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Yanarella CF, Fattel L, Lawrence-Dill CJ. Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies. G3 (BETHESDA, MD.) 2024:jkae161. [PMID: 39099140 DOI: 10.1093/g3journal/jkae161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 06/23/2024] [Indexed: 08/06/2024]
Abstract
We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.
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Affiliation(s)
- Colleen F Yanarella
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
| | - Leila Fattel
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics and Genomics Program, Iowa State University, Ames, IA 50011, USA
| | - Carolyn J Lawrence-Dill
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics and Genomics Program, Iowa State University, Ames, IA 50011, USA
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA 50011, USA
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2
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Khound R, Rajput SG, Schnable JC, Vetriventhan M, Santra DK. Genome-wide association study reveals marker-trait associations for major agronomic traits in proso millet (Panicum miliaceum L.). PLANTA 2024; 260:44. [PMID: 38963439 DOI: 10.1007/s00425-024-04465-4] [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: 03/16/2024] [Accepted: 06/12/2024] [Indexed: 07/05/2024]
Abstract
MAIN CONCLUSION The pilot-scale genome-wide association study in the US proso millet identified twenty marker-trait associations for five morpho-agronomic traits identifying genomic regions for future studies (e.g. molecular breeding and map-based cloning). Proso millet (Panicum miliaceum L.) is an ancient grain recognized for its excellent water-use efficiency and short growing season. It is an indispensable part of the winter wheat-based dryland cropping system in the High Plains of the USA. Its grains are endowed with high nutritional and health-promoting properties, making it increasingly popular in the global market for healthy grains. There is a dearth of genomic resources in proso millet for developing molecular tools to complement conventional breeding for developing high-yielding varieties. Genome-wide association study (GWAS) is a widely used method to dissect the genetics of complex traits. In this pilot study of the first-ever GWAS in the US proso millet, 71 globally diverse genotypes of 109 the US proso millet core collection were evaluated for five major morpho-agronomic traits at two locations in western Nebraska, and GWAS was conducted to identify single nucleotide polymorphisms (SNPs) associated with these traits. Analysis of variance showed that there was a significant difference among the genotypes, and all five traits were also found to be highly correlated with each other. Sequence reads from genotyping-by-sequencing (GBS) were used to identify 11,147 high-quality bi-allelic SNPs. Population structure analysis with those SNPs showed stratification within the core collection. The GWAS identified twenty marker-trait associations (MTAs) for the five traits. Twenty-nine putative candidate genes associated with the five traits were also identified. These genomic regions can be used to develop genetic markers for marker-assisted selection in proso millet breeding.
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Affiliation(s)
- Rituraj Khound
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Santosh G Rajput
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Dryland Genetics Inc, Ames, IA, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Mani Vetriventhan
- Genebank, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, Telangana, India
| | - Dipak K Santra
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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3
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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; 119:844-860. [PMID: 38812347 DOI: 10.1111/tpj.16801] [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: 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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Guo S, Ai J, Zheng N, Hu H, Xu Z, Chen Q, Li L, Liu Y, Zhang H, Li J, Pan Q, Chen F, Yuan L, Fu J, Gu R, Wang J, Du X. A genome-wide association study uncovers a ZmRap2.7-ZCN9/ZCN10 module to regulate ABA signalling and seed vigour in maize. PLANT BIOTECHNOLOGY JOURNAL 2024. [PMID: 38761386 DOI: 10.1111/pbi.14362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/20/2024] [Accepted: 03/24/2024] [Indexed: 05/20/2024]
Abstract
Seed vigour, including rapid, uniform germination and robust seedling establishment under various field conditions, is becoming an increasingly essential agronomic trait for achieving high yield in crops. However, little is known about this important seed quality trait. In this study, we performed a genome-wide association study to identify a key transcription factor ZmRap2.7, which regulates seed vigour through transcriptionally repressing expressions of three ABA signalling genes ZmPYL3, ZmPP2C and ZmABI5 and two phosphatidylethanolamine-binding genes ZCN9 and ZCN10. In addition, ZCN9 and ZCN10 proteins could interact with ZmPYL3, ZmPP2C and ZmABI5 proteins, and loss-of-function of ZmRap2.7 and overexpression of ZCN9 and ZCN10 reduced ABA sensitivity and seed vigour, suggesting a complex regulatory network for regulation of ABA signalling mediated seed vigour. Finally, we showed that four SNPs in ZmRap2.7 coding region influenced its transcriptionally binding activity to the downstream gene promoters. Together with previously identified functional variants within and surrounding ZmRap2.7, we concluded that the distinct allelic variations of ZmRap2.7 were obtained independently during maize domestication and improvement, and responded separately for the diversities of seed vigour, flowering time and brace root development. These results provide novel genes, a new regulatory network and an evolutional mechanism for understanding the molecular mechanism of seed vigour.
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Affiliation(s)
- Shasha Guo
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Junmin Ai
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Nannan Zheng
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Hairui Hu
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Zhuoyi Xu
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Quanquan Chen
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Li Li
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Yunjun Liu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jieping Li
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Qingchun Pan
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Fanjun Chen
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Lixing Yuan
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China
| | - Junjie Fu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Riliang Gu
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Joint Research Institute of China Agricultural University in Aksu, Aksu, China
| | - Jianhua Wang
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Xuemei Du
- State Key Laboratory of Maize Bio-breeding, Beijing Innovation Center for Crop Seed Technology (MOA), College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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5
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Poretsky E, Cagirici HB, Andorf CM, Sen TZ. Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits. G3 (BETHESDA, MD.) 2024; 14:jkae059. [PMID: 38492232 PMCID: PMC11075552 DOI: 10.1093/g3journal/jkae059] [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: 10/20/2023] [Revised: 10/20/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024]
Abstract
The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.
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Affiliation(s)
- Elly Poretsky
- Crop Improvement and Genetics Research Unit, U.S. Department of Agriculture, Agricultural Research Service, 800 Buchanan St., Albany, CA 94710, USA
| | - Halise Busra Cagirici
- Crop Improvement and Genetics Research Unit, U.S. Department of Agriculture, Agricultural Research Service, 800 Buchanan St., Albany, CA 94710, USA
| | - Carson M Andorf
- Corn Insects and Crop Genetics Research, U.S. Department of Agriculture, Agricultural Research Service, Ames, IA 50011, USA
- Department of Computer Science, Iowa State University, Ames, IA 50011, USA
| | - Taner Z Sen
- Crop Improvement and Genetics Research Unit, U.S. Department of Agriculture, Agricultural Research Service, 800 Buchanan St., Albany, CA 94710, USA
- Department of Bioengineering, University of California, 306 Stanley Hall, Berkeley, CA 94720, USA
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6
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Guo S, Lv L, Zhao Y, Wang J, Lu X, Zhang M, Wang R, Zhang Y, Guo X. Using High-Throughput Phenotyping Analysis to Decipher the Phenotypic Components and Genetic Architecture of Maize Seedling Salt Tolerance. Genes (Basel) 2023; 14:1771. [PMID: 37761911 PMCID: PMC10530905 DOI: 10.3390/genes14091771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Soil salinization is a worldwide problem that limits agricultural production. It is important to understand the salt stress tolerance ability of maize seedlings and explore the underlying related genetic resources. In this study, we used a high-throughput phenotyping platform with a 3D laser sensor (Planteye F500) to identify the digital biomass, plant height and normalized vegetation index under normal and saline conditions at multiple time points. The result revealed that a three-leaf period (T3) was identified as the key period for the phenotypic variation in maize seedlings under salt stress. Moreover, we mapped the salt-stress-related SNPs and identified candidate genes in the natural population via a genome-wide association study. A total of 44 candidate genes were annotated, including 26 candidate genes under normal conditions and 18 candidate genes under salt-stressed conditions. This study demonstrates the feasibility of using a high-throughput phenotyping platform to accurately, continuously quantify morphological traits of maize seedlings in different growing environments. And the phenotype and genetic information of this study provided a theoretical basis for the breeding of salt-resistant maize varieties and the study of salt-resistant genes.
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Affiliation(s)
- Shangjing Guo
- College of Agronomy, Liaocheng University, Liaocheng 252059, China
| | - Lujia Lv
- College of Agronomy, Liaocheng University, Liaocheng 252059, China
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA (DeoxyriboNucleic Acid) Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Minggang Zhang
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ronghuan Wang
- Beijing Key Laboratory of Maize DNA (DeoxyriboNucleic Acid) Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ying Zhang
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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7
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Ahn E, Botkin J, Ellur V, Lee Y, Poudel K, Prom LK, Magill C. Genome-Wide Association Study of Seed Morphology Traits in Senegalese Sorghum Cultivars. PLANTS (BASEL, SWITZERLAND) 2023; 12:2344. [PMID: 37375969 DOI: 10.3390/plants12122344] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023]
Abstract
Sorghum is considered the fifth most important crop in the world. Despite the potential value of Senegalese germplasm for various traits, such as resistance to fungal diseases, there is limited information on the study of sorghum seed morphology. In this study, 162 Senegalese germplasms were evaluated for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness and brightness by scanning and analyzing morphology-related traits with SmartGrain software at the USDA-ARS Plant Science Research Unit. Correlations between seed morphology-related traits and traits associated with anthracnose and head smut resistance were analyzed. Lastly, genome-wide association studies were performed on phenotypic data collected from over 16,000 seeds and 193,727 publicly available single nucleotide polymorphisms (SNPs). Several significant SNPs were found and mapped to the reference sorghum genome to uncover multiple candidate genes potentially associated with seed morphology. The results indicate clear correlations among seed morphology-related traits and potential associations between seed morphology and the defense response of sorghum. GWAS analysis listed candidate genes associated with seed morphologies that can be used for sorghum breeding in the future.
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Affiliation(s)
- Ezekiel Ahn
- USDA-ARS Plant Science Research Unit, St. Paul, MN 55108, USA
| | - Jacob Botkin
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Vishnutej Ellur
- Molecular Plant Sciences, Washington State University, Pullman, WA 99164, USA
| | - Yoonjung Lee
- Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Kabita Poudel
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Louis K Prom
- USDA-ARS Southern Plains Agricultural Research Center, College Station, TX 77845, USA
| | - Clint Magill
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA
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8
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Kirschner GK. Embracing diversity: a genetic marker dataset with increased marker density facilitates association studies in maize. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:1107-1108. [PMID: 36920972 DOI: 10.1111/tpj.16163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
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9
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Khaipho-Burch M, Ferebee T, Giri A, Ramstein G, Monier B, Yi E, Romay MC, Buckler ES. Elucidating the patterns of pleiotropy and its biological relevance in maize. PLoS Genet 2023; 19:e1010664. [PMID: 36943844 PMCID: PMC10030035 DOI: 10.1371/journal.pgen.1010664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/09/2023] [Indexed: 03/23/2023] Open
Abstract
Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.
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Affiliation(s)
| | - Taylor Ferebee
- Department of Computational Biology, Cornell University, Ithaca, New York
| | - Anju Giri
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Emily Yi
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York
- Institute for Genomic Diversity, Cornell University, Ithaca, New York
- USDA-ARS, Ithaca, New York, United States of America
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10
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Grzybowski MW, Mural RV, Xu G, Turkus J, Yang J, Schnable JC. A common resequencing-based genetic marker data set for global maize diversity. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:1109-1121. [PMID: 36705476 DOI: 10.1111/tpj.16123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.
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Affiliation(s)
- Marcin W Grzybowski
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Gen Xu
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jonathan Turkus
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jinliang Yang
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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11
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Mural RV, Sun G, Grzybowski M, Tross MC, Jin H, Smith C, Newton L, Andorf CM, Woodhouse MR, Thompson AM, Sigmon B, Schnable JC. Association mapping across a multitude of traits collected in diverse environments in maize. Gigascience 2022; 11:6673780. [PMID: 35997208 PMCID: PMC9396454 DOI: 10.1093/gigascience/giac080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/25/2022] [Indexed: 11/14/2022] Open
Abstract
Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.
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Affiliation(s)
- Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Guangchao Sun
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Marcin Grzybowski
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Michael C Tross
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Hongyu Jin
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Christine Smith
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Linsey Newton
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50010, USA.,Department of Computer Science, Iowa State University, Ames, IA 50011, USA
| | | | - Addie M Thompson
- Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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