also discuss the potential and challenges of modern breeding experimental approaches (e.g. de novo domestication, genome editing and speed breeding) used in CWRs and the use of computational (e.g. machine learning) approaches that could speed up utilization of CWR species in breeding programs towards crop adaptability and yield improvement.
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
A divide-and-conquer approach for genomic prediction in rubber tree using machine learning. Sci Rep 2022; 12:18023. [PMID: 36289298 PMCID: PMC9605989 DOI: 10.1038/s41598-022-20416-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/13/2022] [Indexed: 01/20/2023] Open
Abstract
Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.
Collapse
|
23
|
Ali A, Altaf MT, Nadeem MA, Karaköy T, Shah AN, Azeem H, Baloch FS, Baran N, Hussain T, Duangpan S, Aasim M, Boo KH, Abdelsalam NR, Hasan ME, Chung YS. Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes. FRONTIERS IN PLANT SCIENCE 2022; 13:952759. [PMID: 36247536 PMCID: PMC9554552 DOI: 10.3389/fpls.2022.952759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/12/2022] [Indexed: 06/16/2023]
Abstract
The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.
Collapse
Affiliation(s)
- Amjad Ali
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Muhammad Tanveer Altaf
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Tolga Karaköy
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Adnan Noor Shah
- Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Hajra Azeem
- Department of Plant Pathology, Faculty of Agricultural Sciences & Technology, Bahauddin Zakariya University, Multan, Pakistan
| | - Faheem Shehzad Baloch
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Nurettin Baran
- Bitkisel Uretim ve Teknolojileri Bolumu, Uygulamali Bilimler Faku Itesi, Mus Alparslan Universitesi, Mus, Turkey
| | - Tajamul Hussain
- Laboratory of Plant Breeding and Climate Resilient Agriculture, Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, Thailand
| | - Saowapa Duangpan
- Laboratory of Plant Breeding and Climate Resilient Agriculture, Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, Thailand
| | - Muhammad Aasim
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Kyung-Hwan Boo
- Subtropical/Tropical Organism Gene Bank, Department of Biotechnology, College of Applied Life Science, Jeju National University, Jeju, South Korea
| | - Nader R. Abdelsalam
- Agricultural Botany Department, Faculty of Agriculture (Saba Basha), Alexandria University, Alexandria, Egypt
| | - Mohamed E. Hasan
- Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju, South Korea
| |
Collapse
|
24
|
A joint learning approach for genomic prediction in polyploid grasses. Sci Rep 2022; 12:12499. [PMID: 35864135 PMCID: PMC9304331 DOI: 10.1038/s41598-022-16417-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/11/2022] [Indexed: 12/20/2022] Open
Abstract
Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Muneeb M, Feng SF, Henschel A. Can We Convert Genotype Sequences Into Images for Cases/Controls Classification? FRONTIERS IN BIOINFORMATICS 2022; 2:914435. [PMID: 36304278 PMCID: PMC9580854 DOI: 10.3389/fbinf.2022.914435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Converting genotype sequences into images offers advantages, such as genotype data visualization, classification, and comparison of genotype sequences. This study converted genotype sequences into images, applied two-dimensional convolutional neural networks for case/control classification, and compared the results with the one-dimensional convolutional neural network. Surprisingly, the average accuracy of multiple runs of 2DCNN was 0.86, and that of 1DCNN was 0.89, yielding a difference of 0.03, which suggests that even the 2DCNN algorithm works on genotype sequences. Moreover, the results generated by the 2DCNN exhibited less variation than those generated by the 1DCNN, thereby offering greater stability. The purpose of this study is to draw the research community’s attention to explore encoding schemes for genotype data and machine learning algorithms that can be used on genotype data by changing the representation of the genotype data for case/control classification.
Collapse
Affiliation(s)
- Muhammad Muneeb
- Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- *Correspondence: Muhammad Muneeb,
| | - Samuel F. Feng
- Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Research and Data Intelligence Support Center R-DISC, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Andreas Henschel
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Research and Data Intelligence Support Center R-DISC, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| |
Collapse
|
27
|
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.
Collapse
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,
| |
Collapse
|
28
|
Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms. Int J Mol Sci 2022; 23:ijms23095121. [PMID: 35563516 PMCID: PMC9100765 DOI: 10.3390/ijms23095121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/25/2023] Open
Abstract
Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.
Collapse
|
29
|
Canella Vieira C, Zhou J, Usovsky M, Vuong T, Howland AD, Lee D, Li Z, Zhou J, Shannon G, Nguyen HT, Chen P. Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans. FRONTIERS IN PLANT SCIENCE 2022; 13:883280. [PMID: 35592556 PMCID: PMC9111516 DOI: 10.3389/fpls.2022.883280] [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: 02/24/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.
Collapse
Affiliation(s)
- Caio Canella Vieira
- Fisher Delta Research, Extension, and Education Center, Division of Plant Science and Technology, University of Missouri, Portageville, MO, United States
| | - Jing Zhou
- Biological Systems Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Mariola Usovsky
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Tri Vuong
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Amanda D. Howland
- Department of Entomology, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI, United States
| | - Dongho Lee
- Fisher Delta Research, Extension, and Education Center, Division of Plant Science and Technology, University of Missouri, Portageville, MO, United States
| | - Zenglu Li
- Institute of Plant Breeding, Genetics, and Genomics, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United States
| | - Jianfeng Zhou
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Grover Shannon
- Fisher Delta Research, Extension, and Education Center, Division of Plant Science and Technology, University of Missouri, Portageville, MO, United States
| | - Henry T. Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States
| | - Pengyin Chen
- Fisher Delta Research, Extension, and Education Center, Division of Plant Science and Technology, University of Missouri, Portageville, MO, United States
| |
Collapse
|
30
|
Mathew B, Hauptmann A, Léon J, Sillanpää MJ. NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2022; 13:800161. [PMID: 35574107 PMCID: PMC9100816 DOI: 10.3389/fpls.2022.800161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/18/2022] [Indexed: 06/15/2023]
Abstract
Prediction of complex traits based on genome-wide marker information is of central importance for both animal and plant breeding. Numerous models have been proposed for the prediction of complex traits and still considerable effort has been given to improve the prediction accuracy of these models, because various genetics factors like additive, dominance and epistasis effects can influence of the prediction accuracy of such models. Recently machine learning (ML) methods have been widely applied for prediction in both animal and plant breeding programs. In this study, we propose a new algorithm for genomic prediction which is based on neural networks, but incorporates classical elements of LASSO. Our new method is able to account for the local epistasis (higher order interaction between the neighboring markers) in the prediction. We compare the prediction accuracy of our new method with the most commonly used prediction methods, such as BayesA, BayesB, Bayesian Lasso (BL), genomic BLUP and Elastic Net (EN) using the heterogenous stock mouse and rice field data sets.
Collapse
Affiliation(s)
- Boby Mathew
- Bayer CropScience, Monheim am Rhein, Germany
- Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Andreas Hauptmann
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- Department of Computer Science, University College London, London, United Kingdom
| | - Jens Léon
- Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Mikko J. Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| |
Collapse
|
31
|
Genome-Enabled Prediction Methods Based on Machine Learning. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:189-218. [PMID: 35451777 DOI: 10.1007/978-1-0716-2205-6_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.
Collapse
|
32
|
Tay Fernandez CG, Nestor BJ, Danilevicz MF, Gill M, Petereit J, Bayer PE, Finnegan PM, Batley J, Edwards D. Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species. Int J Mol Sci 2022; 23:2671. [PMID: 35269811 PMCID: PMC8910360 DOI: 10.3390/ijms23052671] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - David Edwards
- School of Biological Sciences, The University of Western Australia, Perth, WA 6009, Australia; (C.G.T.F.); (B.J.N.); (M.F.D.); (M.G.); (J.P.); (P.E.B.); (P.M.F.); (J.B.)
| |
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
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.
Collapse
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
| |
Collapse
|
35
|
Coelho de Sousa I, Nascimento M, de Castro Sant’anna I, Teixeira Caixeta E, Ferreira Azevedo C, Damião Cruz C, Lopes da Silva F, Ruas Alkimim E, Campana Nascimento AC, Vergara Lopes Serão N. Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. PLoS One 2022; 17:e0262055. [PMID: 35081139 PMCID: PMC8791507 DOI: 10.1371/journal.pone.0262055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 12/15/2021] [Indexed: 11/18/2022] Open
Abstract
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. The predictive ability was greater when using ANN with three hidden layers than using one hidden layer and GBLUP, with 0.72 and 0.88 for yield and coffee leaf rust resistance, respectively. The concordance rate (CR) of the 10% larger markers effects among the methods varied between 10% and 13.8%, for additive effects and between 5.4% and 11.9% for dominance effects. The narrow-sense ([Formula: see text]) and dominance-only ([Formula: see text]) heritability estimates were 0.25 and 0.06, respectively, for yield, and 0.67 and 0.03, respectively for rust resistance. The ANN was able to estimate the heritabilities from an additive-dominance genomic architectures and the ANN with three hidden layers obtained best predictive ability when compared with those obtained from GBLUP and ANN with one hidden layer.
Collapse
Affiliation(s)
- Ithalo Coelho de Sousa
- Department of Animal Science, Iowa State University, Ames, Iowa, United States of America
- Department of Statistics, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Moysés Nascimento
- Department of Statistics, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Isabela de Castro Sant’anna
- Rubber Tree and Agroforestry Systems Research Center, Campinas Agronomy Institute (IAC), Votuporanga, São Paulo, Brazil
| | | | | | - Cosme Damião Cruz
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Felipe Lopes da Silva
- Department of Plant Science, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | | | | |
Collapse
|
36
|
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.
Collapse
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
| |
Collapse
|
37
|
Mathema VB, Duangkumpha K, Wanichthanarak K, Jariyasopit N, Dhakal E, Sathirapongsasuti N, Kitiyakara C, Sirivatanauksorn Y, Khoomrung S. CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics. Brief Bioinform 2022; 23:6503617. [PMID: 35022651 PMCID: PMC8921635 DOI: 10.1093/bib/bbab550] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/19/2021] [Accepted: 11/29/2021] [Indexed: 01/16/2023] Open
Abstract
Two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) provides a large amount of molecular information from biological samples. However, the lack of a comprehensive compound library or customizable bioinformatics tool is currently a challenge in GC × GC–TOFMS data analysis. We present an open-source deep learning (DL) software called contour regions of interest (ROI) identification, simulation and untargeted metabolomics profiler (CRISP). CRISP integrates multiple customizable deep neural network architectures for assisting the semi-automated identification of ROIs, contour synthesis, resolution enhancement and classification of GC × GC–TOFMS-based contour images. The approach includes the novel aggregate feature representative contour (AFRC) construction and stacked ROIs. This generates an unbiased contour image dataset that enhances the contrasting characteristics between different test groups and can be suitable for small sample sizes. The utility of the generative models and the accuracy and efficacy of the platform were demonstrated using a dataset of GC × GC–TOFMS contour images from patients with late-stage diabetic nephropathy and healthy control groups. CRISP successfully constructed AFRC images and identified over five ROIs to create a deepstacked dataset. The high fidelity, 512 × 512-pixels generative model was trained as a generator with a Fréchet inception distance of <47.00. The trained classifier achieved an AUROC of >0.96 and a classification accuracy of >95.00% for datasets with and without column bleed. Overall, CRISP demonstrates good potential as a DL-based approach for the rapid analysis of 4-D GC × GC–TOFMS untargeted metabolite profiles by directly implementing contour images. CRISP is available at https://github.com/vivekmathema/GCxGC-CRISP.
Collapse
Affiliation(s)
- Vivek Bhakta Mathema
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Kassaporn Duangkumpha
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Kwanjeera Wanichthanarak
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Narumol Jariyasopit
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Esha Dhakal
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Nuankanya Sathirapongsasuti
- Section of Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.,Research Network of NANOTEC - MU Ramathibodi on Nanomedicine, Bangkok, Thailand
| | - Chagriya Kitiyakara
- Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Rama VI Rd., Ratchathewi, Bangkok 10400, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok, Thailand
| |
Collapse
|
38
|
Kwon OS, Hong M, Kim TH, Hwang I, Shim J, Choi EK, Lim HE, Yu HT, Uhm JS, Joung B, Oh S, Lee MH, Kim YH, Pak HN. Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence. Open Heart 2022; 9:e001898. [PMID: 35086918 PMCID: PMC8796259 DOI: 10.1136/openhrt-2021-001898] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN). METHODS This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling. RESULTS The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd-even registration numbers (AUC 0.51±0.01). CONCLUSIONS AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs. TRIAL REGISTRATION NUMBER NCT02138695.
Collapse
Affiliation(s)
- Oh-Seok Kwon
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Myunghee Hong
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Tae-Hoon Kim
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Inseok Hwang
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Jaemin Shim
- Cardiovascular Center, Korea University Medical Center, Seoul, Korea (the Republic of)
| | - Eue-Keun Choi
- Cardiology, Seoul National University, Seoul, Korea (the Republic of)
| | - Hong Euy Lim
- Cardiology, Hallym University Sacred Heart Hospital, Anyang, Gyeonggi-do, Korea (the Republic of)
| | - Hee Tae Yu
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Jae-Sun Uhm
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Boyoung Joung
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Seil Oh
- Cardiology, Seoul National University, Seoul, Korea (the Republic of)
| | - Moon-Hyoung Lee
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| | - Young-Hoon Kim
- Cardiovascular Center, Korea University Medical Center, Seoul, Korea (the Republic of)
| | - Hui-Nam Pak
- Cardiology, Yonsei University Health System, Seodaemun-gu, Seoul, Korea (the Republic of)
| |
Collapse
|
39
|
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).
Collapse
|
40
|
Ubbens J, Parkin I, Eynck C, Stavness I, Sharpe AG. Deep neural networks for genomic prediction do not estimate marker effects. THE PLANT GENOME 2021; 14:e20147. [PMID: 34596363 DOI: 10.1002/tpg2.20147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.
Collapse
Affiliation(s)
- Jordan Ubbens
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| | - Isobel Parkin
- Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada
| | - Christina Eynck
- Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada
| | - Ian Stavness
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| | - Andrew G Sharpe
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| |
Collapse
|
41
|
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.
Collapse
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
| |
Collapse
|
42
|
Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values. Genes (Basel) 2021; 12:genes12111754. [PMID: 34828360 PMCID: PMC8626003 DOI: 10.3390/genes12111754] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/23/2021] [Accepted: 10/23/2021] [Indexed: 11/17/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.
Collapse
|
43
|
Singh D, Chaudhary P, Taunk J, Singh CK, Singh D, Tomar RSS, Aski M, Konjengbam NS, Raje RS, Singh S, Sengar RS, Yadav RK, Pal M. Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence. Int J Mol Sci 2021; 22:10535. [PMID: 34638885 PMCID: PMC8509049 DOI: 10.3390/ijms221910535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/17/2021] [Accepted: 09/23/2021] [Indexed: 11/16/2022] Open
Abstract
Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.
Collapse
Affiliation(s)
- Dharmendra Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Priya Chaudhary
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Jyoti Taunk
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Chandan Kumar Singh
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Deepti Singh
- Department of Botany, Meerut College, Meerut 250001, India
| | - Ram Sewak Singh Tomar
- College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi 284003, India
| | - Muraleedhar Aski
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Noren Singh Konjengbam
- College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal 793103, India
| | - Ranjeet Sharan Raje
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| | - Sanjay Singh
- ICAR- National Institute of Plant Biotechnology, LBS Centre, Pusa Campus, New Delhi 110012, India
| | - Rakesh Singh Sengar
- College of Biotechnology, Sardar Vallabh Bhai Patel Agricultural University, Meerut 250001, India
| | - Rajendra Kumar Yadav
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur 208002, India
| | - Madan Pal
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India
| |
Collapse
|
44
|
Passamonti MM, Somenzi E, Barbato M, Chillemi G, Colli L, Joost S, Milanesi M, Negrini R, Santini M, Vajana E, Williams JL, Ajmone-Marsan P. The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock. Animals (Basel) 2021; 11:2833. [PMID: 34679854 PMCID: PMC8532622 DOI: 10.3390/ani11102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 12/14/2022] Open
Abstract
Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.
Collapse
Affiliation(s)
- Matilde Maria Passamonti
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Elisa Somenzi
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Mario Barbato
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Giovanni Chillemi
- Department for Innovation in Biological, Agro-Food and Forest Systems–DIBAF, Università Della Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy; (G.C.); (M.M.)
| | - Licia Colli
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
- Research Center on Biodiversity and Ancient DNA—BioDNA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy
| | - Stéphane Joost
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; (S.J.); (E.V.)
| | - Marco Milanesi
- Department for Innovation in Biological, Agro-Food and Forest Systems–DIBAF, Università Della Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy; (G.C.); (M.M.)
| | - Riccardo Negrini
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Monia Santini
- Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (CMCC), Viale Trieste 127, 01100 Viterbo, Italy;
| | - Elia Vajana
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; (S.J.); (E.V.)
| | - John Lewis Williams
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
| | - Paolo Ajmone-Marsan
- Department of Animal Science, Food and Nutrition—DIANA, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy; (M.M.P.); (E.S.); (M.B.); (L.C.); (R.N.); (J.L.W.)
- Nutrigenomics and Proteomics Research Center—PRONUTRIGEN, Università Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29122 Piacenza, Italy
| |
Collapse
|
45
|
Nakhle F, Harfouche AL. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. PATTERNS (NEW YORK, N.Y.) 2021; 2:100323. [PMID: 34553170 PMCID: PMC8441561 DOI: 10.1016/j.patter.2021.100323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
Collapse
Affiliation(s)
- Farid Nakhle
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
| | - Antoine L. Harfouche
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
| |
Collapse
|
46
|
Razzaq A, Kaur P, Akhter N, Wani SH, Saleem F. Next-Generation Breeding Strategies for Climate-Ready Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:620420. [PMID: 34367194 PMCID: PMC8336580 DOI: 10.3389/fpls.2021.620420] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 06/14/2021] [Indexed: 05/17/2023]
Abstract
Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.
Collapse
Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Parwinder Kaur
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Naheed Akhter
- College of Allied Health Professional, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| |
Collapse
|
47
|
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.
Collapse
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.)
| |
Collapse
|
48
|
Zeng S, Mao Z, Ren Y, Wang D, Xu D, Joshi T. G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers. Nucleic Acids Res 2021; 49:W228-W236. [PMID: 34037802 PMCID: PMC8262736 DOI: 10.1093/nar/gkab407] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/28/2021] [Accepted: 05/03/2021] [Indexed: 11/13/2022] Open
Abstract
G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and animals as the input to predict quantitative phenotype of interest and genomic markers associated with phenotype. It provides a one-stop-shop platform for researchers to create deep-learning models through an interactive web interface and train these models with uploaded data, using high-performance computing resources plugged at the backend. G2PDeep also provides a series of informative interfaces to monitor the training process and compare the performance among the trained models. The trained models can then be deployed automatically. The quantitative phenotype and genomic markers are predicted using a user-selected trained model and the results are visualized. Our state-of-the-art model has been benchmarked and demonstrated competitive performance in quantitative phenotype predictions by other researchers. In addition, the server integrates the soybean nested association mapping (SoyNAM) dataset with five phenotypes, including grain yield, height, moisture, oil, and protein. A publicly available dataset for seed protein and oil content has also been integrated into the server. The G2PDeep server is publicly available at http://g2pdeep.org. The Python-based deep-learning model is available at https://github.com/shuaizengMU/G2PDeep_model.
Collapse
Affiliation(s)
- Shuai Zeng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Ziting Mao
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yijie Ren
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
49
|
Marsh JI, Hu H, Gill M, Batley J, Edwards D. Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1677-1690. [PMID: 33852055 DOI: 10.1007/s00122-021-03820-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/18/2021] [Indexed: 05/05/2023]
Abstract
Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.
Collapse
Affiliation(s)
- Jacob I Marsh
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth, 6009, Australia.
| |
Collapse
|
50
|
Gaillochet C, Develtere W, Jacobs TB. CRISPR screens in plants: approaches, guidelines, and future prospects. THE PLANT CELL 2021; 33:794-813. [PMID: 33823021 PMCID: PMC8226290 DOI: 10.1093/plcell/koab099] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 04/02/2021] [Indexed: 05/20/2023]
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR)-associated systems have revolutionized genome engineering by facilitating a wide range of targeted DNA perturbations. These systems have resulted in the development of powerful new screens to test gene functions at the genomic scale. While there is tremendous potential to map and interrogate gene regulatory networks at unprecedented speed and scale using CRISPR screens, their implementation in plants remains in its infancy. Here we discuss the general concepts, tools, and workflows for establishing CRISPR screens in plants and analyze the handful of recent reports describing the use of this strategy to generate mutant knockout collections or to diversify DNA sequences. In addition, we provide insight into how to design CRISPR knockout screens in plants given the current challenges and limitations and examine multiple design options. Finally, we discuss the unique multiplexing capabilities of CRISPR screens to investigate redundant gene functions in highly duplicated plant genomes. Combinatorial mutant screens have the potential to routinely generate higher-order mutant collections and facilitate the characterization of gene networks. By integrating this approach with the numerous genomic profiles that have been generated over the past two decades, the implementation of CRISPR screens offers new opportunities to analyze plant genomes at deeper resolution and will lead to great advances in functional and synthetic biology.
Collapse
Affiliation(s)
- Christophe Gaillochet
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Center for Plant Systems Biology, Ghent 9052, Belgium
| | - Ward Develtere
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Center for Plant Systems Biology, Ghent 9052, Belgium
| | - Thomas B Jacobs
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Center for Plant Systems Biology, Ghent 9052, Belgium
| |
Collapse
|