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Lippolis A, Polo PV, de Sousa G, Dechesne A, Pouvreau L, Trindade LM. High-throughput seed quality analysis in faba bean: leveraging Near-InfraRed spectroscopy (NIRS) data and statistical methods. Food Chem X 2024; 23:101583. [PMID: 39071925 PMCID: PMC11282932 DOI: 10.1016/j.fochx.2024.101583] [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: 01/05/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/30/2024] Open
Abstract
Near-infrared spectroscopy (NIRS) provides a high-throughput phenotyping technique to assist breeding for improved faba bean seed quality. We combined chemical analysis of protein, oil content (and composition) with NIRS through chemometrics, employing Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB) as prediction models. Protein was the most reliably predicted trait (R2 = 0.96-0.98) across field trials, followed by oil (R2 = 0.82-0.86) and oleic acid (R2 = 0.31-0.68). Samples for training the models were selected using K-means clustering. The optimal statistical approach for prediction was compound-specific: PLS for protein (Root Mean Squared Error - RMSE = 0.46), BB for oil (RMSE = 0.067), and EN for oleic acid content (RMSE = 2.83). Reduced training set simulations revealed different effects on prediction accuracy depending on the model and compound. Several NIR regions were pinpointed as highly informative for the compounds, using the shrinkage and variable selection capabilities of EN and BB.
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Affiliation(s)
- Antonio Lippolis
- Plant Breeding, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB, Wageningen, the Netherlands
| | - Pamela Vega Polo
- Plant Breeding, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB, Wageningen, the Netherlands
| | - Guilherme de Sousa
- Plant Breeding, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB, Wageningen, the Netherlands
| | - Annemarie Dechesne
- Plant Breeding, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB, Wageningen, the Netherlands
| | - Laurice Pouvreau
- Wageningen Food & Biobased Research, Wageningen University & Research, Bornse Weilanden 9, 6708WG, Wageningen, the Netherlands
| | - Luisa M Trindade
- Plant Breeding, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB, Wageningen, the Netherlands
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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Ballesta P, Ahmar S, Lobos GA, Mieres-Castro D, Jiménez-Aspee F, Mora-Poblete F. Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus. FRONTIERS IN PLANT SCIENCE 2022; 13:871943. [PMID: 35432412 PMCID: PMC9008590 DOI: 10.3389/fpls.2022.871943] [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: 02/08/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.
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Affiliation(s)
- Paulina Ballesta
- The National Fund for Scientific and Technological Development, Talca, Chile
| | - Sunny Ahmar
- The National Fund for Scientific and Technological Development, Talca, Chile
| | - Gustavo A. Lobos
- Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, Universidad de Talca, Talca, Chile
| | | | - Felipe Jiménez-Aspee
- Department of Food Biofunctionality, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
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Zhu X, Maurer HP, Jenz M, Hahn V, Ruckelshausen A, Leiser WL, Würschum T. The performance of phenomic selection depends on the genetic architecture of the target trait. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:653-665. [PMID: 34807268 PMCID: PMC8866387 DOI: 10.1007/s00122-021-03997-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.
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Affiliation(s)
- Xintian Zhu
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Hans Peter Maurer
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Mario Jenz
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
- Hochschule Osnabrück, Sedanstr. 26, 49076, Osnabrück, Germany
| | - Volker Hahn
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | | | - Willmar L Leiser
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany.
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Van Tassel DL, DeHaan LR, Diaz-Garcia L, Hershberger J, Rubin MJ, Schlautman B, Turner K, Miller AJ. Re-imagining crop domestication in the era of high throughput phenomics. CURRENT OPINION IN PLANT BIOLOGY 2022; 65:102150. [PMID: 34883308 DOI: 10.1016/j.pbi.2021.102150] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
De novo domestication is an exciting option for increasing species diversity and ecosystem service functionality of agricultural landscapes. Genomic selection (GS), the application of genomic markers to predict phenotypic traits in a breeding population, offers the possibility of rapid genetic improvement, making GS especially attractive for modifying traits of long-lived species. However, for some wild species just entering the domestication pipeline, especially those with large and complex genomes, a lack of funding and/or prior genome characterization, GS is often out of reach. High throughput phenomics has the potential to augment traditional pedigree selection, reduce costs and amplify impacts of genomic selection, and even create new predictive selection approaches independent of sequencing or pedigrees.
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Affiliation(s)
| | - Lee R DeHaan
- The Land Institute, 2440 E Water Well Rd., Salina, KS, 67401, USA
| | | | - Jenna Hershberger
- The Land Institute, 2440 E Water Well Rd., Salina, KS, 67401, USA; Donald Danforth Plant Science Center, 975 North Warson Road, Saint Louis, MO, 63132, USA
| | - Matthew J Rubin
- Donald Danforth Plant Science Center, 975 North Warson Road, Saint Louis, MO, 63132, USA
| | | | - Kathryn Turner
- The Land Institute, 2440 E Water Well Rd., Salina, KS, 67401, USA
| | - Allison J Miller
- Donald Danforth Plant Science Center, 975 North Warson Road, Saint Louis, MO, 63132, USA; Saint Louis University Department of Biology, 3507 Laclede Avenue, St. Louis, MO, 63103, USA.
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Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations. Processes (Basel) 2021. [DOI: 10.3390/pr9111942] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R2) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R2 = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R2 = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R2 = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (<5%) and normal amylose (>15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.
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