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Torres-Rodríguez JV, Li D, Turkus J, Newton L, Davis J, Lopez-Corona L, Ali W, Sun G, Mural RV, Grzybowski MW, Zamft BM, Thompson AM, Schnable JC. Population-level gene expression can repeatedly link genes to functions in maize. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38812347 DOI: 10.1111/tpj.16801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024]
Abstract
Transcriptome-wide association studies (TWAS) can provide single gene resolution for candidate genes in plants, complementing genome-wide association studies (GWAS) but efforts in plants have been met with, at best, mixed success. We generated expression data from 693 maize genotypes, measured in a common field experiment, sampled over a 2-h period to minimize diurnal and environmental effects, using full-length RNA-seq to maximize the accurate estimation of transcript abundance. TWAS could identify roughly 10 times as many genes likely to play a role in flowering time regulation as GWAS conducted data from the same experiment. TWAS using mature leaf tissue identified known true-positive flowering time genes known to act in the shoot apical meristem, and trait data from a new environment enabled the identification of additional flowering time genes without the need for new expression data. eQTL analysis of TWAS-tagged genes identified at least one additional known maize flowering time gene through trans-eQTL interactions. Collectively these results suggest the gene expression resource described here can link genes to functions across different plant phenotypes expressed in a range of tissues and scored in different experiments.
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Affiliation(s)
- J Vladimir Torres-Rodríguez
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement, Key Laboratory of Crop Gene Resource and Germplasm Enhancement, Key Laboratory of Soybean Biology (Beijing), Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jonathan Turkus
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Linsey Newton
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, 48824, USA
| | - Jensina Davis
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Lina Lopez-Corona
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Waqar Ali
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Guangchao Sun
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Advanced Diagnostic Laboratory, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ravi V Mural
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, 57007, USA
| | - Marcin W Grzybowski
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Bradley M Zamft
- X, The Moonshot Factory, Mountain View, California, 94043, USA
| | - Addie M Thompson
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, Michigan, 48824, USA
| | - James C Schnable
- Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
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Durant PC, Bhasin A, Juenger TE, Heckman RW. Genetically correlated leaf tensile and morphological traits are driven by growing season length in a widespread perennial grass. AMERICAN JOURNAL OF BOTANY 2024; 111:e16349. [PMID: 38783552 DOI: 10.1002/ajb2.16349] [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: 10/23/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 05/25/2024]
Abstract
PREMISE Leaf tensile resistance, a leaf's ability to withstand pulling forces, is an important determinant of plant ecological strategies. One potential driver of leaf tensile resistance is growing season length. When growing seasons are long, strong leaves, which often require more time and resources to construct than weak leaves, may be more advantageous than when growing seasons are short. Growing season length and other ecological conditions may also impact the morphological traits that underlie leaf tensile resistance. METHODS To understand variation in leaf tensile resistance, we measured size-dependent leaf strength and size-independent leaf toughness in diverse genotypes of the widespread perennial grass Panicum virgatum (switchgrass) in a common garden. We then used quantitative genetic approaches to estimate the heritability of leaf tensile resistance and whether there were genetic correlations between leaf tensile resistance and other morphological traits. RESULTS Leaf tensile resistance was positively associated with aboveground biomass (a proxy for fitness). Moreover, both measures of leaf tensile resistance exhibited high heritability and were positively genetically correlated with leaf lamina thickness and leaf mass per area (LMA). Leaf tensile resistance also increased with the growing season length in the habitat of origin, and this effect was mediated by both LMA and leaf thickness. CONCLUSIONS Differences in growing season length may promote selection for different leaf lifespans and may explain existing variation in leaf tensile resistance in P. virgatum. In addition, the high heritability of leaf tensile resistance suggests that P. virgatum will be able to respond to climate change as growing seasons lengthen.
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Affiliation(s)
- P Camilla Durant
- Department of Integrated Biology, University of Texas at Austin, Austin, 78712, TX, USA
| | - Amit Bhasin
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, 78712, TX, USA
| | - Thomas E Juenger
- Department of Integrated Biology, University of Texas at Austin, Austin, 78712, TX, USA
| | - Robert W Heckman
- Department of Integrated Biology, University of Texas at Austin, Austin, 78712, TX, USA
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3
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Nowak B, Tomkowiak A, Sobiech A, Bocianowski J, Kowalczewski PŁ, Spychała J, Jamruszka T. Identification and Analysis of Candidate Genes Associated with Yield Structure Traits and Maize Yield Using Next-Generation Sequencing Technology. Genes (Basel) 2023; 15:56. [PMID: 38254946 PMCID: PMC10815399 DOI: 10.3390/genes15010056] [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: 12/08/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
The main challenge of agriculture in the 21st century is the continuous increase in food production. In addition to ensuring food security, the goal of modern agriculture is the continued development and production of plant-derived biomaterials. Conventional plant breeding methods do not allow breeders to achieve satisfactory results in obtaining new varieties in a short time. Currently, advanced molecular biology tools play a significant role worldwide, markedly contributing to biological progress. The aim of this study was to identify new markers linked to candidate genes determining grain yield. Next-generation sequencing, gene association, and physical mapping were used to identify markers. An additional goal was to also optimize diagnostic procedures to identify molecular markers on reference materials. As a result of the conducted research, 19 SNP markers significantly associated with yield structure traits in maize were identified. Five of these markers (28629, 28625, 28640, 28649, and 29294) are located within genes that can be considered candidate genes associated with yield traits. For two markers (28639 and 29294), different amplification products were obtained on the electrophorograms. For marker 28629, a specific product of 189 bp was observed for genotypes 1, 4, and 10. For marker 29294, a specific product of 189 bp was observed for genotypes 1 and 10. Both markers can be used for the preliminary selection of well-yielding genotypes.
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Affiliation(s)
- Bartosz Nowak
- Smolice Plant Breeding Ltd., IHAR Group, Smolice 146, 63-740 Kobylin, Poland;
| | - Agnieszka Tomkowiak
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
| | - Aleksandra Sobiech
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland;
| | - Przemysław Łukasz Kowalczewski
- Department of Food Technology of Plant Origin, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland;
| | - Julia Spychała
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
| | - Tomasz Jamruszka
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland; (A.S.); (J.S.); (T.J.)
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Chhotaray S, Vohra V, Uttam V, Santhosh A, Saxena P, Gahlyan RK, Gowane G. TWAS revealed significant causal loci for milk production and its composition in Murrah buffaloes. Sci Rep 2023; 13:22401. [PMID: 38104199 PMCID: PMC10725422 DOI: 10.1038/s41598-023-49767-x] [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: 01/20/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] Open
Abstract
Milk yield is the most complex trait in dairy animals, and mapping all causal variants even with smallest effect sizes has been difficult with the genome-wide association study (GWAS) sample sizes available in geographical regions with small livestock holdings such as Indian sub-continent. However, Transcriptome-wide association studies (TWAS) could serve as an alternate for fine mapping of expression quantitative trait loci (eQTLs). This is a maiden attempt to identify milk production and its composition related genes using TWAS in Murrah buffaloes (Bubalus bubalis). TWAS was conducted on a test (N = 136) set of Murrah buffaloes genotyped through ddRAD sequencing. Their gene expression level was predicted using reference (N = 8) animals having both genotype and mammary epithelial cell (MEC) transcriptome information. Gene expression prediction was performed using Elastic-Net and Dirichlet Process Regression (DPR) model with fivefold cross-validation and without any cross-validation. DPR model without cross-validation predicted 80.92% of the total genes in the test group of Murrah buffaloes which was highest compared to other methods. TWAS in test individuals based on predicted gene expression, identified a significant association of one unique gene for Fat%, and two for SNF% at Bonferroni corrected threshold. The false discovery rates (FDR) corrected P-values of the top ten SNPs identified through GWAS were comparatively higher than TWAS. Gene ontology of TWAS-identified genes was performed to understand the function of these genes, it was revealed that milk production and composition genes were mainly involved in Relaxin, AMPK, and JAK-STAT signaling pathway, along with CCRI, and several key metabolic processes. The present study indicates that TWAS offers a lower false discovery rate and higher significant hits than GWAS for milk production and its composition traits. Hence, it is concluded that TWAS can be effectively used to identify genes and cis-SNPs in a population, which can be used for fabricating a low-density genomic chip for predicting milk production in Murrah buffaloes.
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Affiliation(s)
- Supriya Chhotaray
- Division of Animal Genetics and Breeding, ICAR-Central Institute for Research on Buffaloes, Hisar, Haryana, 125001, India
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Vikas Vohra
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India.
| | - Vishakha Uttam
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Ameya Santhosh
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Punjika Saxena
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Rajesh Kumar Gahlyan
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Gopal Gowane
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
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Zhao P, Ma X, Zhang R, Cheng M, Niu Y, Shi X, Ji W, Xu S, Wang X. Integration of genome-wide association study, linkage analysis, and population transcriptome analysis to reveal the TaFMO1-5B modulating seminal root growth in bread wheat. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1385-1400. [PMID: 37713270 DOI: 10.1111/tpj.16432] [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: 03/23/2023] [Revised: 07/10/2023] [Accepted: 08/12/2023] [Indexed: 09/16/2023]
Abstract
Bread wheat, one of the keystone crops for global food security, is challenged by climate change and resource shortage. The root system plays a vital role in water and nutrient absorption, making it essential for meeting the growing global demand. Here, using an association-mapping population composed of 406 accessions, we identified QTrl.Rs-5B modulating seminal root development with a genome-wide association study and validated its genetic effects with two F5 segregation populations. Transcriptome-wide association study prioritized TaFMO1-5B, a gene encoding the flavin-containing monooxygenases, as the causal gene for QTrl.Rs-5B, whose expression levels correlate negatively with the phenotyping variations among our population. The lines silenced for TaFMO1-5B consistently showed significantly larger seminal roots in different genetic backgrounds. Additionally, the agriculture traits measured in multiple environments showed that QTrl.Rs-5B also affects yield component traits and plant architecture-related traits, and its favorable haplotype modulates these traits toward that of modern cultivars, suggesting the application potential of QTrl.Rs-5B for wheat breeding. Consistently, the frequency of the favorable haplotype of QTrl.Rs-5B increased with habitat expansion and breeding improvement of bread wheat. In conclusion, our findings identified and demonstrated the effects of QTrl.Rs-5B on seminal root development and illustrated that it is a valuable genetic locus for wheat root improvement.
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Affiliation(s)
- Peng Zhao
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Xiuyun Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Ruize Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Mingzhu Cheng
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Yaxin Niu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Xue Shi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Wanquan Ji
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Shengbao Xu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Xiaoming Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, Shaanxi, 712100, China
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Tanaka R, Wu D, Li X, Tibbs-Cortes LE, Wood JC, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Li X, Deason NT, Schoenbaum GR, Buell CR, DellaPenna D, Yu J, Gore MA. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. THE PLANT GENOME 2023; 16:e20276. [PMID: 36321716 DOI: 10.1002/tpg2.20276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.
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Affiliation(s)
- Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | | | - Joshua C Wood
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | | | - Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - John P Hamilton
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Brieanne Vaillancourt
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, 99164, USA
| | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | | | - C Robin Buell
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
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7
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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.
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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.
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8
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Chien PS, Chen PH, Lee CR, Chiou TJ. Transcriptome-wide association study coupled with eQTL analysis reveals the genetic connection between gene expression and flowering time in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5653-5666. [PMID: 37419660 DOI: 10.1093/jxb/erad262] [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: 12/07/2022] [Accepted: 07/06/2023] [Indexed: 07/09/2023]
Abstract
Genome-wide association study (GWAS) has improved our understanding of complex traits, but challenges exist in distinguishing causation versus association caused by linkage disequilibrium. Instead, transcriptome-wide association studies (TWAS) detect direct associations between expression levels and phenotypic variations, providing an opportunity to better prioritize candidate genes. To assess the feasibility of TWAS, we investigated the association between transcriptomes, genomes, and various traits in Arabidopsis, including flowering time. The associated genes formerly known to regulate growth allometry or metabolite production were first identified by TWAS. Next, for flowering time, six TWAS-newly identified genes were functionally validated. Analysis of the expression quantitative trait locus (eQTL) further revealed a trans-regulatory hotspot affecting the expression of several TWAS-identified genes. The hotspot covers the FRIGIDA (FRI) gene body, which possesses multiple haplotypes differentially affecting the expression of downstream genes, such as FLOWERING LOCUS C (FLC) and SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1). We also revealed multiple independent paths towards the loss of function of FRI in natural accessions. Altogether, this study demonstrates the potential of combining TWAS with eQTL analysis to identify important regulatory modules of FRI-FLC-SOC1 for quantitative traits in natural populations.
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Affiliation(s)
- Pei-Shan Chien
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Pin-Hua Chen
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Cheng-Ruei Lee
- Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan
- Institute of Plant Biology, National Taiwan University, Taipei, Taiwan
| | - Tzyy-Jen Chiou
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
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9
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Liang M, An B, Deng T, Du L, Li K, Cao S, Du Y, Xu L, Zhang L, Gao X, Cao Y, Zhao Y, Li J, Gao H. Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle. Front Genet 2023; 13:982433. [PMID: 36685878 PMCID: PMC9852892 DOI: 10.3389/fgene.2022.982433] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Locating the genetic variation of important livestock and poultry economic traits is essential for genetic improvement in breeding programs. Identifying the candidate genes for the productive ability of Huaxi cattle was one crucial element for practical breeding. Based on the genotype and phenotype data of 1,478 individuals and the RNA-seq data of 120 individuals contained in 1,478 individuals, we implemented genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and Fisher's combined test (FCT) to identify the candidate genes for the carcass trait, the weight of longissimus dorsi muscle (LDM). The results indicated that GWAS, TWAS, and FCT identified seven candidate genes for LDM altogether: PENK was located by GWAS and FCT, PPAT was located by TWAS and FCT, and XKR4, MTMR3, FGFRL1, DHRS4, and LAP3 were only located by one of the methods. After functional analysis of these candidate genes and referring to the reported studies, we found that they were mainly functional in the progress of the development of the body and the growth of muscle cells. Combining advanced breeding techniques such as gene editing with our study will significantly accelerate the genetic improvement for the future breeding of Huaxi cattle.
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Affiliation(s)
- Mang Liang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bingxing An
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tianyu Deng
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lili Du
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Keanning Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Sheng Cao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yueying Du
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xue Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yang Cao
- Jilin Academy of Agricultural Sciences, Changchun, China
| | - Yuming Zhao
- Jilin Academy of Agricultural Sciences, Changchun, China
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Huijiang Gao,
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10
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Mahmood U, Li X, Fan Y, Chang W, Niu Y, Li J, Qu C, Lu K. Multi-omics revolution to promote plant breeding efficiency. FRONTIERS IN PLANT SCIENCE 2022; 13:1062952. [PMID: 36570904 PMCID: PMC9773847 DOI: 10.3389/fpls.2022.1062952] [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: 10/06/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.
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Affiliation(s)
- Umer Mahmood
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Xiaodong Li
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yonghai Fan
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Wei Chang
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yue Niu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Jiana Li
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Cunmin Qu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Kun Lu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
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11
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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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12
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Hansen PB, Ruud AK, de los Campos G, Malinowska M, Nagy I, Svane SF, Thorup-Kristensen K, Jensen JD, Krusell L, Asp T. Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare. PLANTS 2022; 11:plants11172190. [PMID: 36079572 PMCID: PMC9459846 DOI: 10.3390/plants11172190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/19/2022] [Accepted: 08/21/2022] [Indexed: 11/30/2022]
Abstract
Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72–0.91) than with genomic models alone (0.55–0.86). The correlation between predictions and phenotypes varied from 0.17–0.28 for control plants and 0.23–0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.
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Affiliation(s)
- Pernille Bjarup Hansen
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
- Correspondence: (P.B.H.); (T.A.); Tel.: +45-87158243 (T.A.)
| | - Anja Karine Ruud
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Gustavo de los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Marta Malinowska
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Simon Fiil Svane
- Section for Crop Sciences, Department of Plant and Environmental Sciences, Copenhagen University, 2630 Taastrup, Denmark
| | - Kristian Thorup-Kristensen
- Section for Crop Sciences, Department of Plant and Environmental Sciences, Copenhagen University, 2630 Taastrup, Denmark
| | | | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700 Horsens, Denmark
| | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
- Correspondence: (P.B.H.); (T.A.); Tel.: +45-87158243 (T.A.)
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13
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Lin M, Qiao P, Matschi S, Vasquez M, Ramstein GP, Bourgault R, Mohammadi M, Scanlon MJ, Molina I, Smith LG, Gore MA. Integrating GWAS and TWAS to elucidate the genetic architecture of maize leaf cuticular conductance. PLANT PHYSIOLOGY 2022; 189:2144-2158. [PMID: 35512195 PMCID: PMC9342973 DOI: 10.1093/plphys/kiac198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/28/2022] [Indexed: 05/11/2023]
Abstract
The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed. Dissecting the genetic architecture of natural variation for maize (Zea mays L.) leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we performed an integrated genome- and transcriptome-wide association studies (GWAS and TWAS) to identify candidate genes putatively regulating variation in leaf gc. Of the 22 plausible candidate genes identified, 4 were predicted to be involved in cuticle precursor biosynthesis and export, 2 in cell wall modification, 9 in intracellular membrane trafficking, and 7 in the regulation of cuticle development. A gene encoding an INCREASED SALT TOLERANCE1-LIKE1 (ISTL1) protein putatively involved in intracellular protein and membrane trafficking was identified in GWAS and TWAS as the strongest candidate causal gene. A set of maize nested near-isogenic lines that harbor the ISTL1 genomic region from eight donor parents were evaluated for gc, confirming the association between gc and ISTL1 in a haplotype-based association analysis. The findings of this study provide insights into the role of regulatory variation in the development of the maize leaf cuticle and will ultimately assist breeders to develop drought-tolerant maize for target environments.
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Affiliation(s)
- Meng Lin
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Pengfei Qiao
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | | | - Miguel Vasquez
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California 92093, USA
| | | | - Richard Bourgault
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Marc Mohammadi
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Michael J Scanlon
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Isabel Molina
- Department of Biology, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada
| | - Laurie G Smith
- Section of Cell and Developmental Biology, University of California San Diego, La Jolla, California 92093, USA
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14
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Wu D, Li X, Tanaka R, Wood JC, Tibbs-Cortes LE, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Diepenbrock CH, Li X, Deason NT, Schoenbaum GR, Yu J, Buell CR, DellaPenna D, Gore MA. Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics 2022; 221:6603118. [PMID: 35666198 PMCID: PMC9339294 DOI: 10.1093/genetics/iyac091] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/01/2022] [Indexed: 11/20/2022] Open
Abstract
Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.
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Affiliation(s)
| | | | | | - Joshua C Wood
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Maria Magallanes-Lundback
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Nolan Bornowski
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - John P Hamilton
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Brieanne Vaillancourt
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | | | - Xianran Li
- United States Department of Agriculture, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Nicholas T Deason
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - C Robin Buell
- Department of Crop & Soil Sciences, Institute of Plant Breeding, Genetics, & Genomics, University of Georgia, Athens, GA 30602, USA
| | - Dean DellaPenna
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Michael A Gore
- Corresponding author: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
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15
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Hershberger J, Tanaka R, Wood JC, Kaczmar N, Wu D, Hamilton JP, DellaPenna D, Buell CR, Gore MA. Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels. THE PLANT GENOME 2022; 15:e20197. [PMID: 35262278 DOI: 10.1002/tpg2.20197] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/23/2022] [Indexed: 06/14/2023]
Abstract
Sweet corn (Zea mays L.) is consistently one of the most highly consumed vegetables in the United States, providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step toward understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.
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Affiliation(s)
- Jenna Hershberger
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Joshua C Wood
- Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Nicholas Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - John P Hamilton
- Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - C Robin Buell
- Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
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16
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Yang Z, Xu G, Zhang Q, Obata T, Yang J. Genome-wide mediation analysis: an empirical study to connect phenotype with genotype via intermediate transcriptomic data in maize. Genetics 2022; 221:6572813. [PMID: 35460234 PMCID: PMC9157066 DOI: 10.1093/genetics/iyac057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping genotype to phenotype is an essential topic in genetics and genomics research. As the Omics data become increasingly available, 2-variable methods have been widely applied to associate genotype with the phenotype (genome-wide association study), gene expression with the phenotype (transcriptome-wide association study), and genotype with gene expression. However, signals detected by these 2-variable association methods suffer from low mapping resolution or inexplicit causality between genotype and phenotype, making it challenging to interpret and validate the molecular mechanisms of the underlying genomic variations and the candidate genes. Under the context of genetics research, we hypothesized a causal chain from genotype to phenotype partially mediated by intermediate molecular processes, i.e. gene expression. To test this hypothesis, we applied the high-dimensional mediation analysis, a class of causal inference method with an assumed causal chain from the exposure to the mediator to the outcome, and implemented it with a maize association panel (N = 280 lines). Using 40 publicly available agronomy traits, 66 newly generated metabolite traits, and published RNA-seq data from 7 different tissues, our empirical study detected 736 unique mediating genes. Noticeably, 83/736 (11%) genes were identified in mediating more than 1 trait, suggesting the prevalence of pleiotropic mediating effects. We demonstrated that several identified mediating genes are consistent with their known functions. In addition, our results provided explicit hypotheses for functional validation and suggested that the mediation analysis is a powerful tool to integrate Omics data to connect genotype to phenotype.
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Affiliation(s)
- Zhikai Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA,Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Gen Xu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA,Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH 03824, USA
| | - Toshihiro Obata
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA,Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Jinliang Yang
- Corresponding author: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
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17
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Ferrero-Serrano Á, Sylvia MM, Forstmeier PC, Olson AJ, Ware D, Bevilacqua PC, Assmann SM. Experimental demonstration and pan-structurome prediction of climate-associated riboSNitches in Arabidopsis. Genome Biol 2022; 23:101. [PMID: 35440059 PMCID: PMC9017077 DOI: 10.1186/s13059-022-02656-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/20/2022] [Indexed: 11/23/2022] Open
Abstract
Background Genome-wide association studies (GWAS) aim to correlate phenotypic changes with genotypic variation. Upon transcription, single nucleotide variants (SNVs) may alter mRNA structure, with potential impacts on transcript stability, macromolecular interactions, and translation. However, plant genomes have not been assessed for the presence of these structure-altering polymorphisms or “riboSNitches.” Results We experimentally demonstrate the presence of riboSNitches in transcripts of two Arabidopsis genes, ZINC RIBBON 3 (ZR3) and COTTON GOLGI-RELATED 3 (CGR3), which are associated with continentality and temperature variation in the natural environment. These riboSNitches are also associated with differences in the abundance of their respective transcripts, implying a role in regulating the gene's expression in adaptation to local climate conditions. We then computationally predict riboSNitches transcriptome-wide in mRNAs of 879 naturally inbred Arabidopsis accessions. We characterize correlations between SNPs/riboSNitches in these accessions and 434 climate descriptors of their local environments, suggesting a role of these variants in local adaptation. We integrate this information in CLIMtools V2.0 and provide a new web resource, T-CLIM, that reveals associations between transcript abundance variation and local environmental variation. Conclusion We functionally validate two plant riboSNitches and, for the first time, demonstrate riboSNitch conditionality dependent on temperature, coining the term “conditional riboSNitch.” We provide the first pan-genome-wide prediction of riboSNitches in plants. We expand our previous CLIMtools web resource with riboSNitch information and with 1868 additional Arabidopsis genomes and 269 additional climate conditions, which will greatly facilitate in silico studies of natural genetic variation, its phenotypic consequences, and its role in local adaptation. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02656-4.
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Affiliation(s)
- Ángel Ferrero-Serrano
- Department of Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA.
| | - Megan M Sylvia
- Department of Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Peter C Forstmeier
- Department of Biochemistry, Microbiology, and Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Andrew J Olson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.,USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
| | - Philip C Bevilacqua
- Department of Biochemistry, Microbiology, and Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA.,Department of Chemistry, Pennsylvania State University, University Park, State College, PA, 16802, USA.,Center for RNA Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Sarah M Assmann
- Department of Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA. .,Center for RNA Molecular Biology, Pennsylvania State University, University Park, State College, PA, 16802, USA.
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18
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Zhang Y, Zhang H, Zhao H, Xia Y, Zheng X, Fan R, Tan Z, Duan C, Fu Y, Li L, Ye J, Tang S, Hu H, Xie W, Yao X, Guo L. Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus. Genome Biol 2022; 23:86. [PMID: 35346318 PMCID: PMC8962237 DOI: 10.1186/s13059-022-02647-5] [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: 11/24/2021] [Accepted: 03/07/2022] [Indexed: 01/01/2023] Open
Abstract
Background Brassica napus is an important vegetable oil source worldwide. Seed coat content is a complex quantitative trait that negatively correlates with the seed oil content in B. napus. Results Here we provide insights into the genetic basis of natural variation of seed coat content by transcriptome-wide association studies (TWAS) and genome-wide association studies (GWAS) using 382 B. napus accessions. By population transcriptomic analysis, we identify more than 700 genes and four gene modules that are significantly associated with seed coat content. We also characterize three reliable quantitative trait loci (QTLs) controlling seed coat content by GWAS. Combining TWAS and correlation networks of seed coat content-related gene modules, we find that BnaC07.CCR-LIKE (CCRL) and BnaTT8s play key roles in the determination of the trait by modulating lignin biosynthesis. By expression GWAS analysis, we identify a regulatory hotspot on chromosome A09, which is involved in controlling seed coat content through BnaC07.CCRL and BnaTT8s. We then predict the downstream genes regulated by BnaTT8s using multi-omics datasets. We further experimentally validate that BnaCCRL and BnaTT8 positively regulate seed coat content and lignin content. BnaCCRL represents a novel identified gene involved in seed coat development. Furthermore, we also predict the key genes regulating carbon allocation between phenylpropane compounds and oil during seed development in B. napus. Conclusions This study helps us to better understand the complex machinery of seed coat development and provides a genetic resource for genetic improvement of seed coat content in B. napus breeding. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02647-5.
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19
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Bari MAA, Zheng P, Viera I, Worral H, Szwiec S, Ma Y, Main D, Coyne CJ, McGee RJ, Bandillo N. Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction. Front Genet 2022; 12:707754. [PMID: 35003202 PMCID: PMC8740293 DOI: 10.3389/fgene.2021.707754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
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Affiliation(s)
- Md Abdullah Al Bari
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ping Zheng
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Indalecio Viera
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Hannah Worral
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Stephen Szwiec
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Clarice J Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State University, Pullman, WA, United States
| | - Rebecca J McGee
- USDA-ARS Grain Legume Genetics and Physiology Research, Pullman, WA, United States
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
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20
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Albert E, Sauvage C. Identification and Validation of Candidate Genes from Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:249-272. [PMID: 35641769 DOI: 10.1007/978-1-0716-2237-7_15] [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] [Indexed: 06/15/2023]
Abstract
Exploiting the statistical associations coming out from a GWAS experiment to identify and validate candidate genes may be potentially difficult and time consuming. To fill the gap between the identification of candidate genes toward their functional validation onto the trait performance, the prioritization of variants underlying the GWAS-associated regions is necessary. In parallel, recent developments in genomics and statistical methods have been achieved notably in human genetic and they are accordingly being adopted in plant breeding toward the study of the genetic architecture of traits to sustain genetic gains. In this chapter, we aim at providing both theoretical and practical aspects underlying three main options including (1) the MetaGWAS analysis, (2) the statistical fine mapping and (3) the integration of functional data toward the identification and validation of candidate genes from a GWAS experiment.
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21
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Zhang X, Zhu Y, Kremling KAG, Romay MC, Bukowski R, Sun Q, Gao S, Buckler ES, Lu F. Genome-wide analysis of deletions in maize population reveals abundant genetic diversity and functional impact. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:273-290. [PMID: 34661697 DOI: 10.1007/s00122-021-03965-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Abstract
Two read depth methods were jointly used in next-generation sequencing data to identify deletions in maize population. GWAS by deletions were analyzed for gene expression pattern and classical traits, respectively. Many studies have confirmed that structural variation (SV) is pervasive throughout the maize genome. Deletion is one type of SV that may impact gene expression and cause phenotypic changes in quantitative traits. In this study, two read count approaches were used to analyze the deletions in the whole-genome sequencing data of 270 maize inbred lines. A total of 19,754 deletion windows overlapped 12,751 genes, which were unevenly distributed across the genome. The deletions explained population structure well and correlated with genomic features. The deletion proportion of genes was determined to be negatively correlated with its expression. The detection of gene expression quantitative trait loci (eQTL) indicated that local eQTL were fewer but had larger effects than distant ones. The common associated genes were related to basic metabolic processes, whereas unique associated genes with eQTL played a role in the stress or stimulus responses in multiple tissues. Compared with the eQTL detected by SNPs derived from the same sequencing data, 89.4% of the associated genes could be detected by both markers. The effect of top eQTL detected by SNPs was usually larger than that detected by deletions for the same gene. A genome-wide association study (GWAS) on flowering time and plant height illustrated that only a few loci could be consistently captured by SNPs, suggesting that combining deletion and SNP for GWAS was an excellent strategy to dissect trait architecture. Our findings will provide insights into characteristic and biological function of genome-wide deletions in maize.
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Affiliation(s)
- Xiao Zhang
- Maize Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, China.
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Ministry of Agriculture, Chengdu, Sichuan, China.
- Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, NY, USA.
| | - Yonghui Zhu
- Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, Sichuan, China
| | - Karl A G Kremling
- Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, NY, USA
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, NY, USA
| | - Robert Bukowski
- Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, USA
| | - Qi Sun
- Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, USA
| | - Shibin Gao
- Maize Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, China
- Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Ministry of Agriculture, Chengdu, Sichuan, China
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, NY, USA
- USDA-ARS, R. W. Holley Center, Cornell University, Ithaca, NY, USA
| | - Fei Lu
- Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, NY, USA.
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China.
- CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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22
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Nutritional improvement of cereal crops to combat hidden hunger during COVID-19 pandemic: Progress and prospects. ADVANCES IN FOOD SECURITY AND SUSTAINABILITY 2022. [PMCID: PMC8917837 DOI: 10.1016/bs.af2s.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
COVID-19 has posed a severe challenge on food security by limiting access to food for the marginally placed population. While access to food is a challenge, access to nutritional food is a greater challenge to the population. The present-day foods are not sufficient to meet the nutritional requirements of the human body. In a pandemic condition, providing nutritious food to the population is imperative to ensure the health and well-being of humankind. Exploiting the existing biodiversity of crop species and deploying classical and modern tools to improve the nutritional potential of these species holds the key to addressing the above challenge. Breeding has been a classical tool of crop improvement that relied predominantly on genetic diversity. Collecting and conserving diverse germplasms and characterizing their diversity using molecular markers is essential to preserve diversity and use them in genetic improvement programs. These markers are also valuable for association mapping analyses to identify the genetic determinants of traits-of-interest in crop species. Association mapping identifies the quantitative trait loci (QTL) underlying the trait-of-interest by exploring marker-trait associations, and these QTLs can further be exploited for the genetic improvement of cultivated species through genomics-assisted breeding. Conventional breeding and genomics approaches are also being applied to develop biofortified cereal crops to reduce nutritional deficiencies in consumers. In this context, chapter explains the prerequisites for association mapping, population structure, genetic diversity, different approaches of performing association mapping to dissect nutritional traits, use the information for genomics-assisted breeding for nutrient-rich cereal crops, and application of genomics strategies in crop biofortification. These approaches will ensure food and nutrition security for all amidst the current COVID-19 crisis.
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23
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Pignon CP, Fernandes SB, Valluru R, Bandillo N, Lozano R, Buckler E, Gore MA, Long SP, Brown PJ, Leakey ADB. Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. PLANT PHYSIOLOGY 2021; 187:2544-2562. [PMID: 34618072 PMCID: PMC8644692 DOI: 10.1093/plphys/kiab395] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/26/2021] [Indexed: 05/07/2023]
Abstract
Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.
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Affiliation(s)
- Charles P Pignon
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Samuel B Fernandes
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN1 3QE, UK
| | - Nonoy Bandillo
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota 58105, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Edward Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
- United States Department of Agriculture, Agricultural Research Service (USDA-ARS) R.W. Holley Center for Agriculture and Health, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Stephen P Long
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster LA1 1YX, UK
| | - Patrick J Brown
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Andrew D B Leakey
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Author for communication:
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24
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Castano-Duque L, Gilbert MK, Mack BM, Lebar MD, Carter-Wientjes CH, Sickler CM, Cary JW, Rajasekaran K. Flavonoids Modulate the Accumulation of Toxins From Aspergillus flavus in Maize Kernels. FRONTIERS IN PLANT SCIENCE 2021; 12:761446. [PMID: 34899785 PMCID: PMC8662736 DOI: 10.3389/fpls.2021.761446] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/15/2021] [Indexed: 06/14/2023]
Abstract
Aspergillus flavus is an opportunistic fungal pathogen capable of producing aflatoxins, potent carcinogenic toxins that accumulate in maize kernels after infection. To better understand the molecular mechanisms of maize resistance to A. flavus growth and aflatoxin accumulation, we performed a high-throughput transcriptomic study in situ using maize kernels infected with A. flavus strain 3357. Three maize lines were evaluated: aflatoxin-contamination resistant line TZAR102, semi-resistant MI82, and susceptible line Va35. A modified genotype-environment association method (GEA) used to detect loci under selection via redundancy analysis (RDA) was used with the transcriptomic data to detect genes significantly influenced by maize line, fungal treatment, and duration of infection. Gene ontology enrichment analysis of genes highly expressed in infected kernels identified molecular pathways associated with defense responses to fungi and other microbes such as production of pathogenesis-related (PR) proteins and lipid bilayer formation. To further identify novel genes of interest, we incorporated genomic and phenotypic field data from a genome wide association analysis with gene expression data, allowing us to detect significantly expressed quantitative trait loci (eQTL). These results identified significant association between flavonoid biosynthetic pathway genes and infection by A. flavus. In planta fungal infections showed that the resistant line, TZAR102, has a higher fold increase of the metabolites naringenin and luteolin than the susceptible line, Va35, when comparing untreated and fungal infected plants. These results suggest flavonoids contribute to plant resistance mechanisms against aflatoxin contamination through modulation of toxin accumulation in maize kernels.
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25
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Ferguson JN, Fernandes SB, Monier B, Miller ND, Allen D, Dmitrieva A, Schmuker P, Lozano R, Valluru R, Buckler ES, Gore MA, Brown PJ, Spalding EP, Leakey ADB. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. PLANT PHYSIOLOGY 2021; 187:1481-1500. [PMID: 34618065 PMCID: PMC9040483 DOI: 10.1093/plphys/kiab346] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/29/2021] [Indexed: 05/04/2023]
Abstract
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
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Affiliation(s)
- John N Ferguson
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin
53706, USA
| | - Dylan Allen
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Anna Dmitrieva
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Peter Schmuker
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
- Present address: Lincoln Institute for Agri-Food Technology,
University of Lincoln, Lincoln LN2 2LG, UK
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New
York 14853, USA
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
Cornell University, Ithaca, New York 14853, USA
| | - Patrick J Brown
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Present address: Section of Agricultural Plant Biology,
Department of Plant Sciences, University of California Davis, California 95616,
USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin
53706, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Crop Sciences, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Plant Biology, University of Illinois at
Urbana-Champaign, Urbana, Illinois 61901, USA
- Author for communication: ,
Present address: Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA,
UK
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26
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Ferguson JN, Fernandes SB, Monier B, Miller ND, Allen D, Dmitrieva A, Schmuker P, Lozano R, Valluru R, Buckler ES, Gore MA, Brown PJ, Spalding EP, Leakey ADB. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. PLANT PHYSIOLOGY 2021; 187:1481-1500. [PMID: 34618065 DOI: 10.1093/plphys/kiab34] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 06/29/2021] [Indexed: 05/27/2023]
Abstract
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
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Affiliation(s)
- John N Ferguson
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Nathan D Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Dylan Allen
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Anna Dmitrieva
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Peter Schmuker
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Ravi Valluru
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
| | - Patrick J Brown
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
| | - Edgar P Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
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27
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Varshney RK, Bohra A, Roorkiwal M, Barmukh R, Cowling WA, Chitikineni A, Lam HM, Hickey LT, Croser JS, Bayer PE, Edwards D, Crossa J, Weckwerth W, Millar H, Kumar A, Bevan MW, Siddique KHM. Fast-forward breeding for a food-secure world. Trends Genet 2021; 37:1124-1136. [PMID: 34531040 DOI: 10.1016/j.tig.2021.08.002] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.
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Affiliation(s)
- Rajeev K Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch WA 6150, Western Australia, Australia; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
| | - Abhishek Bohra
- ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India
| | - Manish Roorkiwal
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India; The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Rutwik Barmukh
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Wallace A Cowling
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Annapurna Chitikineni
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Hon-Ming Lam
- School of Life Sciences and Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, QLD, Australia
| | - Janine S Croser
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Philipp E Bayer
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - David Edwards
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; School of Biological Sciences, The University of Western Australia, Crawley, WA, Australia
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
| | - Harvey Millar
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Crawley, WA, Australia
| | - Arvind Kumar
- Deputy Director General's Office, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | | | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
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28
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Sales CRG, Wang Y, Evers JB, Kromdijk J. Improving C4 photosynthesis to increase productivity under optimal and suboptimal conditions. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5942-5960. [PMID: 34268575 PMCID: PMC8411859 DOI: 10.1093/jxb/erab327] [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: 03/12/2021] [Accepted: 07/09/2021] [Indexed: 05/05/2023]
Abstract
Although improving photosynthetic efficiency is widely recognized as an underutilized strategy to increase crop yields, research in this area is strongly biased towards species with C3 photosynthesis relative to C4 species. Here, we outline potential strategies for improving C4 photosynthesis to increase yields in crops by reviewing the major bottlenecks limiting the C4 NADP-malic enzyme pathway under optimal and suboptimal conditions. Recent experimental results demonstrate that steady-state C4 photosynthesis under non-stressed conditions can be enhanced by increasing Rubisco content or electron transport capacity, both of which may also stimulate CO2 assimilation at supraoptimal temperatures. Several additional putative bottlenecks for photosynthetic performance under drought, heat, or chilling stress or during photosynthetic induction await further experimental verification. Based on source-sink interactions in maize, sugarcane, and sorghum, alleviating these photosynthetic bottlenecks during establishment and growth of the harvestable parts are likely to improve yield. The expected benefits are also shown to be augmented by the increasing trend in planting density, which increases the impact of photosynthetic source limitation on crop yields.
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Affiliation(s)
- Cristina R G Sales
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Yu Wang
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jochem B Evers
- Centre for Crops Systems Analysis (WUR), Wageningen University, Wageningen, The Netherlands
| | - Johannes Kromdijk
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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29
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Li D, Liu Q, Schnable PS. TWAS results are complementary to and less affected by linkage disequilibrium than GWAS. PLANT PHYSIOLOGY 2021; 186:1800-1811. [PMID: 33823025 PMCID: PMC8331151 DOI: 10.1093/plphys/kiab161] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 03/12/2021] [Indexed: 06/12/2023]
Abstract
A genome-wide association study (GWAS) is used to identify genetic markers associated with phenotypic variation. In contrast, a transcriptome-wide association study (TWAS) detects associations between gene expression levels and phenotypic variation. It has previously been shown that in the cross-pollinated species, maize (Zea mays), GWAS, and TWAS identify complementary sets of trait-associated genes, many of which exhibit characteristics of true positives. Here, we extend this conclusion to the self-pollinated species, Arabidopsis thaliana and soybean (Glycine max). Linkage disequilibrium (LD) can result in the identification, via GWAS, of false-positive associations. In all three analyzed plant species, most trait-associated genes identified via TWAS are well separated physically from other candidate genes. Hence, TWAS is less affected by LD than is GWAS, demonstrating that TWAS is particularly well suited for association studies in genomes with slow rates of LD decay, such as soybean. TWAS is reasonably robust to the plant organs/tissues used to determine expression levels. In summary, this study confirms that TWAS is a promising approach for accurate gene-level association mapping in plants that is complementary to GWAS, and established that TWAS can exhibit substantial advantages relative to GWAS in species with slow rates of LD decay.
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Affiliation(s)
- Delin Li
- Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, China
- Data Biotech (Beijing) Co. Ltd., Beijing, 100085, China
- National Key Facility for Gene Resources and Genetic Improvement, Key Lab of Crop Germplasm Utilization, Ministry of Agriculture, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, 100081, China
| | - Qiang Liu
- Department of Agronomy, Iowa State University, Ames, Iowa 50011-3650, USA
| | - Patrick S Schnable
- Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, China
- Department of Agronomy, Iowa State University, Ames, Iowa 50011-3650, USA
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30
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Miculan M, Nelissen H, Ben Hassen M, Marroni F, Inzé D, Pè ME, Dell’Acqua M. A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays). THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 107:1056-1071. [PMID: 34087008 PMCID: PMC8519057 DOI: 10.1111/tpj.15364] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/31/2021] [Indexed: 05/13/2023]
Abstract
The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript-trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript-trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.
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Affiliation(s)
- Mara Miculan
- Institute of Life SciencesScuola Superiore Sant’AnnaPisa56127Italy
| | - Hilde Nelissen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Manel Ben Hassen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Fabio Marroni
- IGA Technology ServicesUdine33100Italy
- Department of Agricultural, FoodAT, Environmental and Animal Sciences (DI4A)University of UdineUdine33100Italy
| | - Dirk Inzé
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhent9052Belgium
- Center for Plant Systems Biology, VIBGhent9052Belgium
| | - Mario Enrico Pè
- Institute of Life SciencesScuola Superiore Sant’AnnaPisa56127Italy
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31
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Blanc J, Kremling KAG, Buckler E, Josephs EB. Local adaptation contributes to gene expression divergence in maize. G3-GENES GENOMES GENETICS 2021; 11:6114460. [PMID: 33604670 PMCID: PMC8022924 DOI: 10.1093/g3journal/jkab004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/20/2020] [Indexed: 11/14/2022]
Abstract
Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.
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Affiliation(s)
- Jennifer Blanc
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Karl A G Kremling
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Inari Agriculture, Cambridge, MA 02139, USA
| | - Edward Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Emily B Josephs
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA.,Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI 48824, USA
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32
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Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. TRENDS IN PLANT SCIENCE 2021; 26:631-649. [PMID: 33893045 DOI: 10.1016/j.tplants.2021.03.010] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 05/18/2023]
Abstract
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.
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Affiliation(s)
- Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crops Plant Research (IPK), Gatersleben, Germany
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Mark E Sorrells
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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33
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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34
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Yu GE, Shin Y, Subramaniyam S, Kang SH, Lee SM, Cho C, Lee SS, Kim CK. Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus. Sci Rep 2021; 11:8019. [PMID: 33850210 PMCID: PMC8044237 DOI: 10.1038/s41598-021-87281-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/24/2021] [Indexed: 11/27/2022] Open
Abstract
Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.
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Affiliation(s)
- Go-Eun Yu
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea
| | - Younhee Shin
- Research and Development Center, Insilicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea
| | | | - Sang-Ho Kang
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea
| | - Si-Myung Lee
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea
| | - Chuloh Cho
- Crop Foundation Research Division, National Institute of Crop Science, RDA, Wanju, 55365, Korea
| | - Seung-Sik Lee
- Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, 29 Geumgu-gil, Jeongeup, 56212, Korea.,Department of Radiation Science and Technology, University of Science and Technology, Daejeon, 34113, Korea
| | - Chang-Kug Kim
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, 54874, Korea.
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35
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Yang N, Yan J. New genomic approaches for enhancing maize genetic improvement. CURRENT OPINION IN PLANT BIOLOGY 2021; 60:101977. [PMID: 33418269 DOI: 10.1016/j.pbi.2020.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/07/2020] [Accepted: 11/16/2020] [Indexed: 05/13/2023]
Abstract
Maize (Zea mays) is one of the most widely grown crops in the world, with an annual global production of over 1147 million tons. Genomics approaches are thought to be the best solution for accelerating yield improvement to meet the challenges of a growing population and global climate change. Here, we review current approaches to the exploration of novel genetic variation in genomes, DNA modifications, and transcription levels of cultivated maize, landraces, and wild relatives. We discuss applications of genetic engineering to maize yield improvement and highlight future directions for maize genomics studies.
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Affiliation(s)
- Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
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36
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Campbell MT, Hu H, Yeats TH, Brzozowski LJ, Caffe-Treml M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices. Front Genet 2021; 12:643733. [PMID: 33868378 PMCID: PMC8044359 DOI: 10.3389/fgene.2021.643733] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.
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Affiliation(s)
- Malachy T. Campbell
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Haixiao Hu
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Trevor H. Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Lauren J. Brzozowski
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Melanie Caffe-Treml
- Seed Technology Lab 113, Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, United States
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, United States
| | - Kevin P. Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - Mark E. Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Michael A. Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
- R.W. Holley Center for Agriculture & Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY, United States
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37
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Tibbs Cortes L, Zhang Z, Yu J. Status and prospects of genome-wide association studies in plants. THE PLANT GENOME 2021; 14:e20077. [PMID: 33442955 DOI: 10.1002/tpg2.20077] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 11/18/2020] [Indexed: 05/22/2023]
Abstract
Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.
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Affiliation(s)
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, 50010, USA
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38
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Lan Y, Sun R, Ouyang J, Ding W, Kim MJ, Wu J, Li Y, Shi T. AtMAD: Arabidopsis thaliana multi-omics association database. Nucleic Acids Res 2021; 49:D1445-D1451. [PMID: 33219693 PMCID: PMC7778929 DOI: 10.1093/nar/gkaa1042] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/08/2020] [Accepted: 10/21/2020] [Indexed: 12/22/2022] Open
Abstract
Integration analysis of multi-omics data provides a comprehensive landscape for understanding biological systems and mechanisms. The abundance of high-quality multi-omics data (genomics, transcriptomics, methylomics and phenomics) for the model organism Arabidopsis thaliana enables scientists to study the genetic mechanism of many biological processes. However, no resource is available to provide comprehensive and systematic multi-omics associations for Arabidopsis. Here, we developed an Arabidopsis thaliana Multi-omics Association Database (AtMAD, http://www.megabionet.org/atmad), a public repository for large-scale measurements of associations between genome, transcriptome, methylome, pathway and phenotype in Arabidopsis, designed for facilitating identification of eQTL, emQTL, Pathway-mQTL, Phenotype-pathway, GWAS, TWAS and EWAS. Candidate variants/methylations/genes were identified in AtMAD for specific phenotypes or biological processes, many of them are supported by experimental evidence. Based on the multi-omics association strategy, we have identified 11 796 cis-eQTLs and 10 119 trans-eQTLs. Among them, 68 837 environment-eQTL associations and 149 622 GWAS-eQTL associations were identified and stored in AtMAD. For expression–methylation quantitative trait loci (emQTL), we identified 265 776 emQTLs and 122 344 pathway-mQTLs. For TWAS and EWAS, we obtained 62 754 significant phenotype-gene associations and 3 993 379 significant phenotype-methylation associations, respectively. Overall, the multi-omics associated network in AtMAD will provide new insights into exploring biological mechanisms of plants at multi-omics levels.
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Affiliation(s)
- Yiheng Lan
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Ruikun Sun
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jian Ouyang
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Wubing Ding
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Min-Jun Kim
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China
| | - Jun Wu
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yuhua Li
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.,Big Data and Engineering Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
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39
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Dale R, Oswald S, Jalihal A, LaPorte MF, Fletcher DM, Hubbard A, Shiu SH, Nelson ADL, Bucksch A. Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling. FRONTIERS IN PLANT SCIENCE 2021; 12:687652. [PMID: 34354723 PMCID: PMC8329482 DOI: 10.3389/fpls.2021.687652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/01/2021] [Indexed: 05/10/2023]
Abstract
The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.
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Affiliation(s)
- Renee Dale
- Donald Danforth Plant Science Center, St. Louis, MO, United States
- *Correspondence: Renee Dale
| | - Scott Oswald
- Warnell School of Forestry and Natural Resources and Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Amogh Jalihal
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Mary-Francis LaPorte
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
| | - Daniel M. Fletcher
- Bioengineering Sciences Research Group, Department of Mechanical Engineering, School of Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, United Kingdom
| | - Allen Hubbard
- Donald Danforth Plant Science Center, St. Louis, MO, United States
| | - Shin-Han Shiu
- Department of Plant Biology and Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, United States
| | | | - Alexander Bucksch
- Warnell School of Forestry and Natural Resources and Institute of Bioinformatics, University of Georgia, Athens, GA, United States
- Department of Plant Biology, University of Georgia, Athens, GA, United States
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
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40
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Cruz DF, De Meyer S, Ampe J, Sprenger H, Herman D, Van Hautegem T, De Block J, Inzé D, Nelissen H, Maere S. Using single-plant-omics in the field to link maize genes to functions and phenotypes. Mol Syst Biol 2020; 16:e9667. [PMID: 33346944 PMCID: PMC7751767 DOI: 10.15252/msb.20209667] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 10/29/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene-phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory-generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field-generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab-field gap.
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Affiliation(s)
- Daniel Felipe Cruz
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Sam De Meyer
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Joke Ampe
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Heike Sprenger
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Dorota Herman
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Tom Van Hautegem
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Jolien De Block
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Dirk Inzé
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Hilde Nelissen
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Steven Maere
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
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41
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Singh N, Rai V, Singh NK. Multi-omics strategies and prospects to enhance seed quality and nutritional traits in pigeonpea. THE NUCLEUS 2020. [DOI: 10.1007/s13237-020-00341-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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42
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Liang Z, Qiu Y, Schnable JC. Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature. MOLECULAR PLANT 2020; 13:907-922. [PMID: 32171733 DOI: 10.1016/j.molp.2020.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/20/2020] [Accepted: 03/08/2020] [Indexed: 06/10/2023]
Abstract
Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.
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Affiliation(s)
- Zhikai Liang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA; Plant Science Innovation Center, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yumou Qiu
- Department of Statistics, Iowa State University, Ames, IA, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA; Plant Science Innovation Center, University of Nebraska-Lincoln, Lincoln, NE, USA.
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