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Perry A, Eddelbuettel D, Rosenthal G, Blackmon H. Polly: An R package for genotyping microsatellites and detecting highly polymorphic DNA markers from short-read data. Mol Ecol Resour 2024; 24:e13933. [PMID: 38299378 PMCID: PMC10994724 DOI: 10.1111/1755-0998.13933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 02/02/2024]
Abstract
Highly polymorphic markers, such as microsatellites, are invaluable for the study of natural populations. However, contemporary methods for genotyping highly polymorphic variants have serious drawbacks that impede their efficiency. We created Polly, an R package with C++ source code that uses Illumina short-read data to genotype microsatellites, detect highly polymorphic variants and identify clusters of highly polymorphic SNPs, indels and microsatellites. We tested Polly on short-read data from Xiphophorus birchmanni (Teleostei: Poeciliidae) and Arabidopsis thaliana, finding it to be efficient and accurate both for microsatellite genotyping and polymorphic marker detection. This program can be applied to any diploid population for which there exists short-read data and at least one scaffolded reference genome.
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Affiliation(s)
- Annabel Perry
- Harvard University, Department of Human Evolutionary Biology
- Texas A&M University, Department of Biology
| | | | - Gil Rosenthal
- Texas A&M University, Department of Biology
- Università degli Studi di Padova, Dipartimento di Biologia
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2
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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Zou C, Sapkota S, Figueroa-Balderas R, Glaubitz J, Cantu D, Kingham BF, Sun Q, Cadle-Davidson L. A multitiered haplotype strategy to enhance phased assembly and fine mapping of a disease resistance locus. PLANT PHYSIOLOGY 2023; 193:2321-2336. [PMID: 37706526 DOI: 10.1093/plphys/kiad494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/10/2023] [Accepted: 08/17/2023] [Indexed: 09/15/2023]
Abstract
Fine mapping of quantitative trait loci (QTL) to dissect the genetic basis of traits of interest is essential to modern breeding practice. Here, we employed a multitiered haplotypic marker system to increase fine mapping accuracy by constructing a chromosome-level, haplotype-resolved parental genome, accurate detection of recombination sites, and allele-specific characterization of the transcriptome. In the first tier of this system, we applied the preexisting panel of 2,000 rhAmpSeq core genome markers that is transferable across the entire Vitis genus and provides a genomic resolution of 200 kb to 1 Mb. The second tier consisted of high-density haplotypic markers generated from Illumina skim sequencing data for samples enriched for relevant recombinations, increasing the potential resolution to hundreds of base pairs. We used this approach to dissect a novel Resistance to Plasmopara viticola-33 (RPV33) locus conferring resistance to grapevine downy mildew, narrowing the candidate region to only 0.46 Mb. In the third tier, we used allele-specific RNA-seq analysis to identify a cluster of 3 putative disease resistance RPP13-like protein 2 genes located tandemly in a nonsyntenic insertion as candidates for the disease resistance trait. In addition, combining the rhAmpSeq core genome haplotype markers and skim sequencing-derived high-density haplotype markers enabled chromosomal-level scaffolding and phasing of the grape Vitis × doaniana 'PI 588149' assembly, initially built solely from Pacific Biosciences (PacBio) high-fidelity (HiFi) reads, leading to the correction of 16 large-scale phasing errors. Our mapping strategy integrates high-density, phased genetic information with individual reference genomes to pinpoint the genetic basis of QTLs and will likely be widely adopted in highly heterozygous species.
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Affiliation(s)
- Cheng Zou
- BRC Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853, USA
| | - Surya Sapkota
- School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY 14456, USA
- Grape Genetics Research Unit, USDA-ARS, Geneva, NY 14456, USA
| | - Rosa Figueroa-Balderas
- Department of Viticulture and Enology, University of California Davis, Davis, CA 95616, USA
| | - Jeff Glaubitz
- BRC Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853, USA
| | - Dario Cantu
- Department of Viticulture and Enology, University of California Davis, Davis, CA 95616, USA
| | - Brewster F Kingham
- DNA Sequencing & Genotyping Center, Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA
| | - Qi Sun
- BRC Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, 14853, USA
| | - Lance Cadle-Davidson
- School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY 14456, USA
- Grape Genetics Research Unit, USDA-ARS, Geneva, NY 14456, USA
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Bencurova E, Akash A, Dobson RC, Dandekar T. DNA storage-from natural biology to synthetic biology. Comput Struct Biotechnol J 2023; 21:1227-1235. [PMID: 36817961 PMCID: PMC9932295 DOI: 10.1016/j.csbj.2023.01.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
Natural DNA storage allows cellular differentiation, evolution, the growth of our children and controls all our ecosystems. Here, we discuss the fundamental aspects of DNA storage and recent advances in this field, with special emphasis on natural processes and solutions that can be exploited. We point out new ways of efficient DNA and nucleotide storage that are inspired by nature. Within a few years DNA-based information storage may become an attractive and natural complementation to current electronic data storage systems. We discuss rapid and directed access (e.g. DNA elements such as promotors, enhancers), regulatory signals and modulation (e.g. lncRNA) as well as integrated high-density storage and processing modules (e.g. chromosomal territories). There is pragmatic DNA storage for use in biotechnology and human genetics. We examine DNA storage as an approach for synthetic biology (e.g. light-controlled nucleotide processing enzymes). The natural polymers of DNA and RNA offer much for direct storage operations (read-in, read-out, access control). The inbuilt parallelism (many molecules at many places working at the same time) is important for fast processing of information. Using biology concepts from chromosomal storage, nucleic acid processing as well as polymer material sciences such as electronical effects in enzymes, graphene, nanocellulose up to DNA macramé , DNA wires and DNA-based aptamer field effect transistors will open up new applications gradually replacing classical information storage methods in ever more areas over time (decades).
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Affiliation(s)
- Elena Bencurova
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Aman Akash
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Renwick C.J. Dobson
- Biomolecular Interaction Centre, University of Canterbury, Christchurch, New Zealand,Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Australia
| | - Thomas Dandekar
- Department of Bioinformatics, University of Würzburg, Würzburg, Germany,Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany,Corresponding author at: Department of Bioinformatics, University of Würzburg, Würzburg, Germany.
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Jin SK, Xu LN, Yang QQ, Zhang MQ, Wang SL, Wang RA, Tao T, Hong LM, Guo QQ, Jia SW, Song T, Leng YJ, Cai XL, Gao JP. High-resolution quantitative trait locus mapping for rice grain quality traits using genotyping by sequencing. FRONTIERS IN PLANT SCIENCE 2023; 13:1050882. [PMID: 36714703 PMCID: PMC9878556 DOI: 10.3389/fpls.2022.1050882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
Rice is a major food crop that sustains approximately half of the world population. Recent worldwide improvements in the standard of living have increased the demand for high-quality rice. Accurate identification of quantitative trait loci (QTLs) for rice grain quality traits will facilitate rice quality breeding and improvement. In the present study, we performed high-resolution QTL mapping for rice grain quality traits using a genotyping-by-sequencing approach. An F2 population derived from a cross between an elite japonica variety, Koshihikari, and an indica variety, Nona Bokra, was used to construct a high-density genetic map. A total of 3,830 single nucleotide polymorphism markers were mapped to 12 linkage groups spanning a total length of 2,456.4 cM, with an average genetic distance of 0.82 cM. Seven grain quality traits-the percentage of whole grain, percentage of head rice, percentage of area of head rice, transparency, percentage of chalky rice, percentage of chalkiness area, and degree of chalkiness-of the F2 population were investigated. In total, 15 QTLs with logarithm of the odds (LOD) scores >4 were identified, which mapped to chromosomes 6, 7, and 9. These loci include four QTLs for transparency, four for percentage of chalky rice, four for percentage of chalkiness area, and three for degree of chalkiness, accounting for 0.01%-61.64% of the total phenotypic variation. Of these QTLs, only one overlapped with previously reported QTLs, and the others were novel. By comparing the major QTL regions in the rice genome, several key candidate genes reported to play crucial roles in grain quality traits were identified. These findings will expedite the fine mapping of these QTLs and QTL pyramiding, which will facilitate the genetic improvement of rice grain quality.
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Affiliation(s)
- Su-Kui Jin
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Li-Na Xu
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Qing-Qing Yang
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Ming-Qiu Zhang
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Shui-Lian Wang
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Ruo-An Wang
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Tao Tao
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Lian-Min Hong
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Qian-Qian Guo
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Shu-Wen Jia
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Tao Song
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yu-Jia Leng
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Xiu-Ling Cai
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
| | - Ji-Ping Gao
- JiangsuKey Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, China
- National Key Laboratory of Plant Molecular Genetics, Chinese Academy of Sciences (CAS) Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China
- Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, China
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Maldonado-Taipe N, Barbier F, Schmid K, Jung C, Emrani N. High-Density Mapping of Quantitative Trait Loci Controlling Agronomically Important Traits in Quinoa ( Chenopodium quinoa Willd.). FRONTIERS IN PLANT SCIENCE 2022; 13:916067. [PMID: 35812962 PMCID: PMC9261497 DOI: 10.3389/fpls.2022.916067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Quinoa is a pseudocereal originating from the Andean regions. Despite quinoa's long cultivation history, genetic analysis of this crop is still in its infancy. We aimed to localize quantitative trait loci (QTL) contributing to the phenotypic variation of agronomically important traits. We crossed the Chilean accession PI-614889 and the Peruvian accession CHEN-109, which depicted significant differences in days to flowering, days to maturity, plant height, panicle length, and thousand kernel weight (TKW), saponin content, and mildew susceptibility. We observed sizeable phenotypic variation across F2 plants and F3 families grown in the greenhouse and the field, respectively. We used Skim-seq to genotype the F2 population and constructed a high-density genetic map with 133,923 single nucleotide polymorphism (SNPs). Fifteen QTL were found for ten traits. Two significant QTL, common in F2 and F3 generations, depicted pleiotropy for days to flowering, plant height, and TKW. The pleiotropic QTL harbored several putative candidate genes involved in photoperiod response and flowering time regulation. This study presents the first high-density genetic map of quinoa that incorporates QTL for several important agronomical traits. The pleiotropic loci can facilitate marker-assisted selection in quinoa breeding programs.
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Affiliation(s)
| | - Federico Barbier
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Karl Schmid
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Christian Jung
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Nazgol Emrani
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, Kiel, Germany
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Sheoran S, Gupta M, Kumari S, Kumar S, Rakshit S. Meta-QTL analysis and candidate genes identification for various abiotic stresses in maize ( Zea mays L.) and their implications in breeding programs. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:26. [PMID: 37309532 PMCID: PMC10248626 DOI: 10.1007/s11032-022-01294-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 03/26/2022] [Indexed: 06/14/2023]
Abstract
Global climate change leads to the concurrence of a number of abiotic stresses including moisture stress (drought, waterlogging), temperature stress (heat, cold), and salinity stress, which are the major factors affecting maize production. To develop abiotic stress tolerance in maize, many quantitative trait loci (QTL) have been identified, but very few of them have been utilized successfully in breeding programs. In this context, the meta-QTL analysis of the reported QTL will enable the identification of stable/real QTL which will pave a reliable way to introgress these QTL into elite cultivars through marker-assisted selection. In this study, a total of 542 QTL were summarized from 33 published studies for tolerance to different abiotic stresses in maize to conduct meta-QTL analysis using BiomercatorV4.2.3. Among those, only 244 major QTL with more than 10% phenotypic variance were preferably utilised to carry out meta-QTL analysis. In total, 32 meta-QTL possessing 1907 candidate genes were detected for different abiotic stresses over diverse genetic and environmental backgrounds. The MQTL2.1, 5.1, 5.2, 5.6, 7.1, 9.1, and 9.2 control different stress-related traits for combined abiotic stress tolerance. The candidate genes for important transcription factor families such as ERF, MYB, bZIP, bHLH, NAC, LRR, ZF, MAPK, HSP, peroxidase, and WRKY have been detected for different stress tolerances. The identified meta-QTL are valuable for future climate-resilient maize breeding programs and functional validation of candidate genes studies, which will help to deepen our understanding of the complexity of these abiotic stresses. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01294-9.
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Affiliation(s)
- Seema Sheoran
- ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, 141004 India
- Present Address: ICAR-Indian Agricultural Research Institute, Regional Station, Karnal, 132001 India
| | - Mamta Gupta
- ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, 141004 India
| | - Shweta Kumari
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012 India
| | - Sandeep Kumar
- Present Address: ICAR-Indian Agricultural Research Institute, Regional Station, Karnal, 132001 India
- ICAR-Indian Institute of Pulses Research, Regional Station, Phanda, Bhopal, 462030 India
| | - Sujay Rakshit
- ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, 141004 India
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Torre S, Sebastiani F, Burbui G, Pecori F, Pepori AL, Passeri I, Ghelardini L, Selvaggi A, Santini A. Novel Insights Into Refugia at the Southern Margin of the Distribution Range of the Endangered Species Ulmus laevis. FRONTIERS IN PLANT SCIENCE 2022; 13:826158. [PMID: 35242155 PMCID: PMC8886209 DOI: 10.3389/fpls.2022.826158] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/14/2022] [Indexed: 05/27/2023]
Abstract
Riparian ecosystems, in long-time developed regions, are among the most heavily impacted by human activities; therefore, the distribution of tree riparian species, such as Ulmus laevis, is highly affected. This phenomenon is particularly relevant at the margins of the natural habitat of the species, where populations are small and rare. In these cases, it is difficult to distinguish between relics or introductions, but it is relevant for the restoration of natural habitats and conservation strategies. The aim of this study was to study the phylogeography of the southern distribution of the species. We sequenced the entire chloroplast (cp) genomes of 54 individuals from five sampled populations across different European regions to highlight polymorphisms and analyze their distribution. Thirty-two haplotypes were identified. All the sampled populations showed private haplotypes that can be considered an indicator of long-term residency, given the low mutation rate of organellar DNA. The network of all haplotypes showed a star-like topology, and Serbian haplotypes were present in all branches. The Balkan population showed the highest level of nucleotide and genetic diversity. Low genetic differentiation between populations was observed but we found a significant differentiation among Serbia vs. other provenances. Our estimates of divergent time of U. laevis samples highlight the early split of above all Serbian individuals from other populations, emphasizing the reservoir role of white elm genetic diversity of Serbian population.
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Affiliation(s)
- Sara Torre
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
| | - Federico Sebastiani
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
| | - Guia Burbui
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
| | - Francesco Pecori
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
| | - Alessia L. Pepori
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
| | - Iacopo Passeri
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
| | - Luisa Ghelardini
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari Ambientali e Forestali (DAGRI), Università di Firenze, Florence, Italy
| | - Alberto Selvaggi
- Istituto per le Piante da Legno e l’Ambiente - I.P.L.A. S.p.A., Turin, Italy
| | - Alberto Santini
- Istituto per la Protezione Sostenibile delle Piante, IPSP-CNR, Florence, Italy
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